diff --git a/.github/workflows/mac.yml b/.github/workflows/mac.yml index a2221380d13..292c73c6545 100644 --- a/.github/workflows/mac.yml +++ b/.github/workflows/mac.yml @@ -1,9 +1,10 @@ name: Mac OS X Full Clean Build with ML env: - PIP_DEFAULT_TIMEOUT: 100 - GTFF_USE_PROMPT_TOOLKIT: false - GTFF_USE_ION: false GTFF_ENABLE_QUICK_EXIT: true + GTFF_LOG_COLLECTION: false + GTFF_USE_ION: false + GTFF_USE_PROMPT_TOOLKIT: false + PIP_DEFAULT_TIMEOUT: 100 PYTHONNOUSERSITE: 1 on: push: diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 81ebf1369a8..0585738b449 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,9 +1,10 @@ name: Tests env: - PIP_DEFAULT_TIMEOUT: 100 - GTFF_USE_PROMPT_TOOLKIT: false - GTFF_USE_ION: false GTFF_ENABLE_QUICK_EXIT: true + GTFF_LOG_COLLECTION: false + GTFF_USE_ION: false + GTFF_USE_PROMPT_TOOLKIT: false + PIP_DEFAULT_TIMEOUT: 100 on: [pull_request, push] jobs: linting: diff --git a/.github/workflows/windows.yml b/.github/workflows/windows.yml index 3cf8f68f1b6..4ce2375c19f 100644 --- a/.github/workflows/windows.yml +++ b/.github/workflows/windows.yml @@ -1,9 +1,10 @@ name: Windows 10 Full Clean Build with ML env: - PIP_DEFAULT_TIMEOUT: 100 - GTFF_USE_PROMPT_TOOLKIT: false - GTFF_USE_ION: false GTFF_ENABLE_QUICK_EXIT: true + GTFF_LOG_COLLECTION: false + GTFF_USE_ION: false + GTFF_USE_PROMPT_TOOLKIT: false + PIP_DEFAULT_TIMEOUT: 100 PYTHONNOUSERSITE: 1 on: push: diff --git a/.gitignore b/.gitignore index 0decb79d360..ac66e938e07 100644 --- a/.gitignore +++ b/.gitignore @@ -19,6 +19,9 @@ gamestonk_terminal/config_plot.py gamestonk_terminal/config_terminal.py gamestonk_terminal/feature_flags.py gamestonk_terminal/jupyter/reports/stored/* +gamestonk_terminal/jupyter/dashboards/.ipynb_checkpoints +gamestonk_terminal/jupyter/reports/.ipynb_checkpoints +gamestonk_terminal/jupyter/widgets/.ipynb_checkpoints !gamestonk_terminal/jupyter/reports/stored/.gitkeep !gamestonk_terminal/jupyter/dashboards/*.ipynb !gamestonk_terminal/jupyter/reports/*.ipynb diff --git a/bots/README.md b/bots/README.md old mode 100644 new mode 100755 index 2024efa59b6..7a1129d79e4 --- a/bots/README.md +++ b/bots/README.md @@ -19,7 +19,7 @@ This tutorial assumes that you have successfully installed Gamestonk Terminal. Next, from the GamestonkTerminal root folder run the following command: ```text -poetry install -E discord +poetry install -E bots ``` ## Registering Applications @@ -104,7 +104,7 @@ uvicorn run_discordbot:app Now, you should be ready to invoke the discord bot from server using something like: ```text -/cc +/candle ``` Screenshot 2021-10-18 at 00 21 30 diff --git a/bots/__init__.py b/bots/__init__.py old mode 100644 new mode 100755 index 7e0e5b9649c..b4e0c8847df --- a/bots/__init__.py +++ b/bots/__init__.py @@ -1,2 +1,13 @@ # flake8: noqa -from . import common, economy, etf, groupme, menus, slack, stocks, telegram +from . import ( + common, + config_discordbot, + economy, + etf, + groupme, + imps, + menus, + slack, + stocks, + telegram, +) diff --git a/bots/cmds/cmds.py b/bots/cmds/cmds.py index 873474289ff..227c8ddeba8 100644 --- a/bots/cmds/cmds.py +++ b/bots/cmds/cmds.py @@ -2,7 +2,7 @@ import disnake from disnake.ext import commands -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from bots.menus.menu import Menu diff --git a/bots/cmds/dd_cmds.py b/bots/cmds/dd_cmds.py old mode 100644 new mode 100755 index fd8eaba3982..8ceb53dbd0a --- a/bots/cmds/dd_cmds.py +++ b/bots/cmds/dd_cmds.py @@ -6,6 +6,7 @@ from disnake.ext import commands from bots.helpers import ShowView, ticker_autocomp from bots.stocks.due_diligence.analyst import analyst_command from bots.stocks.due_diligence.arktrades import arktrades_command +from bots.stocks.due_diligence.borrowed import borrowed_command from bots.stocks.due_diligence.customer import customer_command from bots.stocks.due_diligence.est import est_command from bots.stocks.due_diligence.pt import pt_command @@ -36,6 +37,19 @@ class DueDiligenceCommands(commands.Cog): """ await ShowView().discord(analyst_command, inter, "dd analyst", ticker) + @dd.sub_command() + async def borrowed( + inter: disnake.AppCmdInter, + ticker: str = commands.Param(autocomplete=ticker_autocomp), + ): + """Displays borrowed shares available and fee [Stocksera] + + Parameters + ----------- + ticker: Stock Ticker + """ + await ShowView().discord(borrowed_command, inter, "dd borrowed", ticker) + @dd.sub_command() async def pt( inter: disnake.AppCmdInter, diff --git a/bots/cmds/economy_cmds.py b/bots/cmds/economy_cmds.py old mode 100644 new mode 100755 index 28feddc569b..1bfef52f4b8 --- a/bots/cmds/economy_cmds.py +++ b/bots/cmds/economy_cmds.py @@ -17,6 +17,7 @@ from bots.economy.meats import meats_command from bots.economy.metals import metals_command from bots.economy.overview import overview_command from bots.economy.performance import performance_command +from bots.economy.reverse_repo import reverse_repo_command from bots.economy.softs import softs_command from bots.economy.usbonds import usbonds_command from bots.economy.valuation import valuation_command @@ -132,6 +133,20 @@ class EconomyCommands(commands.Cog): valuation_command, inter, "econ valuation", economy_group ) + @econ.sub_command() + async def repo( + self, + inter: disnake.AppCmdInter, + days: int = 100, + ): + """Displays Reverse Repo [Stocksera] + + Parameters + ----------- + days: Number of days to display. Default: 100 + """ + await ShowView().discord(reverse_repo_command, inter, "econ repo", days) + @econ.sub_command() async def performance( self, diff --git a/bots/cmds/etf_cmds.py b/bots/cmds/etf_cmds.py old mode 100644 new mode 100755 index c2cc61e83da..e7fa4abc9af --- a/bots/cmds/etf_cmds.py +++ b/bots/cmds/etf_cmds.py @@ -6,7 +6,7 @@ from disnake.ext import commands from bots.etf.holdings import holdings_command from bots.etf.tops import etfs_disc_command from bots.etf.whatetf import by_ticker_command -from bots.helpers import ShowView +from bots.helpers import ShowView, ticker_autocomp class EtfCommands(commands.Cog): @@ -66,18 +66,30 @@ class EtfCommands(commands.Cog): @holdings.sub_command(name="by-ticker") async def by_ticker( inter: disnake.AppCmdInter, - ticker: str, + ticker: str = commands.Param(autocomplete=ticker_autocomp), num: int = 15, + sort: str = commands.Param( + choices={ + "Percentage of Fund": "fund_percent", + "Market Value": "mkt_value", + }, + ), ): """Displays ETF Holdings By Ticker [ETF DataBase] Parameters ----------- ticker: Ticker to Search + sort: Sort by Percentage of Fund or Market Value num: Amount of ETFs to display Default 15 """ await ShowView().discord( - by_ticker_command, inter, "etf holdings by-ticker", ticker, num + by_ticker_command, + inter, + "etf holdings by-ticker", + ticker, + sort, + num, ) diff --git a/bots/cmds/ta_cmds.py b/bots/cmds/ta_cmds.py old mode 100644 new mode 100755 index 825701bc16a..c335dcd68b3 --- a/bots/cmds/ta_cmds.py +++ b/bots/cmds/ta_cmds.py @@ -60,10 +60,10 @@ class TechnicalAnalysisCommands(commands.Cog): interval: int = commands.Param(choices=[1, 5, 15, 30, 60, 1440]), past_days: int = 0, ma_mode: str = commands.Param(choices=["ema", "sma", "wma", "hma", "zlma"]), - window="", + window: str = "", offset: int = 0, - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -107,8 +107,8 @@ class TechnicalAnalysisCommands(commands.Cog): past_days: int = 0, length="14", scalar="0.015", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -151,8 +151,8 @@ class TechnicalAnalysisCommands(commands.Cog): fast="12", slow="26", signal="9", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -197,8 +197,8 @@ class TechnicalAnalysisCommands(commands.Cog): length="14", scalar="100", drift="1", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -243,8 +243,8 @@ class TechnicalAnalysisCommands(commands.Cog): fast_k="14", slow_d="3", slow_k="3", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -287,8 +287,8 @@ class TechnicalAnalysisCommands(commands.Cog): interval: int = commands.Param(choices=[1, 5, 15, 30, 60, 1440]), past_days: int = 0, length="14", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -352,8 +352,8 @@ class TechnicalAnalysisCommands(commands.Cog): length="14", scalar="100", drift="1", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -397,8 +397,8 @@ class TechnicalAnalysisCommands(commands.Cog): past_days: int = 0, length="25", scalar="100", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -441,8 +441,8 @@ class TechnicalAnalysisCommands(commands.Cog): length="20", std: float = 2.0, ma_mode: str = commands.Param(choices=["ema", "sma", "wma", "hma", "zlma"]), - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -484,8 +484,8 @@ class TechnicalAnalysisCommands(commands.Cog): ticker: str = commands.Param(autocomplete=ticker_autocomp), upper_length="25", lower_length="100", - start="", - end="", + start: str = "", + end: str = "", ): """Displays chart with donchian channel [Yahoo Finance] @@ -520,8 +520,8 @@ class TechnicalAnalysisCommands(commands.Cog): scalar="2", ma_mode: str = commands.Param(choices=["ema", "sma", "wma", "hma", "zlma"]), offset="0", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -567,8 +567,8 @@ class TechnicalAnalysisCommands(commands.Cog): interval: int = commands.Param(choices=[1, 5, 15, 30, 60, 1440]), past_days: int = 0, is_open="False", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -609,8 +609,8 @@ class TechnicalAnalysisCommands(commands.Cog): is_open: bool = False, fast="3", slow="10", - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -652,8 +652,8 @@ class TechnicalAnalysisCommands(commands.Cog): ticker: str = commands.Param(autocomplete=ticker_autocomp), interval: int = commands.Param(choices=[1, 5, 15, 30, 60, 1440]), past_days: int = 0, - start="", - end="", + start: str = "", + end: str = "", extended_hours: bool = False, heikin_candles: bool = False, ): @@ -687,18 +687,37 @@ class TechnicalAnalysisCommands(commands.Cog): self, inter: disnake.AppCmdInter, ticker: str = commands.Param(autocomplete=ticker_autocomp), - start="", - end="", + interval: int = commands.Param(choices=[1, 5, 15, 30, 60, 1440]), + past_days: int = 0, + start: str = "", + end: str = "", + extended_hours: bool = False, + heikin_candles: bool = False, ): """Displays chart with fibonacci retracement [Yahoo Finance] Parameters ----------- ticker: Stock Ticker + interval : Chart Minute Interval, 1440 for Daily + past_days: Past Days to Display. Default: 0(Not for Daily) start: YYYY-MM-DD format end: YYYY-MM-DD format + extended_hours: Display Pre/After Market Hours. Default: False + heikin_candles: Heikin Ashi candles. Default: False """ - await ShowView().discord(fib_command, inter, "ta fib", ticker, start, end) + await ShowView().discord( + fib_command, + inter, + "ta fib", + ticker, + interval, + past_days, + start, + end, + extended_hours, + heikin_candles, + ) @ta.sub_command() async def view( diff --git a/bots/common/helpers.py b/bots/common/helpers.py index 62d68d614ed..2de4f3d7adf 100644 --- a/bots/common/helpers.py +++ b/bots/common/helpers.py @@ -89,7 +89,7 @@ def non_slash(text: str, sender: Any, showview: Any): full_cmd = cmd.split("/") group = full_cmd[0].split("_")[0] parents = {x.split("_")[0] for x in available_commands} - if text[0] == "!" or text[0] == "/": + if text and text[0] in ["!", "/"]: if group in parents: if full_cmd[0] in available_commands: selected = commands[full_cmd[0]] diff --git a/bots/common/plot_tbls.py b/bots/common/plot_tbls.py new file mode 100644 index 00000000000..840b6f15e9c --- /dev/null +++ b/bots/common/plot_tbls.py @@ -0,0 +1,166 @@ +from math import floor +from typing import List, Optional, Tuple, Union + +import pandas as pd +import plotly + + +# pylint: disable=R0913 +def plot_df( + df: Union[pd.Series, pd.DataFrame], + print_index: bool = True, + title: Optional[dict] = None, + tbl_header_visible: bool = True, + tbl_header: Optional[dict] = None, + tbl_cells: Optional[dict] = None, + row_fill_color: Optional[Tuple[str, str]] = None, + col_width: Optional[Union[int, float, List[Union[int, float]]]] = None, + fig_size: Optional[Tuple[int, int]] = None, + **layout_kwargs, +) -> plotly.graph_objects.Figure: + """Plots a pd.Series or pd.DataFrame. + + Parameters + ---------- + df : Union[pd.Series, pd.DataFrame] + Series or dataframe to be plotted. + print_index : bool, default True + If `True`, prints the dataframe's index. `df.index.name` will become the index + column header. + title : dict, default None + A dict possibly containing `plotly` key/value pairs: + https://plotly.com/python/reference/layout/#layout-title + + More relevant key/value pairs: + + - font_color : color + - font_family : str + - font_size : number greater than or equal to 1 + - text : str + - x : number between or equal to 0 and 1, default 0.5 + + Sets the x position with respect to `xref` in normalized coordinates + from "0" (left) to "1" (right). + - xanchor : enumerated, one of ("auto", "left", "center", "right"), + default "auto" + + Sets the title's horizontal alignment with respect to its x position. + "left" means that the title starts at x, "right" means that the title ends + at x and "center" means that the title's center is at x. "auto" divides + `xref` by three and calculates the `xanchor` value automatically based on + the value of `x`. + tbl_header_visible : bool, default True, + If `False`, table header will be invisible. Takes precedence over `tbl_header` + argument. + tbl_header, tbl_cells : dict, default None + A dict possibly containing `plotly` key/value pairs: + https://plotly.com/python/reference/table/#table-header + https://plotly.com/python/reference/table/#table-cells + + More relevant key/value pairs: + + - align : enumerated or array of enumerateds, + one of ("left", "center", "right"), default "center" + - fill_color : color, default "white" + - font_color : color or array of colors + - font_family : str or array of str + - font_size : number or array of numbers greater than or equal to 1 + - height : number, default 28 + - line_width : number or array of numbers, default 1 + row_fill_color : Tuple[str, str], default None + Tuple of colors that will be used to alternate row colors. Takes precedence + over `tbl_cells["fill_color"]`. + col_width : number or array of numbers, default None + The width of columns expressed as a ratio. Columns fill the available width + in proportion of their specified column widths. + fig_size : Tuple[int, int], default None + Tuple specifying the `width` and `height` of the figure. + **layout_kwargs + Plotly accepts a large number of layout-related keyword arguments. + A detailed descriptions is available at + https://plotly.com/python-api-reference/generated/plotly.graph_objects.Layout.html. + + Returns + ------- + plotly.graph_objects.Figure + Returns a figure object. + + """ + + def _alternate_row_colors() -> Optional[List[str]]: + color_list = None + # alternate row colors + row_count = len(df) + if row_fill_color is not None: + # determine how many rows in `df` and then create a list with alternating + # row colors + row_odd_count = floor(row_count / 2) + row_count % 2 + row_even_count = floor(row_count / 2) + odd_list = [row_fill_color[0]] * row_odd_count + even_list = [row_fill_color[1]] * row_even_count + color_list = [x for y in zip(odd_list, even_list) for x in y] + if row_odd_count > row_even_count: + color_list.append(row_fill_color[0]) + + return color_list + + def _tbl_values(): + if print_index: + header_values = [ + "" + x + "" + for x in [ + df.index.name if df.index.name is not None else "", + *df.columns, + ] + ] + cell_values = [df.index, *[df[col] for col in df]] + else: + header_values = ["" + x + "" for x in df.columns.to_list()] + cell_values = [df[col] for col in df] + + return header_values, cell_values + + row_color_list = _alternate_row_colors() + header_vals, cell_vals = _tbl_values() + + if not tbl_header: + tbl_header = dict() + tbl_header.update(values=header_vals) + + if not tbl_header_visible: + tbl_header.update( + fill_color="white", font_color="white", line_color="white", height=1 + ) + + if not tbl_cells: + tbl_cells = dict() + tbl_cells.update( + values=cell_vals, + fill_color=[row_color_list] * len(df) + if row_color_list + else tbl_cells.get("fill_color"), + ) + + fig = plotly.graph_objs.Figure( + data=[plotly.graph_objs.Table(header=tbl_header, cells=tbl_cells)] + ) + + fig.data[0]["columnwidth"] = col_width if col_width else None + + if not title: + title = dict() + title.update( + x=0.01 if title.get("x") is None else title.get("x"), + xanchor="left" if title.get("xanchor") is None else title.get("xanchor"), + ) + + fig.update_layout( + title=title, + margin=dict(autoexpand=False, b=5, l=5, r=5, t=40 if title.get("text") else 5), + width=fig_size[0] if fig_size else None, + height=fig_size[1] if fig_size else None, + autosize=False if col_width else None, + **layout_kwargs, + ) + + return fig diff --git a/bots/config_discordbot.py b/bots/config_discordbot.py old mode 100644 new mode 100755 index 22982c45296..73ecae748e4 --- a/bots/config_discordbot.py +++ b/bots/config_discordbot.py @@ -7,16 +7,17 @@ import disnake import pyimgur from dotenv import load_dotenv -env_files = [f for f in os.listdir() if f.endswith(".env")] +# Path to bots +bots_path = Path(__file__).parent.absolute() + +env_files = [f for f in bots_path.iterdir() if f.as_posix().endswith(".env")] + if env_files: load_dotenv(env_files[0]) # Relative path to the terminal sys.path.append("..") -# Path to bots -bots_path = Path(__file__).parent.absolute() - # https://discord.com/developers/applications/ DISCORD_BOT_TOKEN = os.getenv("GT_DISCORD_BOT_TOKEN") or "REPLACE_ME" @@ -41,86 +42,14 @@ COMMAND_PREFIX = "!" DATE_FORMAT = "%Y-%m-%d" COLOR = disnake.Color.from_rgb(255, 0, 0) INTERACTIVE = False -INTERACTIVE_DIR = bots_path / "in/" +INTERACTIVE_DIR = bots_path / "interactive/" INTERACTIVE_URL = "" -IMG_DIR = bots_path / "in/images/" +IMG_DIR = bots_path / "interactive/images/" IMAGES_URL = "" # Ex. "http://your-site.com/images/" # IMG_BG = bots_path.joinpath("files/bg.png") # Light BG -IMG_BG = bots_path / "files/bg-dark.png" # Dark BG -PLT_3DMESH_COLORSCALE = "Jet" -PLT_3DMESH_SCENE = dict( - xaxis=dict( - backgroundcolor="rgb(94, 94, 94)", - gridcolor="white", - showbackground=True, - zerolinecolor="white", - ), - yaxis=dict( - backgroundcolor="rgb(94, 94, 94)", - gridcolor="white", - showbackground=True, - zerolinecolor="white", - ), - zaxis=dict( - backgroundcolor="rgb(94, 94, 94)", - gridcolor="white", - showbackground=True, - zerolinecolor="white", - ), - aspectratio=dict(x=1.2, y=1.2, z=0.8), -) -PLT_3DMESH_HOVERLABEL = dict(bgcolor="gold") -PLT_3DMESH_STYLE_TEMPLATE = "plotly_dark" -PLT_CANDLE_STYLE_TEMPLATE = "plotly_dark" -PLT_SCAT_STYLE_TEMPLATE = "plotly_dark" -PLT_TA_STYLE_TEMPLATE = "plotly_dark" -PLT_TA_COLORWAY = [ - "#fdc708", - "#d81aea", - "#00e6c3", - "#9467bd", - "#e250c3", - "#d1fa3d", -] -PLT_TBL_HEADER = dict( - height=30, - fill_color="rgb(30, 30, 30)", - font_color="white", - font_size=14, - line_color="rgb(63, 63, 63)", - line_width=2, -) -PLT_TBL_CELLS = dict( - height=25, - font_size=12, - fill_color="rgb(50, 50, 50)", - font_color="white", - line_color="rgb(63, 63, 63)", - line_width=2, -) -PLT_TBL_FONT = dict(family="Fira Code") -PLT_TBL_ROW_COLORS = ["rgb(55, 55, 55)", "rgb(50, 50, 50)"] -PLT_FONT = dict( - family="Fira Code", - size=12, -) -PLT_WATERMARK = dict( - source=( - "https://raw.githubusercontent.com/GamestonkTerminal/GamestonkTerminal/main/images" - "/gst_logo_lockup_rGreen_with_letters_only.png" - ), - xref="paper", - yref="paper", - x=0.88, - y=0.28, - sizex=0.8, - sizey=0.9, - opacity=0.04, - xanchor="right", - yanchor="bottom", - layer="below", -) +IMG_BG = bots_path.joinpath("files/bg-dark.png") # Dark BG + MENU_TIMEOUT = 30 DEBUG = False diff --git a/bots/config_plots.py b/bots/config_plots.py new file mode 100644 index 00000000000..e56a0dbe0fa --- /dev/null +++ b/bots/config_plots.py @@ -0,0 +1,97 @@ +from typing import Any, List, Optional, Tuple + +# Vsurf Plot Settings +PLT_3DMESH_COLORSCALE = "Jet" +PLT_3DMESH_SCENE = dict( + xaxis=dict( + backgroundcolor="rgb(94, 94, 94)", + gridcolor="white", + showbackground=True, + zerolinecolor="white", + ), + yaxis=dict( + backgroundcolor="rgb(94, 94, 94)", + gridcolor="white", + showbackground=True, + zerolinecolor="white", + ), + zaxis=dict( + backgroundcolor="rgb(94, 94, 94)", + gridcolor="white", + showbackground=True, + zerolinecolor="white", + ), + aspectratio=dict(x=1.2, y=1.2, z=0.8), +) +PLT_3DMESH_HOVERLABEL = dict(bgcolor="gold") +PLT_3DMESH_STYLE_TEMPLATE = "plotly_dark" + +# Chart Plots Settings +PLT_CANDLE_STYLE_TEMPLATE = "plotly_dark" +PLT_SCAT_STYLE_TEMPLATE = "plotly_dark" +PLT_TA_STYLE_TEMPLATE = "plotly_dark" +PLT_FONT = dict( + family="Fira Code", + size=12, +) +PLT_TA_COLORWAY = [ + "#fdc708", + "#d81aea", + "#00e6c3", + "#9467bd", + "#e250c3", + "#d1fa3d", +] +PLT_FIB_COLORWAY: List[Any] = [ + "rgba(195, 50, 69, 1)", # 0 + "rgba(130, 38, 96, 1)", # 0.235 + "rgba(72, 39, 124, 1)", # 0.382 + "rgba(0, 93, 168, 1)", # 0.5 + "rgba(173, 0, 95, 1)", # 0.618 + "rgba(253, 199, 8, 1)", # 0.65 Golden Pocket + "rgba(147, 103, 188, 1)", # 1 + dict( + family="Fire Code", + size=16, + ), # Fib's Text + dict(color="rgba(0, 230, 195, 1)", width=2, dash="dash"), # Fib Trendline +] +PLT_WATERMARK = dict( + source=( + "https://raw.githubusercontent.com/GamestonkTerminal/GamestonkTerminal/main/images" + "/gst_logo_lockup_rGreen_with_letters_only.png" + ), + xref="paper", + yref="paper", + x=0.88, + y=0.28, + sizex=0.8, + sizey=0.9, + opacity=0.04, + xanchor="right", + yanchor="bottom", + layer="below", +) + +# Table Plots Settings +PLT_TBL_HEADER = dict( + height=30, + fill_color="rgb(30, 30, 30)", + font_color="white", + font_size=14, + line_color="rgb(63, 63, 63)", + line_width=2, +) +PLT_TBL_CELLS = dict( + height=25, + font_size=12, + fill_color="rgb(50, 50, 50)", + font_color="white", + line_color="rgb(63, 63, 63)", + line_width=2, +) +PLT_TBL_FONT = dict(family="Fira Code") +PLT_TBL_ROW_COLORS: Optional[Tuple[str, str]] = ( + "rgb(55, 55, 55)", + "rgb(50, 50, 50)", +) diff --git a/bots/economy/cpi.py b/bots/economy/cpi.py old mode 100644 new mode 100755 index e809225e91e..612d6054546 --- a/bots/economy/cpi.py +++ b/bots/economy/cpi.py @@ -4,10 +4,9 @@ from datetime import datetime, timedelta import pandas as pd import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end -from gamestonk_terminal.economy.fred import fred_model +from gamestonk_terminal.economy import fred_model logger = logging.getLogger(__name__) @@ -17,7 +16,7 @@ def cpi_command(start=""): """Displays Consumer Prices Index (CPI)""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "econ cpi %s", start, @@ -26,7 +25,7 @@ def cpi_command(start=""): if start == "": start = datetime.now() - timedelta(days=900) else: - start = datetime.strptime(start, cfg.DATE_FORMAT) + start = datetime.strptime(start, imps.DATE_FORMAT) # Retrieve data df_d = ("date", "CPI") @@ -56,7 +55,7 @@ def cpi_command(start=""): end = datetime.now() + timedelta(days=30) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) fig = go.Figure() @@ -67,21 +66,21 @@ def cpi_command(start=""): y=df["CPI"], ) ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) - fig.update_layout( - xaxis_range=[df.index[0], end], - ) + + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) + fig.update_xaxes(dtick="M2", tickformat="%b\n%Y") fig.update_layout( + xaxis_range=[df.index[0], end], margin=dict(l=0, r=0, t=40, b=50), - template=cfg.PLT_SCAT_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_SCAT_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title="CPI Monthly", title_x=0.5, yaxis_title="Consumer Prices", legend_title="", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( fixedrange=False, nticks=20, @@ -98,15 +97,15 @@ def cpi_command(start=""): # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": "Consumer Prices Index", diff --git a/bots/economy/currencies.py b/bots/economy/currencies.py old mode 100644 new mode 100755 index c276cafa258..61d6d106ec4 --- a/bots/economy/currencies.py +++ b/bots/economy/currencies.py @@ -1,27 +1,26 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import wsj_model logger = logging.getLogger(__name__) +# pylint: disable=E1137 @log_start_end(log=logger) def currencies_command(): """Currencies overview [Wall St. Journal]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-currencies") # Retrieve data df = wsj_model.global_currencies() - df = pd.DataFrame.from_dict(df) + df = df.fillna("") # Check for argument if df.empty: @@ -29,42 +28,36 @@ def currencies_command(): df["Last Price"] = pd.to_numeric(df["Last"].astype(float)) df["Change"] = pd.to_numeric(df["Chng"].astype(float)) - df["% Change"] = pd.to_numeric(df["%Chng"].astype(float)) - df = df.drop(columns=["Last", "Chng", "%Chng"]) + df["%Chng"] = pd.to_numeric(df["%Chng"].astype(float)) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) - formats = {"Last Price": "${:.2f}", "Change": "${:.2f}", "% Change": "{:.2f}%"} + formats = { + "Last Price": "${:.2f}", + "Change": "${:.2f}", + "%Chng": "{:.2f}%", + } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 - df = df.fillna("") + df["Change"] = df.apply(lambda x: f"{x['Change']} ({x['%Chng']})", axis=1) + df.set_index(" ", inplace=True) - font_color = ( - ["white"] * 2 - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["Change"].str.contains("-") - ] - ] - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["% Change"].str.contains("-") - ] - ] - ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 2 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["%Chng"].str.contains("-")] + ] + df = df.drop(columns=["Last", "Chng", "%Chng"]) + fig = imps.plot_df( df, - fig_size=(720, (40 + (40 * len(df.index)))), - col_width=[8, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(620, (40 + (40 * len(df.index)))), + col_width=[4.2, 2.4, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -75,7 +68,7 @@ def currencies_command(): ) ) ) - imagefile = save_image("econ-currencies.png", fig) + imagefile = imps.save_image("econ-currencies.png", fig) return { "title": "Economy: [WSJ] Currencies", "imagefile": imagefile, diff --git a/bots/economy/energy.py b/bots/economy/energy.py old mode 100644 new mode 100755 index 35db4367be7..5c9d03f2a80 --- a/bots/economy/energy.py +++ b/bots/economy/energy.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model @@ -16,7 +14,7 @@ def energy_command(): """Displays energy futures data [Finviz]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-energy") # Retrieve data @@ -27,14 +25,14 @@ def energy_command(): if df.empty: raise Exception("No available data found") - formats = {"last": "${:.2f}", "prevClose": "${:.2f}"} + formats = {"last": "${:.2f}", "prevClose": "${:.2f}", "change": "{:.2f}%"} for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 df = df.sort_values(by="ticker", ascending=False) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) df = df.fillna("") @@ -47,23 +45,36 @@ def energy_command(): "change", ] ] + df.index.names = [""] df = df.rename( columns={"prevClose": "PrevClose", "last": "Last", "change": "Change"} ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Yld Chg"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), - col_width=[8, 3, 2], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(550, (40 + (40 * len(df.index)))), + col_width=[4, 2.1, 1.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = imps.save_image("econ-energy.png", fig) - imagefile = save_image("econ-energy.png", fig) return { "title": "[Finviz] Energy Futures", "imagefile": imagefile, diff --git a/bots/economy/feargreed.py b/bots/economy/feargreed.py index 2313b6cbc5e..ce026a4f076 100644 --- a/bots/economy/feargreed.py +++ b/bots/economy/feargreed.py @@ -4,7 +4,7 @@ import os import matplotlib.pyplot as plt -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import cnn_model, cnn_view diff --git a/bots/economy/futures.py b/bots/economy/futures.py old mode 100644 new mode 100755 index 651f0f101eb..a4385d2eff3 --- a/bots/economy/futures.py +++ b/bots/economy/futures.py @@ -1,16 +1,13 @@ import logging import os -import df2img import disnake import pandas as pd -import yahoo_fin.stock_info as si +import requests -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import get_user_agent logger = logging.getLogger(__name__) @@ -20,11 +17,17 @@ def futures_command(): """Futures [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("futures") # Retrieve data - df = si.get_futures() + req = pd.read_html( + requests.get( + "https://finance.yahoo.com/commodities", + headers={"User-Agent": get_user_agent()}, + ).text + ) + df = req[0] # Check for argument if df.empty: @@ -34,34 +37,29 @@ def futures_command(): df["Last Price"] = pd.to_numeric(df["Last Price"].astype(float)) df["Change"] = pd.to_numeric(df["Change"].astype(float)) - formats = {"Last Price": "${:.2f}", "Change": "${:.2f}"} + formats = { + "Last Price": "${:.2f}", + "Change": "${:.2f}", + } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 - df = df.applymap(str) df = df.fillna("") + df["Change"] = df.apply( + lambda x: f"{x['Change']} ({x['% Change']})", axis=1 + ) + df.drop(columns="Symbol") df = df.rename(columns={"Name": " "}) df.set_index(" ", inplace=True) - df = df[["Last Price", "Change", "% Change"]] + df = df[["Last Price", "Change"]] + + font_color = ["white"] * 2 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Change"].str.contains("-")] + ] - font_color = ( - ["white"] * 2 - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["Change"].str.contains("-") - ] - ] - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["% Change"].str.contains("-") - ] - ] - ) dindex = len(df.index) if dindex > 15: embeds: list = [] @@ -69,31 +67,22 @@ def futures_command(): i, i2, end = 0, 0, 15 df_pg, embeds_img, images_list = pd.DataFrame(), [], [] while i < dindex: - df_pg = df.iloc[i:end] + df_pg = df[["Last Price", "Change"]].iloc[i:end] df_pg.append(df_pg) - font_color = ( - ["white"] * 2 - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df_pg["Change"].str.contains("-") - ] + font_color = ["white"] * 2 + [ + [ + "#e4003a" if boolv else "#00ACFF" + for boolv in df_pg["Change"].str.contains("-") ] - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df_pg["% Change"].str.contains("-") - ] - ] - ) - fig = df2img.plot_dataframe( + ] + fig = imps.plot_df( df_pg, - fig_size=(720, (40 + (45 * len(df.index)))), - col_width=[4, 2, 2, 2], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(620, (40 + (45 * len(df.index)))), + col_width=[4.2, 1.8, 2.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -105,14 +94,14 @@ def futures_command(): ) ) imagefile = "econ-futures.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -124,7 +113,7 @@ def futures_command(): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -134,13 +123,13 @@ def futures_command(): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -154,7 +143,7 @@ def futures_command(): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -162,14 +151,14 @@ def futures_command(): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(720, (40 + (45 * len(df.index)))), - col_width=[4, 2, 2, 2], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(620, (40 + (45 * len(df.index)))), + col_width=[4.2, 1.8, 2.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -181,7 +170,7 @@ def futures_command(): ) ) imagefile = "econ-futures.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) output = { "title": title, diff --git a/bots/economy/futures_coms.py b/bots/economy/futures_coms.py old mode 100644 new mode 100755 index 57f9002c9fd..becc0a1127c --- a/bots/economy/futures_coms.py +++ b/bots/economy/futures_coms.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import wsj_model @@ -16,7 +14,7 @@ def futures_coms_command(): """Futures and commodities overview [Wall St. Journal]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-futures") # Retrieve data @@ -28,42 +26,38 @@ def futures_coms_command(): df["Last Price"] = pd.to_numeric(df["Price"].astype(float)) df["Change"] = pd.to_numeric(df["Chg"].astype(float)) - df["% Change"] = pd.to_numeric(df["%Chg"].astype(float)) - df = df.drop(columns=["Price", "Chg", "%Chg"]) + df["%Chg"] = pd.to_numeric(df["%Chg"].astype(float)) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) - formats = {"Last Price": "${:.2f}", "Change": "${:.2f}", "% Change": "{:.2f}%"} + formats = { + "Last Price": "${:.2f}", + "Change": "${:.2f}", + "%Chg": "{:.2f}%", + } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 + df["Change"] = df.apply(lambda x: f"{x['Change']} ({x['%Chg']})", axis=1) + df = df.fillna("") df.set_index(" ", inplace=True) - font_color = ( - ["white"] * 2 - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["Change"].str.contains("-") - ] - ] - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["% Change"].str.contains("-") - ] - ] - ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 2 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["%Chg"].str.contains("-")] + ] + + df = df.drop(columns=["Price", "Chg", "%Chg"]) + fig = imps.plot_df( df, - fig_size=(720, (40 + (40 * len(df.index)))), - col_width=[8, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(620, (40 + (40 * len(df.index)))), + col_width=[4, 2.4, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -74,5 +68,5 @@ def futures_coms_command(): ) ) ) - imagefile = save_image("econ-futures.png", fig) + imagefile = imps.save_image("econ-futures.png", fig) return {"title": "Economy: [WSJ] Futures/Commodities", "imagefile": imagefile} diff --git a/bots/economy/glbonds.py b/bots/economy/glbonds.py old mode 100644 new mode 100755 index e5e02f1dacd..70adc973135 --- a/bots/economy/glbonds.py +++ b/bots/economy/glbonds.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import wsj_model @@ -16,7 +14,7 @@ def glbonds_command(): """Global bonds overview [Wall St. Journal]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-glbonds") # Retrieve data @@ -33,13 +31,13 @@ def glbonds_command(): formats = { "Rate (%)": "{:.2f}%", "Yld (%)": "{:.2f}%", - "Yld Chg (%)": "{:.2f}%", + "Yld Chg (%)": "{:.2f}%", } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) df = df.fillna("") @@ -53,17 +51,29 @@ def glbonds_command(): ], axis="columns", ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Yld Chg"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), + fig_size=(500, (40 + (40 * len(df.index)))), col_width=[8, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) - imagefile = save_image("econ-glbonds.png", fig) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = imps.save_image("econ-glbonds.png", fig) return {"title": "Economy: [WSJ] Global Bonds", "imagefile": imagefile} diff --git a/bots/economy/grains.py b/bots/economy/grains.py old mode 100644 new mode 100755 index f1a17c2e1a8..6de145d4fe5 --- a/bots/economy/grains.py +++ b/bots/economy/grains.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model @@ -16,7 +14,7 @@ def grains_command(): """Displays grains futures data [Finviz]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-grains") # Retrieve data @@ -27,7 +25,7 @@ def grains_command(): if df.empty: raise Exception("No available data found") - formats = {"last": "${:.2f}", "prevClose": "${:.2f}"} + formats = {"last": "${:.2f}", "prevClose": "${:.2f}", "change": "{:.2f}%"} for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 @@ -36,7 +34,7 @@ def grains_command(): df = df.sort_values(by="ticker", ascending=False) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df) df = df[ @@ -46,23 +44,35 @@ def grains_command(): "change", ] ] + df.index.names = [""] df = df.rename( columns={"prevClose": "PrevClose", "last": "Last", "change": "Change"} ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Change"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), - col_width=[6, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(500, (40 + (40 * len(df.index)))), + col_width=[5, 3.2, 2.8, 2.8], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) - - imagefile = save_image("econ-grains.png", fig) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = imps.save_image("econ-grains.png", fig) return { "title": "Economy: [Finviz] Grains Futures", diff --git a/bots/economy/indices.py b/bots/economy/indices.py old mode 100644 new mode 100755 index a0368569f32..1da9bb79c65 --- a/bots/economy/indices.py +++ b/bots/economy/indices.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import wsj_model @@ -15,7 +13,7 @@ logger = logging.getLogger(__name__) def indices_command(): """US indices overview [Wall St. Journal]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-indices") # Retrieve data @@ -27,42 +25,38 @@ def indices_command(): df["Last Price"] = pd.to_numeric(df["Price"].astype(float)) df["Change"] = pd.to_numeric(df["Chg"].astype(float)) - df["% Change"] = pd.to_numeric(df["%Chg"].astype(float)) - df = df.drop(columns=["Price", "Chg", "%Chg"]) + df["%Chg"] = pd.to_numeric(df["%Chg"].astype(float)) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) - formats = {"Last Price": "${:.2f}", "Change": "${:.2f}", "% Change": "{:.2f}%"} + formats = { + "Last Price": "${:.2f}", + "Change": "${:.2f}", + "%Chg": "{:.2f}%", + } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 + df["Change"] = df.apply(lambda x: f"{x['Change']} ({x['%Chg']})", axis=1) + df = df.fillna("") df.set_index(" ", inplace=True) - font_color = ( - ["white"] * 2 - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["Change"].str.contains("-") - ] - ] - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["% Change"].str.contains("-") - ] - ] - ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 2 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["%Chg"].str.contains("-")] + ] + + df = df.drop(columns=["Price", "Chg", "%Chg"]) + fig = imps.plot_df( df, - fig_size=(720, (40 + (40 * len(df.index)))), - col_width=[8, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(620, (40 + (40 * len(df.index)))), + col_width=[4, 2.4, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -73,7 +67,7 @@ def indices_command(): ) ) ) - imagefile = save_image("econ-indices.png", fig) + imagefile = imps.save_image("econ-indices.png", fig) return { "title": "Economy: [WSJ] US Indices", "imagefile": imagefile, diff --git a/bots/economy/meats.py b/bots/economy/meats.py old mode 100644 new mode 100755 index ddf364472e0..3e141cbf174 --- a/bots/economy/meats.py +++ b/bots/economy/meats.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model @@ -16,7 +14,7 @@ def meats_command(): """Displays meats futures data [Finviz]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-meats") # Retrieve data @@ -31,12 +29,12 @@ def meats_command(): df = df.set_index("label") df = df.sort_values(by="ticker", ascending=False) - formats = {"last": "${:.2f}", "prevClose": "${:.2f}"} + formats = {"last": "${:.2f}", "prevClose": "${:.2f}", "change": "{:.2f}%"} for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df) df = df[ @@ -46,23 +44,35 @@ def meats_command(): "change", ] ] + df.index.names = [""] df = df.rename( columns={"prevClose": "PrevClose", "last": "Last", "change": "Change"} ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Change"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), - col_width=[6, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(500, (40 + (40 * len(df.index)))), + col_width=[5, 3, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) - - imagefile = save_image("econ-meats.png", fig) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = imps.save_image("econ-meats.png", fig) return { "title": "Economy: [Finviz] Meats Futures", "imagefile": imagefile, diff --git a/bots/economy/metals.py b/bots/economy/metals.py old mode 100644 new mode 100755 index 7591f6322c1..ba0e6cce632 --- a/bots/economy/metals.py +++ b/bots/economy/metals.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model @@ -15,7 +13,7 @@ logger = logging.getLogger(__name__) def metals_command(): """Displays metals futures data [Finviz]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-metals") # Retrieve data @@ -26,12 +24,12 @@ def metals_command(): if df.empty: raise Exception("No available data found") - formats = {"last": "${:.2f}", "prevClose": "${:.2f}"} + formats = {"last": "${:.2f}", "prevClose": "${:.2f}", "change": "{:.2f}%"} for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df) df = df.sort_values(by="ticker", ascending=False) @@ -51,18 +49,29 @@ def metals_command(): columns={"prevClose": "PrevClose", "last": "Last", "change": "Change"} ) - fig = df2img.plot_dataframe( + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Change"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), - col_width=[6, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(570, (40 + (40 * len(df.index)))), + col_width=[5, 3, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) - imagefile = save_image("econ-metals.png", fig) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = imps.save_image("econ-metals.png", fig) return { "title": "Economy: [WSJ] Metals Futures", "imagefile": imagefile, diff --git a/bots/economy/overview.py b/bots/economy/overview.py old mode 100644 new mode 100755 index b92cf5529dd..c954ee00eb6 --- a/bots/economy/overview.py +++ b/bots/economy/overview.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import wsj_model @@ -15,7 +13,7 @@ logger = logging.getLogger(__name__) def overview_command(): """Market data overview [Wall St. Journal]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-overview") # Retrieve data @@ -27,42 +25,38 @@ def overview_command(): df["Last Price"] = pd.to_numeric(df["Price"].astype(float)) df["Change"] = pd.to_numeric(df["Chg"].astype(float)) - df["% Change"] = pd.to_numeric(df["%Chg"].astype(float)) - df = df.drop(columns=["Price", "Chg", "%Chg"]) + df["%Chg"] = pd.to_numeric(df["%Chg"].astype(float)) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) - formats = {"Last Price": "${:.2f}", "Change": "${:.2f}", "% Change": "{:.2f}%"} + formats = { + "Last Price": "${:.2f}", + "Change": "${:.2f}", + "%Chg": "{:.2f}%", + } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 + df["Change"] = df.apply(lambda x: f"{x['Change']} ({x['%Chg']})", axis=1) + df = df.fillna("") df.set_index(" ", inplace=True) - font_color = ( - ["white"] * 2 - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["Change"].str.contains("-") - ] - ] - + [ - [ - "#e4003a" if boolv else "#00ACFF" - for boolv in df["% Change"].str.contains("-") - ] - ] - ) - fig = df2img.plot_dataframe( + + font_color = ["white"] * 2 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Change"].str.contains("-")] + ] + + df = df.drop(columns=["Price", "Chg", "%Chg"]) + fig = imps.plot_df( df, - fig_size=(720, (40 + (40 * len(df.index)))), - col_width=[8, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(620, (40 + (40 * len(df.index)))), + col_width=[4, 2.4, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -74,7 +68,7 @@ def overview_command(): ) ) - imagefile = save_image("econ-indices.png", fig) + imagefile = imps.save_image("econ-overview.png", fig) return { "title": "Economy: [WSJ] Market Overview", "imagefile": imagefile, diff --git a/bots/economy/performance.py b/bots/economy/performance.py old mode 100644 new mode 100755 index 2e5abd3ddbe..67024b0ff8d --- a/bots/economy/performance.py +++ b/bots/economy/performance.py @@ -1,13 +1,13 @@ import logging +import os -import df2img -import numpy as np +import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model +from gamestonk_terminal.helper_funcs import lambda_long_number_format logger = logging.getLogger(__name__) @@ -35,12 +35,12 @@ def performance_command(economy_group="sector"): } # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-performance %s", economy_group) # Select default group if not economy_group: - if cfg.DEBUG: + if imps.DEBUG: logger.debug("Use default economy_group: 'sector'") economy_group = "sector" @@ -62,19 +62,18 @@ def performance_command(economy_group="sector"): # Output data df = pd.DataFrame(df_group) - df = df.replace(np.nan, 0) df["Volume"] = df["Volume"] / 1_000_000 df["Avg Volume"] = df["Avg Volume"] / 1_000_000 formats = { "Perf Month": "{:.2f}", - "Perf Quart": "{:.2f}", - "Perf Half": "{:.2f}", - "Perf Year": "{:.2f}", - "Perf YTD": "{:.2f}", + "Perf Quart": "{:.2f}%", + "Perf Half": "{:.2f}%", + "Perf Year": "{:.2f}%", + "Perf YTD": "{:.2f}%", "Avg Volume": "{:.0f}M", - "Change": "{:.2f}", + "Change": "{:.2f}%", "Volume": "{:.0f}M", } for col, value in formats.items(): @@ -85,36 +84,118 @@ def performance_command(economy_group="sector"): "Name", "Week", "Month", - "3Month", - "6Month", - "1Year", + "3 Mouth", + "6 Mouth", + "1 Year", "YTD", "Recom", - "Avg Vol.", - "RelVolume", + "Avg Volume", + "Rel Volume", "Change", "Volume", ], axis="columns", ) - - df = df.fillna("") df.set_index("Name", inplace=True) + df = df.fillna("-") + df = df.applymap(lambda x: lambda_long_number_format(x, 2)) - fig = df2img.plot_dataframe( - df, - fig_size=(1500, (40 + (50 * len(df.index)))), - col_width=[10, 3, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, - paper_bgcolor="rgba(0, 0, 0, 0)", - ) - fig.update_traces(cells=(dict(align=["left", "center"]))) + title = f"Economy: [WSJ] Performance {group}" - imagefile = save_image("econ-performance.png", fig) - return { - "title": f"Economy: [WSJ] Performance {group}", - "imagefile": imagefile, - } + dindex = len(df.index) + if dindex > 2: + embeds: list = [] + # Output + i, i2, end = 0, 0, 2 + df_pg, embeds_img, images_list = pd.DataFrame(), [], [] + while i < dindex: + df_pg = df.iloc[i:end] + df_pg.append(df_pg) + fig = imps.plot_df( + df_pg.transpose(), + fig_size=(800, 720), + col_width=[6, 10], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces(cells=(dict(align=["center", "right"]))) + imagefile = "econ_performance.png" + imagefile = imps.save_image(imagefile, fig) + + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile + images_list.append(imagefile) + else: + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) + image_link = uploaded_image.link + os.remove(imagefile_save) + + embeds_img.append( + f"{image_link}", + ) + embeds.append( + disnake.Embed( + title=title, + colour=imps.COLOR, + ), + ) + i2 += 1 + i += 2 + end += 2 + + # Author/Footer + for i in range(0, i2): + embeds[i].set_author( + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, + ) + embeds[i].set_footer( + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, + ) + + i = 0 + for i in range(0, i2): + embeds[i].set_image(url=embeds_img[i]) + + i += 1 + embeds[0].set_footer(text=f"Page 1 of {len(embeds)}") + choices = [ + disnake.SelectOption(label="Home", value="0", emoji="🟢"), + ] + + output = { + "view": imps.Menu, + "title": title, + "embed": embeds, + "choices": choices, + "embeds_img": embeds_img, + "images_list": images_list, + } + else: + fig = imps.plot_df( + df.transpose(), + fig_size=(800, 720), + col_width=[6, 10], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces(cells=(dict(align=["center", "right"]))) + imagefile = imps.save_image("econ_performance.png", fig) + + output = { + "title": title, + "imagefile": imagefile, + } + + return output diff --git a/bots/economy/reverse_repo.py b/bots/economy/reverse_repo.py new file mode 100755 index 00000000000..4d95df4ca1e --- /dev/null +++ b/bots/economy/reverse_repo.py @@ -0,0 +1,171 @@ +import logging +import os + +import disnake +import pandas as pd +import requests + +from bots import imps +from gamestonk_terminal.decorators import log_start_end + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def reverse_repo_command(days: int = 100): + """Displays Reverse Repo [Stocksera.com]""" + + # Debug user input + if imps.DEBUG: + logger.debug("dd repo %s", days) + + df = pd.DataFrame( + requests.get( + f"https://stocksera.pythonanywhere.com/api/reverse_repo/?days={str(days)}" + ).json() + ) + + if df.empty: + raise Exception("No Data Found") + + title = "Reverse Repo [Stocksera]" + + df["Difference"] = df["Amount"].diff().fillna(0) + + formats = { + "Amount": "${:.2f}B", + "Average": "${:.2f}B", + "Difference": "${:.2f}B", + } + for col, value in formats.items(): + df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 + + font_color = ["white"] * 4 + [ + [ + "#e4003a" if boolv else "#00ACFF" + for boolv in df["Difference"].str.contains("-") + ] # type: ignore + ] + + df = df.drop(columns="Moving Avg") + df = df.sort_values(by="Date", ascending=False) + df.set_index("Date", inplace=True) + df.columns = df.columns.str.capitalize() + + dindex = len(df.index) + if dindex > 15: + embeds: list = [] + # Output + i, i2, end = 0, 0, 15 + df_pg, embeds_img, images_list = pd.DataFrame(), [], [] + while i < dindex: + df_pg = df.iloc[i:end] + font_color = ["white"] * 4 + [ + [ + "#e4003a" if boolv else "#00ACFF" + for boolv in df_pg["Difference"].str.contains("-") + ] # type: ignore + ] + df_pg.append(df_pg) + fig = imps.plot_df( + df_pg, + fig_size=(650, (40 + (40 * len(df.index)))), + col_width=[1.8, 1.5, 1.7, 1.3, 1.8], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces( + cells=( + dict( + align=["center", "right", "center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = "dd_r_repo.png" + imagefile = imps.save_image(imagefile, fig) + + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile + images_list.append(imagefile) + else: + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) + image_link = uploaded_image.link + os.remove(imagefile_save) + + embeds_img.append( + f"{image_link}", + ) + embeds.append( + disnake.Embed( + title=title, + colour=imps.COLOR, + ), + ) + i2 += 1 + i += 15 + end += 15 + + # Author/Footer + for i in range(0, i2): + embeds[i].set_author( + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, + ) + embeds[i].set_footer( + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, + ) + + i = 0 + for i in range(0, i2): + embeds[i].set_image(url=embeds_img[i]) + + i += 1 + embeds[0].set_footer(text=f"Page 1 of {len(embeds)}") + choices = [ + disnake.SelectOption(label="Home", value="0", emoji="🟢"), + ] + + output = { + "view": imps.Menu, + "title": title, + "embed": embeds, + "choices": choices, + "embeds_img": embeds_img, + "images_list": images_list, + } + else: + fig = imps.plot_df( + df, + fig_size=(650, (40 + (40 * len(df.index)))), + col_width=[1.8, 1.5, 1.7, 1.3, 1.8], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces( + cells=( + dict( + align=["center", "right", "center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = "dd_r_repo.png" + imagefile = imps.save_image(imagefile, fig) + + output = { + "title": title, + "imagefile": imagefile, + } + return output diff --git a/bots/economy/softs.py b/bots/economy/softs.py old mode 100644 new mode 100755 index 0e268ade5b4..c8e3b294563 --- a/bots/economy/softs.py +++ b/bots/economy/softs.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model @@ -16,7 +14,7 @@ def softs_command(): """Displays softs futures data [Finviz]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-softs") # Retrieve data @@ -27,12 +25,12 @@ def softs_command(): if df.empty: raise Exception("No available data found") - formats = {"last": "${:.2f}", "prevClose": "${:.2f}"} + formats = {"last": "${:.2f}", "prevClose": "${:.2f}", "change": "{:.2f}%"} for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df) df = df.sort_values(by="ticker", ascending=False) @@ -52,19 +50,30 @@ def softs_command(): columns={"prevClose": "PrevClose", "last": "Last", "change": "Change"} ) - fig = df2img.plot_dataframe( + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Change"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), - col_width=[6, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(550, (40 + (40 * len(df.index)))), + col_width=[5, 3, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + imagefile = imps.save_image("econ-softs.png", fig) - imagefile = save_image("econ-softs.png", fig) return { "title": "Economy: [Finviz] Softs Futures", "imagefile": imagefile, diff --git a/bots/economy/usbonds.py b/bots/economy/usbonds.py old mode 100644 new mode 100755 index cb91540b323..df12715d6d0 --- a/bots/economy/usbonds.py +++ b/bots/economy/usbonds.py @@ -1,10 +1,8 @@ import logging -import df2img import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import wsj_model @@ -16,7 +14,7 @@ def usbonds_command(): """US bonds overview [Wall St. Journal]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-usbonds") # Retrieve data @@ -33,7 +31,7 @@ def usbonds_command(): formats = { "Rate (%)": "{:.2f}%", "Yld (%)": "{:.2f}%", - "Yld Chg (%)": "{:.2f}%", + "Yld Chg (%)": "{:.2f}%", } for col, value in formats.items(): df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 @@ -50,19 +48,31 @@ def usbonds_command(): axis="columns", ) - fig = df2img.plot_dataframe( + font_color = ["white"] * 3 + [ + ["#e4003a" if boolv else "#00ACFF" for boolv in df["Yld Chg"].str.contains("-")] + ] + + fig = imps.plot_df( df, - fig_size=(800, (40 + (40 * len(df.index)))), - col_width=[8, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(550, (40 + (40 * len(df.index)))), + col_width=[4, 2, 2, 2.1], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) + fig.update_traces( + cells=( + dict( + align=["center", "right"], + font=dict(color=font_color), + ) + ) + ) + fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = save_image("econ-usbonds.png", fig) + imagefile = imps.save_image("econ-usbonds.png", fig) return { "title": "Economy: [WSJ] US Bonds", "imagefile": imagefile, diff --git a/bots/economy/valuation.py b/bots/economy/valuation.py old mode 100644 new mode 100755 index 0203e0b0320..6e3e8bfe3a4 --- a/bots/economy/valuation.py +++ b/bots/economy/valuation.py @@ -1,13 +1,13 @@ import logging +import os -import df2img -import numpy as np +import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.economy import finviz_model +from gamestonk_terminal.helper_funcs import lambda_long_number_format logger = logging.getLogger(__name__) @@ -35,12 +35,12 @@ def valuation_command(economy_group="sector"): } # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("econ-valuation %s", economy_group) # Select default group if economy_group == "": - if cfg.DEBUG: + if imps.DEBUG: logger.debug("Use default economy_group: 'sector'") economy_group = "sector" @@ -63,64 +63,106 @@ def valuation_command(economy_group="sector"): # Output data df = pd.DataFrame(df_group) - df = df.replace(np.nan, 0) - - df = df.set_axis( - [ - "Name", - "MarketCap", - "P/E", - "FwdP/E", - "PEG", - "P/S", - "P/B", - "P/C", - "P/FCF", - "EPSpast5Y", - "EPSnext5Y", - "Salespast5Y", - "Change", - "Volume", - ], - axis="columns", - ) - - df["P/E"] = pd.to_numeric(df["P/E"].astype(float)) - df["FwdP/E"] = pd.to_numeric(df["FwdP/E"].astype(float)) - df["EPSpast5Y"] = pd.to_numeric(df["EPSpast5Y"].astype(float)) - df["EPSnext5Y"] = pd.to_numeric(df["EPSnext5Y"].astype(float)) - df["Salespast5Y"] = pd.to_numeric(df["Salespast5Y"].astype(float)) - df["Volume"] = pd.to_numeric(df["Volume"].astype(float)) - df["Volume"] = df["Volume"] / 1_000_000 - - formats = { - "P/E": "{:.2f}", - "FwdP/E": "{:.2f}", - "EPSpast5Y": "{:.2f}", - "EPSnext5Y": "{:.2f}", - "Salespast5Y": "{:.2f}", - "Change": "{:.2f}", - "Volume": "{:.0f}M", - } - for col, value in formats.items(): - df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 - - df = df.fillna("") df.set_index("Name", inplace=True) + df = df.fillna("-") + df = df.applymap(lambda x: lambda_long_number_format(x, 2)) - fig = df2img.plot_dataframe( - df, - fig_size=(1600, (40 + (50 * len(df.index)))), - col_width=[12, 5, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 4, 4], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, - paper_bgcolor="rgba(0, 0, 0, 0)", - ) - fig.update_traces(cells=(dict(align=["left", "center"]))) - imagefile = save_image("econ-valuation.png", fig) - return { - "title": "Economy: [Finviz] Valuation", - "imagefile": imagefile, - } + title = f"Economy: [Finviz] Valuation {group}" + + dindex = len(df.index) + if dindex > 2: + embeds: list = [] + # Output + i, i2, end = 0, 0, 2 + df_pg, embeds_img, images_list = pd.DataFrame(), [], [] + while i < dindex: + df_pg = df.iloc[i:end] + df_pg.append(df_pg) + fig = imps.plot_df( + df_pg.transpose(), + fig_size=(800, 720), + col_width=[6, 10], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces(cells=(dict(align=["center", "right"]))) + imagefile = "econ_valuation.png" + imagefile = imps.save_image(imagefile, fig) + + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile + images_list.append(imagefile) + else: + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) + image_link = uploaded_image.link + os.remove(imagefile_save) + + embeds_img.append( + f"{image_link}", + ) + embeds.append( + disnake.Embed( + title=title, + colour=imps.COLOR, + ), + ) + i2 += 1 + i += 2 + end += 2 + + # Author/Footer + for i in range(0, i2): + embeds[i].set_author( + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, + ) + embeds[i].set_footer( + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, + ) + + i = 0 + for i in range(0, i2): + embeds[i].set_image(url=embeds_img[i]) + + i += 1 + embeds[0].set_footer(text=f"Page 1 of {len(embeds)}") + choices = [ + disnake.SelectOption(label="Home", value="0", emoji="🟢"), + ] + + output = { + "view": imps.Menu, + "title": title, + "embed": embeds, + "choices": choices, + "embeds_img": embeds_img, + "images_list": images_list, + } + else: + fig = imps.plot_df( + df.transpose(), + fig_size=(800, 720), + col_width=[6, 10], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces(cells=(dict(align=["center", "right"]))) + imagefile = imps.save_image("econ_valuation.png", fig) + + output = { + "title": title, + "imagefile": imagefile, + } + + return output diff --git a/bots/etf/holdings.py b/bots/etf/holdings.py index a23c85c3a74..d1224b21651 100755 --- a/bots/etf/holdings.py +++ b/bots/etf/holdings.py @@ -1,13 +1,9 @@ import logging import os -import df2img import disnake -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.etf import stockanalysis_model @@ -18,7 +14,7 @@ logger = logging.getLogger(__name__) def holdings_command(etf="", num: int = 15): """Display ETF Holdings. [Source: StockAnalysis]""" - if cfg.DEBUG: + if imps.DEBUG: logger.debug("etfs") holdings = stockanalysis_model.get_etf_holdings(etf.upper()) @@ -38,26 +34,26 @@ def holdings_command(etf="", num: int = 15): while i < dindex: df_pg = holdings.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(800, (40 + (40 * dindex))), - col_width=[3, 4, 4], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(500, (40 + (40 * dindex))), + col_width=[1, 2, 2], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["left", "center"]))) + fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "etf-holdings.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -69,7 +65,7 @@ def holdings_command(etf="", num: int = 15): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -79,13 +75,13 @@ def holdings_command(etf="", num: int = 15): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -99,7 +95,7 @@ def holdings_command(etf="", num: int = 15): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -107,18 +103,18 @@ def holdings_command(etf="", num: int = 15): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( holdings, - fig_size=(800, (40 + (40 * dindex))), - col_width=[3, 4, 4], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(500, (40 + (40 * dindex))), + col_width=[1, 2, 2], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["left", "center"]))) - imagefile = helpers.save_image("etf-holdings.png", fig) + fig.update_traces(cells=(dict(align=["center", "right"]))) + imagefile = imps.save_image("etf-holdings.png", fig) output = { "title": title, diff --git a/bots/etf/tops.py b/bots/etf/tops.py old mode 100644 new mode 100755 index 29f5ec6b9c9..da085399049 --- a/bots/etf/tops.py +++ b/bots/etf/tops.py @@ -1,13 +1,10 @@ import logging import os -import df2img import disnake +import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.etf.discovery import wsj_model @@ -19,7 +16,7 @@ def etfs_disc_command(sort=""): """Displays ETF's Top Gainers/Decliners, Most Active [Wall Street Journal]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("etfs") df_etfs = wsj_model.etf_movers(sort, export=True) @@ -27,12 +24,28 @@ def etfs_disc_command(sort=""): if df_etfs.empty: raise Exception("No available data found") - df_etfs.set_index(" ", inplace=True) - prfx = "Top" - if sort == "active": - prfx = "Most" + prfx = "Most" if sort == "active" else "Top" title = f"ETF Movers ({prfx} {sort.capitalize()})" + df_etfs["Price"] = pd.to_numeric(df_etfs["Price"].astype(float)) + + df_etfs["Price"] = df_etfs.apply(lambda x: f"${x['Price']:.2f}", axis=1) + df_etfs["Change"] = df_etfs.apply( + lambda x: f"${x['Chg']:.2f} ({x['%Chg']:.2f}%)", axis=1 + ) + + df_etfs.set_index(" ", inplace=True) + df_etfs = df_etfs.drop(columns=["Chg", "%Chg"]) + + df_etfs = df_etfs[ + [ + "Name", + "Price", + "Change", + "Vol", + ] + ] + dindex = len(df_etfs.index) if dindex > 15: embeds: list = [] @@ -42,26 +55,26 @@ def etfs_disc_command(sort=""): while i < dindex: df_pg = df_etfs.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1200, (40 + (40 * dindex))), - col_width=[1, 9, 1.5, 1.5, 1.5, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(820, (40 + (40 * dindex))), + col_width=[1.1, 9, 1.5, 3, 1.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["left"]))) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) imagefile = "disc-etfs.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -73,7 +86,7 @@ def etfs_disc_command(sort=""): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -83,13 +96,13 @@ def etfs_disc_command(sort=""): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -103,7 +116,7 @@ def etfs_disc_command(sort=""): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -111,18 +124,18 @@ def etfs_disc_command(sort=""): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_etfs, - fig_size=(1200, (40 + (40 * dindex))), - col_width=[1, 9, 1.5, 1.5, 1.5, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(820, (40 + (40 * dindex))), + col_width=[1, 9, 1.5, 3, 1.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["left"]))) - imagefile = helpers.save_image("disc-etfs.png", fig) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) + imagefile = imps.save_image("disc-etfs.png", fig) output = { "title": title, diff --git a/bots/etf/whatetf.py b/bots/etf/whatetf.py index 52300cc3f8a..704da0e1b68 100755 --- a/bots/etf/whatetf.py +++ b/bots/etf/whatetf.py @@ -1,50 +1,27 @@ import logging import os -import re import time import bs4 -import df2img import disnake -import natsort import pandas as pd import undetected_chromedriver.v2 as uc from selenium.webdriver.common.by import By -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end +# pylint:disable=no-member + logger = logging.getLogger(__name__) -conversion_mapping = { - "K": 1_000, - "M": 1_000_000, -} - -all_units = "|".join(conversion_mapping.keys()) -float_re = natsort.numeric_regex_chooser(natsort.ns.FLOAT | natsort.ns.SIGNED) -unit_finder = re.compile(rf"({float_re})\s*({all_units})", re.IGNORECASE) - - -def unit_replacer(matchobj): - """ - Given a regex match object, return a replacement string where units are modified - """ - number = matchobj.group(1) - unit = matchobj.group(2) - new_number = float(number) * conversion_mapping[unit] - return f"{new_number} in" - @log_start_end(log=logger) def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): """Display ETF Holdings. [Source: StockAnalysis]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("etfs") options = uc.ChromeOptions() @@ -91,10 +68,9 @@ def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): raise Exception("No company holdings found!\n") if sort == "mkt_value": - # [unit_finder.sub(unit_replacer, x) for x in df["Market Value"]] - key = natsort.index_natsorted( + key = imps.natsort.index_natsorted( df["Market Value"], - key=lambda x: unit_finder.sub(unit_replacer, x), + key=lambda x: imps.unit_finder.sub(imps.unit_replacer, x), reverse=True, ) df = df.reindex(key) @@ -112,28 +88,28 @@ def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, fig_size=(800, (40 * dindex)), col_width=[0.6, 3.5, 0.65, 1.1], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( cells=(dict(align=["center", "center", "center", "right"])) ) imagefile = "etf-byticker.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -145,7 +121,7 @@ def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -155,13 +131,13 @@ def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -175,7 +151,7 @@ def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -183,18 +159,18 @@ def by_ticker_command(ticker="", sort="fund_percent", num: int = 15): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, fig_size=(800, (40 * dindex)), col_width=[0.6, 3.5, 0.65, 1.1], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "center", "center", "right"]))) - imagefile = helpers.save_image("etf-holdings.png", fig) + imagefile = imps.save_image("etf-holdings.png", fig) output = { "title": title, diff --git a/bots/groupme/run_groupme.py b/bots/groupme/run_groupme.py index 5ad21810b53..554df197a42 100644 --- a/bots/groupme/run_groupme.py +++ b/bots/groupme/run_groupme.py @@ -1,8 +1,8 @@ import json +from bots.common.helpers import non_slash from bots.groupme.groupme_helpers import send_message from bots.helpers import ShowView -from bots.common.helpers import non_slash def handle_groupme(request) -> bool: diff --git a/bots/helpers.py b/bots/helpers.py old mode 100644 new mode 100755 index 04113a38340..6be0d5067f8 --- a/bots/helpers.py +++ b/bots/helpers.py @@ -4,18 +4,20 @@ import re import uuid from typing import List -import df2img import disnake import financedatabase as fd +import natsort import pandas as pd +import plotly.graph_objects import yfinance as yf from numpy.core.fromnumeric import transpose from PIL import Image from plotly.offline import plot -import bots.config_discordbot as cfg +from bots import imps from bots.groupme.groupme_helpers import send_image, send_message +# pylint: disable=W0613 logger = logging.getLogger(__name__) presets_custom = [ @@ -182,7 +184,21 @@ def quote(ticker): return quote_data -def autocrop_image(image, border=0): +def autocrop_image(image: Image, border=0) -> Image: + """Crop empty space from PIL image + + Parameters + ---------- + image : Image + PIL image to crop + border : int, optional + scale border outwards, by default 0 + + Returns + ------- + Image + Cropped image + """ bbox = image.getbbox() image = image.crop(bbox) (width, height) = image.size @@ -193,18 +209,45 @@ def autocrop_image(image, border=0): return cropped_image -def uuid_get(): +conversion_mapping = { + "K": 1_000, + "M": 1_000_000, +} + +all_units = "|".join(conversion_mapping.keys()) +float_re = natsort.numeric_regex_chooser(natsort.ns.FLOAT | natsort.ns.SIGNED) +unit_finder = re.compile(rf"({float_re})\s*({all_units})", re.IGNORECASE) + + +def unit_replacer(matchobj): + """ + Given a regex match object, return a replacement string where units are modified + """ + number = matchobj.group(1) + unit = matchobj.group(2) + new_number = float(number) * conversion_mapping[unit] + return f"{new_number}" + + +def uuid_get() -> str: + """Returns a UUID + + Returns + ------- + str + UUID Ex. e48c4851a42711ec8e11fb53fa4c20e5 + """ rand = str(uuid.uuid1()).replace("-", "") return rand -def country_autocomp(inter, country: str): # pylint: disable=W0613 +def country_autocomp(inter, country: str): data = fd.show_options("equities", "countries") clow = country.lower() return [country for country in data if country.lower().startswith(clow)][:24] -def industry_autocomp(inter, industry: str): # pylint: disable=W0613 +def industry_autocomp(inter, industry: str): data = fd.show_options("equities", "industries") if not industry: industry = "a" @@ -212,7 +255,7 @@ def industry_autocomp(inter, industry: str): # pylint: disable=W0613 return [industry for industry in data if industry.lower().startswith(ilow)][:24] -def metric_autocomp(inter, metric: str): # pylint: disable=W0613 +def metric_autocomp(inter, metric: str): data: dict = metric_yf_keys if not metric: data = list(data.keys()) # type: ignore @@ -223,7 +266,7 @@ def metric_autocomp(inter, metric: str): # pylint: disable=W0613 ] -def ticker_autocomp(inter, ticker: str): # pylint: disable=W0613 +def ticker_autocomp(inter, ticker: str): if not ticker: return ["Start Typing", "for a", "stock ticker"] print(f"ticker_autocomp [ticker]: {ticker}") @@ -234,14 +277,14 @@ def ticker_autocomp(inter, ticker: str): # pylint: disable=W0613 return [ticker for ticker in df if ticker.lower().startswith(tlow)][:24] -def expiry_autocomp(inter, ticker: str): # pylint: disable=W0613 +def expiry_autocomp(inter, ticker: str): data = inter.filled_options["ticker"] yf_ticker = yf.Ticker(data) dates = list(yf_ticker.options) return dates[:24] -def presets_custom_autocomp(inter, preset: str): # pylint: disable=W0613 +def presets_custom_autocomp(inter, preset: str): df = presets_custom if not preset: return df[:24] @@ -250,7 +293,7 @@ def presets_custom_autocomp(inter, preset: str): # pylint: disable=W0613 return [preset for preset in df if preset.lower().startswith(plow)][:24] -def signals_autocomp(inter, signal: str): # pylint: disable=W0613 +def signals_autocomp(inter, signal: str): df = signals if not signal: return df[:24] @@ -259,8 +302,24 @@ def signals_autocomp(inter, signal: str): # pylint: disable=W0613 return [signal for signal in df if signal.lower().startswith(slow)][:24] -def inter_chart(fig, file, **data): - filename = f"{file.replace('.png', '')}_{uuid_get()}.html" +def inter_chart(fig: plotly.graph_objects, filename: str, **data) -> str: + """Takes plotly chart object and saves as a html file for interactive charts + + Parameters + ---------- + fig : plotly.graph_objects + Table object to autocrop and save + filename : str + Name to save html as + **kawrgs: + config: dict = plotly config Ex. dict(scrollZoom=True, displayModeBar=False) + callback: bool = enable js_callback for clickable news + Returns + ------- + str + Link for interactive charts Ex. f"[Interactive]({imps.INTERACTIVE_URL}/{filename})" + """ + filename = f"{filename.replace('.png', '')}_{uuid_get()}.html" if "config" not in data: config = dict(scrollZoom=True, displayModeBar=False) plot_div = plot(fig, output_type="div", include_plotlyjs=True, config=config) @@ -306,25 +365,54 @@ def inter_chart(fig, file, **data): """ # Write out HTML file - with open(f"{cfg.INTERACTIVE_DIR / filename}", "w") as f: + with open(f"{imps.INTERACTIVE_DIR / filename}", "w") as f: f.write(html_str) - plt_link = f"[Interactive]({cfg.INTERACTIVE_URL}/{filename})" + plt_link = f"[Interactive]({imps.INTERACTIVE_URL}/{filename})" return plt_link -def save_image(file, fig): - imagefile = f"{file.replace('.png', '')}_{uuid_get()}.png" - filesave = cfg.IMG_DIR / imagefile - df2img.save_dataframe(fig=fig, filename=filesave) +def save_image(filename: str, fig: plotly.graph_objects) -> str: + """Takes plotly table object, adds uuid to filename, and autocrops + + Parameters + ---------- + filename : str + Name to save image as + fig : plotly.graph_objects + Table object to autocrop and save + + Returns + ------- + str + filename with UUID added to use for bot processing + """ + imagefile = f"{filename.replace('.png', '')}_{uuid_get()}.png" + filesave = imps.IMG_DIR / imagefile + fig.write_image(filesave) image = Image.open(filesave) image = autocrop_image(image, 0) image.save(filesave, "PNG", quality=100) return imagefile -def image_border(file, **kwargs): - imagefile = f"{file.replace('.png', '')}_{uuid_get()}.png" - filesave = cfg.IMG_DIR / imagefile +def image_border(filename: str, **kwargs) -> str: + """Takes fig, base64, or already saved image and adds border to it + + Parameters + ---------- + filename : str + Name to save image as. If no fig or base64, will try to find + an image with this name to open. + **kawrgs: + fig=plotly.graph_objects + base64=BytesIo object + Returns + ------- + str + filename with UUID added to use for bot processing + """ + imagefile = f"{filename.replace('.png', '')}_{uuid_get()}.png" + filesave = imps.IMG_DIR / imagefile if "fig" in kwargs: fig = kwargs["fig"] fig.write_image(filesave) @@ -333,7 +421,7 @@ def image_border(file, **kwargs): img = Image.open(kwargs["base64"]) else: img = Image.open(filesave) - im_bg = Image.open(cfg.IMG_BG) + im_bg = Image.open(imps.IMG_BG) w = img.width + 520 h = img.height + 240 @@ -366,15 +454,15 @@ class ShowView: else: title = data.get("title", "") embed = disnake.Embed( - title=title, colour=cfg.COLOR, description=data.get("description", "") + title=title, colour=imps.COLOR, description=data.get("description", "") ) embed.set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) if "imagefile" in data: filename = data["imagefile"] - imagefile = cfg.IMG_DIR / filename + imagefile = (imps.IMG_DIR / filename).as_posix() image = disnake.File(imagefile, filename=filename) embed.set_image(url=f"attachment://{filename}") os.remove(imagefile) @@ -393,25 +481,25 @@ class ShowView: except Exception as e: embed = disnake.Embed( title=name, - colour=cfg.COLOR, + colour=imps.COLOR, description=e, ) embed.set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) await inter.send(embed=embed, delete_after=30.0) - def groupme(self, func, group_id, name, *args, **kwargs): # pylint: disable=W0613 + def groupme(self, func, group_id, name, *args, **kwargs): data = func(*args, **kwargs) if "imagefile" in data: - imagefile = cfg.IMG_DIR / data["imagefile"] + imagefile = (imps.IMG_DIR / data["imagefile"]).as_posix() send_image(imagefile, group_id, data.get("description", "")) elif "embeds_img" in data: imagefiles = data["images_list"] for img in imagefiles: - imagefile = cfg.IMG_DIR / img + imagefile = (imps.IMG_DIR / img).as_posix() send_image(imagefile, group_id, data.get("description", "")) elif "description" in data: title = data.get("title", "") @@ -435,7 +523,7 @@ class ShowView: .replace(".html)", ".html\n\n") ) message = f"{title}\n{description}" - imagefile = cfg.IMG_DIR / data["imagefile"] + imagefile = (imps.IMG_DIR / data["imagefile"]).as_posix() client.files_upload( file=imagefile, initial_comment=message, @@ -452,7 +540,7 @@ class ShowView: title = data["title"] if "titles" not in data else data["titles"][0] N = 0 for img in data["images_list"]: - imagefile = cfg.IMG_DIR / img + imagefile = (imps.IMG_DIR / img).as_posix() if N == 0: message = f"{title}\n{description}" payload = { @@ -483,12 +571,10 @@ class ShowView: payload = {"channel": channel_id, "username": user_id, "text": message} client.chat_postMessage(**payload) - def telegram( - self, func, message, bot, cmd, *args, **kwargs - ): # pylint: disable=W0613 + def telegram(self, func, message, bot, cmd, *args, **kwargs): data = func(*args, **kwargs) if "imagefile" in data: - imagefile = cfg.IMG_DIR / data["imagefile"] + imagefile = (imps.IMG_DIR / data["imagefile"]).as_posix() title = data["title"] description = ( data.get("description", "") @@ -502,23 +588,21 @@ class ShowView: bot.send_photo(message.chat.id, image) os.remove(imagefile) elif "embeds_img" in data: - title = data["title"] if "titles" not in data else data["titles"][0] + res_title = data["title"] if "titles" not in data else data["titles"][0] N = 0 for img in data["images_list"]: - imagefile = cfg.IMG_DIR / img + imagefile = (imps.IMG_DIR / img).as_posix() if N == 0: description = ( data.get("description", "") .replace("[Interactive](", "") .replace(".html)", ".html\n\n") ) - res = f"{title}\n{description}" - bot.reply_to(message, res) - title = "" + res_title = f"{res_title}\n{description}" if N < len(data["titles"]) and N != 0: - title = data["titles"][N] + res_title = data["titles"][N] with open(imagefile, "rb") as image: - bot.reply_to(message, title) + bot.reply_to(message, res_title) bot.send_photo(message.chat.id, image) N += 1 os.remove(imagefile) diff --git a/bots/imps.py b/bots/imps.py new file mode 100644 index 00000000000..3201bcb969e --- /dev/null +++ b/bots/imps.py @@ -0,0 +1,8 @@ +"""Bot Imports""" +# flake8: noqa +# pylint: disable=unused-import,W0401,W0614 +from .common.plot_tbls import * +from .config_discordbot import * +from .config_plots import * +from .helpers import * +from .menus.menu import Menu diff --git a/tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom[custom_imports/stocks/test.csv].txt b/bots/interactive/__init__.py similarity index 100% rename from tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom[custom_imports/stocks/test.csv].txt rename to bots/interactive/__init__.py diff --git a/tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom[none].txt b/bots/interactive/images/__init__.py similarity index 100% rename from tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom[none].txt rename to bots/interactive/images/__init__.py diff --git a/bots/load_candle.py b/bots/load_candle.py old mode 100644 new mode 100755 index 4b7c519f656..4acca9abd10 --- a/bots/load_candle.py +++ b/bots/load_candle.py @@ -12,7 +12,7 @@ from binance.client import Client from plotly.subplots import make_subplots from scipy import stats -import bots.config_discordbot as cfg +from bots import imps futures = "=F" or "^" crypto = "-" @@ -143,7 +143,7 @@ def stock_data( max_days = day_list[interval] if dt_utcnow_local_tz().weekday() not in range(2, 5): - past_days += 2 + past_days += 3 p_days = ( ((past_days + 1) if (past_days < max_days) else max_days) if (interval != 1440 and past_days < 365) @@ -157,12 +157,12 @@ def stock_data( bar_start if (bar_start < start) else start ) # Check if past days requested further back than start else: - start = datetime.strptime(start, cfg.DATE_FORMAT) - bar_start = datetime.strptime(start, cfg.DATE_FORMAT) - timedelta(days=p_days) + start = datetime.strptime(start, imps.DATE_FORMAT).astimezone(est_tz) + bar_start = start - timedelta(days=p_days) if end == "": end = dt_utcnow_local_tz() else: - end = datetime.strptime(end, cfg.DATE_FORMAT) + end = datetime.strptime(end, imps.DATE_FORMAT).astimezone(est_tz) if interval == 1440 and crypto not in ticker.upper(): df_stock = yf.download( @@ -192,8 +192,8 @@ def stock_data( (past_days + 10) if (((10 + past_days) + max_days) < max_days) else max_days ) s_int = str(interval) + "m" - if crypto in ticker.upper() and (cfg.API_BINANCE_KEY != "REPLACE_ME"): - client = Client(cfg.API_BINANCE_KEY, cfg.API_BINANCE_SECRET) + if crypto in ticker.upper() and (imps.API_BINANCE_KEY != "REPLACE_ME"): + client = Client(imps.API_BINANCE_KEY, imps.API_BINANCE_SECRET) interval_map = { "1440m": client.KLINE_INTERVAL_1DAY, "60m": client.KLINE_INTERVAL_1HOUR, @@ -322,7 +322,7 @@ def candle_fig( data["bar"] > (dt_utcnow_local_tz() - timedelta(days=(dt[data["int_bar"]] * 3))) ) - else 0.4 + else 0.33 ) bar_opacity = ( bar_opacity @@ -330,7 +330,7 @@ def candle_fig( data["bar"] > (dt_utcnow_local_tz() - timedelta(days=(dt[data["int_bar"]] * 4))) ) - else 0.6 + else 0.35 ) bar_opacity = ( bar_opacity @@ -338,7 +338,7 @@ def candle_fig( data["bar"] > (dt_utcnow_local_tz() - timedelta(days=(dt[data["int_bar"]] * 10))) ) - else 0.7 + else 0.4 ) else: bar_opacity = 0.2 @@ -405,7 +405,7 @@ def candle_fig( df_date, df_title, df_current, df_content, df_url = [], [], [], [], [] - if (cfg.API_NEWS_TOKEN != "REPLACE_ME") and crypto in ticker.upper(): + if (imps.API_NEWS_TOKEN != "REPLACE_ME") and crypto in ticker.upper(): d_stock = yf.Ticker(ticker).info s_from = (dt_utcnow_local_tz() - timedelta(days=28)).strftime("%Y-%m-%d") term = ( @@ -416,7 +416,7 @@ def candle_fig( link = ( f"https://newsapi.org/v2/everything?q={term}&from={s_from}&sortBy=publishedAt&language=en" - f"&apiKey={cfg.API_NEWS_TOKEN}" + f"&apiKey={imps.API_NEWS_TOKEN}" ) response = requests.get(link) articles = response.json()["articles"] @@ -433,12 +433,12 @@ def candle_fig( df_content.append(textwrap.indent(text=content, prefix="
")) df_url.append(article["url"]) - elif cfg.API_FINNHUB_KEY != "REPLACE_ME": - finnhub_client = finnhub.Client(api_key=cfg.API_FINNHUB_KEY) + elif imps.API_FINNHUB_KEY != "REPLACE_ME": + finnhub_client = finnhub.Client(api_key=imps.API_FINNHUB_KEY) start_new = (dt_utcnow_local_tz() - timedelta(days=30)).strftime( - cfg.DATE_FORMAT + imps.DATE_FORMAT ) - end_new = dt_utcnow_local_tz().strftime(cfg.DATE_FORMAT) + end_new = dt_utcnow_local_tz().strftime(imps.DATE_FORMAT) articles = finnhub_client.company_news( ticker.upper(), _from=start_new, to=end_new ) @@ -515,8 +515,8 @@ def candle_fig( row=1, col=1, ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.add_annotation( xref="x domain", yref="y domain", @@ -544,10 +544,10 @@ def candle_fig( fig.update_yaxes(showline=True) fig.update_layout( margin=dict(l=0, r=10, t=40, b=20), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, yaxis2_title="Price", yaxis_title="Volume", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( showgrid=False, fixedrange=False, diff --git a/bots/menus/menu.py b/bots/menus/menu.py index 35503c8bca7..6345eab5a81 100644 --- a/bots/menus/menu.py +++ b/bots/menus/menu.py @@ -3,8 +3,7 @@ from typing import List import disnake from disnake.ext import commands -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps bot = commands.Bot() @@ -34,7 +33,7 @@ class Menu(disnake.ui.View): for i, embed in enumerate(self.embeds): embed.set_footer( text=f"Page {i + 1} of {len(self.embeds)}", - icon_url=cfg.AUTHOR_ICON_URL, + icon_url=imps.AUTHOR_ICON_URL, ) def set_link_button(self) -> None: @@ -51,7 +50,7 @@ class Menu(disnake.ui.View): @disnake.ui.select( placeholder="Page Select", - custom_id=f"select_{str(disnake.Member)}_{helpers.uuid_get()}", + custom_id=f"select_{str(disnake.Member)}_{imps.uuid_get()}", row=1, ) async def selector( @@ -77,7 +76,7 @@ class Menu(disnake.ui.View): label="Previous page", emoji="", style=disnake.ButtonStyle.red, - custom_id=f"persistent_view:prevpage_{str(disnake.Member)}_{helpers.uuid_get()}", + custom_id=f"persistent_view:prevpage_{str(disnake.Member)}_{imps.uuid_get()}", ) async def prev_page( # pylint: disable=W0613 self, @@ -101,7 +100,7 @@ class Menu(disnake.ui.View): label="Next page", emoji="", style=disnake.ButtonStyle.red, - custom_id=f"persistent_view:nextpage_{str(disnake.Member)}_{helpers.uuid_get()}", + custom_id=f"persistent_view:nextpage_{str(disnake.Member)}_{imps.uuid_get()}", ) async def next_page( # pylint: disable=W0613 self, diff --git a/bots/requirements.txt b/bots/requirements.txt old mode 100644 new mode 100755 index 6ceed8d8311..21cae8c51c0 --- a/bots/requirements.txt +++ b/bots/requirements.txt @@ -4,72 +4,73 @@ alabaster==0.7.12; python_version >= "3.7" alpha-vantage==2.3.1 ansiwrap==0.8.4; python_version >= "3.6" anyio==3.5.0; python_full_version >= "3.6.2" and python_version >= "3.7" -appdirs==1.4.4; python_full_version >= "3.6.2" and python_version >= "3.8" -appnope==0.1.2; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" +appnope==0.1.2; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" and python_full_version >= "3.6.2" argon2-cffi-bindings==21.2.0; python_version >= "3.7" argon2-cffi==21.3.0; python_version >= "3.7" ascii-magic==1.6; python_version >= "3.5" asgiref==3.5.0; python_version >= "3.7" astor==0.8.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" astroid==2.9.3; python_full_version >= "3.6.2" -asttokens==2.0.5; python_version >= "3.8" +asttokens==2.0.5; python_full_version >= "3.6.2" and python_version >= "3.8" +async-generator==1.10; python_version >= "3.7" and python_version < "4.0" async-timeout==4.0.2; python_version >= "3.6" and python_full_version >= "3.8.0" atomicwrites==1.4.0; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.4.0" -attrs==21.4.0; python_full_version >= "3.8.0" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") +attrs==21.4.0; python_full_version >= "3.8.0" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") babel==2.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -backcall==0.2.0; python_version >= "3.8" +backcall==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8" backports.zoneinfo==0.2.1; python_version < "3.9" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") -bandit==1.7.2; python_version >= "3.7" +bandit==1.7.4; python_version >= "3.7" beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.6.0" beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.7" -black==21.7b0; python_full_version >= "3.6.2" +black==22.1.0; python_full_version >= "3.6.2" bleach==4.1.0; python_version >= "3.7" +boto3==1.21.25; python_version >= "3.6" +botocore==1.24.25; python_version >= "3.6" bs4==0.0.1 bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") -certifi==2021.10.8; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") -cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0" +certifi==2021.10.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") +cffi==1.15.0; python_version >= "3.7" cfgv==3.3.1; python_full_version >= "3.6.1" charset-normalizer==2.0.12; python_full_version >= "3.8.0" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") -click==8.0.4; python_version >= "3.8" and python_full_version >= "3.6.2" +click==8.0.4; python_version >= "3.7" and python_full_version >= "3.6.2" codespell==2.1.0; python_version >= "3.5" -colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and platform_system == "Windows" and python_version >= "3.7" and (python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.8" and python_full_version >= "3.5.0") +colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and platform_system == "Windows" and python_version >= "3.8" commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" convertdate==2.4.0; python_version >= "3.7" and python_version < "4" coverage==6.3.2; python_version >= "3.7" -cryptography==36.0.1; python_version >= "3.6" +cryptography==36.0.2; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" cssselect==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" -cvxpy==1.1.18; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6" +cvxpy==1.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" cycler==0.11.0; python_version >= "3.7" cython==0.29.28; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "darwin" or python_full_version >= "3.3.0" and sys_platform == "darwin" and python_version >= "3.7" -dateparser==1.1.0; python_version >= "3.5" +dateparser==1.1.1; python_version >= "3.5" datetime==4.4; python_version >= "3.5" debugpy==1.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" -decorator==5.1.1; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" +decorator==5.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" -degiro-connector==2.0.17; python_full_version >= "3.7.1" and python_full_version < "4.0.0" +degiro-connector==2.0.18; python_full_version >= "3.7.1" and python_full_version < "4.0.0" deprecation==2.1.0 detecta==0.0.5; python_version >= "3.6" -df2img==0.2.4; python_version >= "3.8" and python_version < "4.0" disnake==2.4.0; python_full_version >= "3.8.0" distlib==0.3.4; python_full_version >= "3.6.1" -dnspython==2.2.0; python_version >= "3.6" and python_version < "4.0" +dnspython==2.2.1; python_version >= "3.6" and python_version < "4.0" docutils==0.16; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" -ecos==2.0.10; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6" +ecos==2.0.10; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" entrypoints==0.4; python_full_version >= "3.7.0" and python_version >= "3.7" et-xmlfile==1.1.0; python_version >= "3.6" -exchange-calendars==3.6; python_version >= "3.7" and python_full_version >= "3.7.0" -executing==0.8.2; python_version >= "3.8" +exchange-calendars==3.6.1; python_version >= "3.7" and python_full_version >= "3.7.0" +executing==0.8.3; python_full_version >= "3.6.2" and python_version >= "3.8" fastapi==0.73.0; python_full_version >= "3.6.1" fear-greed-index==0.1.4; python_version >= "3.7" ffn==0.3.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" filelock==3.6.0; python_version >= "3.7" and python_full_version >= "3.6.1" financedatabase==1.0.2 -finnhub-python==2.4.10 -finviz==1.4.3 -finvizfinance==0.9.10; python_version >= "3.5" +finnhub-python==2.4.11 +finviz==1.4.4 +finvizfinance==0.12.2; python_version >= "3.5" flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") -fonttools==4.29.1; python_version >= "3.7" -formulaic==0.2.4; python_version >= "3.7" +fonttools==4.31.2; python_version >= "3.7" +formulaic==0.3.2; python_version >= "3.7" fred==3.1 fredapi==0.4.3 frozendict==2.3.0; python_version >= "3.6" @@ -78,48 +79,49 @@ fundamentalanalysis==0.2.12 future==0.18.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" gitdb==4.0.9; python_version >= "3.7" gitpython==3.1.27; python_version >= "3.7" -grpcio==1.44.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6" -h11==0.13.0; python_version >= "3.7" +grpcio==1.45.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6" +h11==0.13.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" hijri-converter==2.2.3; python_version >= "3.6" holidays==0.11.3.1; python_version >= "3.6" -identify==2.4.11; python_version >= "3.7" and python_full_version >= "3.6.1" -idna==3.3; python_full_version >= "3.8.0" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") +identify==2.4.12; python_version >= "3.7" and python_full_version >= "3.6.1" +idna==3.3; python_full_version >= "3.8.0" and python_version >= "3.8" and python_version < "4" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") imagesize==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" inflection==0.5.1; python_version >= "3.6" iniconfig==1.1.1; python_version >= "3.7" -interface-meta==1.2.4; python_version >= "3.7" +interface-meta==1.2.5; python_version >= "3.7" investpy==1.0.8; python_version >= "3.7" -ipykernel==6.9.1; python_version >= "3.7" +ipykernel==6.9.2; python_version >= "3.7" ipympl==0.8.8 -ipython-genutils==0.2.0; python_full_version >= "3.7.0" and python_version >= "3.7" -ipython==8.0.1; python_version >= "3.8" -ipywidgets==7.6.5 +ipython-genutils==0.2.0; python_version >= "3.6" +ipython==8.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" +ipywidgets==7.7.0 iso8601==0.1.16 isort==5.10.1; python_full_version >= "3.6.2" and python_version < "4.0" -jedi==0.18.1; python_version >= "3.8" -jinja2==3.0.3; python_version >= "3.7" +jedi==0.18.1; python_full_version >= "3.6.2" and python_version >= "3.8" +jinja2==3.1.0; python_version >= "3.7" +jmespath==1.0.0; python_version >= "3.7" joblib==1.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" json5==0.9.6; python_version >= "3.7" jsonschema==3.2.0 jupyter-client==7.1.2; python_full_version >= "3.7.0" and python_version >= "3.7" -jupyter-console==6.4.0; python_version >= "3.6" +jupyter-console==6.4.3; python_version >= "3.6" jupyter-core==4.9.2; python_full_version >= "3.7.0" and python_version >= "3.7" -jupyter-server==1.13.5; python_version >= "3.7" +jupyter-server==1.15.6; python_version >= "3.7" jupyter==1.0.0 jupyterlab-pygments==0.1.2; python_version >= "3.7" -jupyterlab-server==2.10.3; python_version >= "3.7" -jupyterlab-widgets==1.0.2; python_version >= "3.6" -jupyterlab==3.2.9; python_version >= "3.6" -kaleido==0.2.1; python_version >= "3.8" and python_version < "4.0" -kiwisolver==1.3.2; python_version >= "3.7" +jupyterlab-server==2.11.2; python_version >= "3.7" +jupyterlab-widgets==1.1.0; python_version >= "3.6" +jupyterlab==3.3.2; python_version >= "3.7" +kaleido==0.2.1.post1 +kiwisolver==1.4.0; python_version >= "3.7" korean-lunar-calendar==0.2.1; python_version >= "3.7" and python_full_version >= "3.7.0" lazy-object-proxy==1.7.1; python_version >= "3.6" and python_full_version >= "3.6.2" linearmodels==4.25; python_version >= "3.7" lxml==4.8.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" m2r2==0.2.8; python_version >= "3.7" markdown-it-py==1.1.0; python_version >= "3.6" and python_version < "4.0" -markupsafe==2.1.0; python_version >= "3.7" -matplotlib-inline==0.1.3; python_version >= "3.8" +markupsafe==2.1.1; python_version >= "3.7" +matplotlib-inline==0.1.3; python_full_version >= "3.6.2" and python_version >= "3.8" matplotlib==3.5.1; python_version >= "3.7" mccabe==0.6.1; python_full_version >= "3.6.2" mdit-py-plugins==0.2.8; python_version >= "3.6" and python_version < "4.0" @@ -129,22 +131,25 @@ more-itertools==8.12.0; python_version >= "3.6" mplfinance==0.12.8b9 multidict==6.0.2; python_version >= "3.7" and python_full_version >= "3.8.0" multitasking==0.0.10 -mypy-extensions==0.4.3; python_version >= "3.8" and python_full_version >= "3.6.2" +mypy-extensions==0.4.3; python_version >= "3.7" and python_full_version >= "3.6.2" mypy==0.930; python_version >= "3.6" myst-parser==0.15.2; python_version >= "3.6" -nbclassic==0.3.5; python_version >= "3.6" -nbclient==0.5.11; python_full_version >= "3.7.0" and python_version >= "3.7" -nbconvert==6.4.2; python_version >= "3.7" -nbformat==5.1.3; python_full_version >= "3.7.0" and python_version >= "3.7" +natsort==8.1.0; python_version >= "3.6" +nbclassic==0.3.7; python_version >= "3.7" +nbclient==0.5.13; python_full_version >= "3.7.0" and python_version >= "3.7" +nbconvert==6.4.4; python_version >= "3.7" +nbformat==5.2.0; python_full_version >= "3.7.0" and python_version >= "3.7" nest-asyncio==1.5.4; python_full_version >= "3.7.0" and python_version >= "3.7" nodeenv==1.6.0; python_full_version >= "3.6.1" -notebook==6.4.8; python_version >= "3.6" +notebook-shim==0.1.0; python_version >= "3.7" +notebook==6.4.10; python_version >= "3.7" numpy==1.21.2; python_version >= "3.7" and python_version < "3.11" oandapyv20==0.6.3 oauthlib==3.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" onetimepass==1.0.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" openpyxl==3.0.9; python_version >= "3.6" -osqp==0.6.2.post5; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6" +osqp==0.6.2.post5; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" +outcome==1.1.0; python_version >= "3.7" and python_version < "4.0" packaging==21.3; python_version >= "3.7" pandas-datareader==0.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" pandas-market-calendars==3.2; python_full_version >= "3.7.0" @@ -152,12 +157,12 @@ pandas-ta==0.3.14b pandas==1.3.5; python_full_version >= "3.7.1" pandocfilters==1.5.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" papermill==2.3.4; python_version >= "3.6" -parso==0.8.3; python_version >= "3.8" -pathspec==0.9.0; python_full_version >= "3.6.2" and python_version >= "3.8" +parso==0.8.3; python_full_version >= "3.6.2" and python_version >= "3.8" +pathspec==0.9.0; python_full_version >= "3.6.2" patsy==0.5.2; python_version >= "3.7" pbr==5.8.1; python_version >= "3.7" -pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" -pickleshare==0.7.5; python_version >= "3.8" +pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" and python_full_version >= "3.6.2" +pickleshare==0.7.5; python_full_version >= "3.6.2" and python_version >= "3.8" pillow==9.0.1; python_version >= "3.7" and python_version < "4" platformdirs==2.5.1; python_version >= "3.7" and python_full_version >= "3.6.2" plotly==5.6.0; python_version >= "3.6" @@ -171,14 +176,15 @@ prompt-toolkit==3.0.28; python_full_version >= "3.6.2" property-cached==1.6.4; python_version >= "3.7" protobuf==3.19.4; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.5" psaw==0.0.12; python_version >= "3" -ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" -pure-eval==0.2.2; python_version >= "3.8" +psutil==5.9.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" +ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" and python_full_version >= "3.6.2" +pure-eval==0.2.2; python_full_version >= "3.6.2" and python_version >= "3.8" py==1.11.0; python_full_version >= "3.7.0" and python_version >= "3.7" and implementation_name == "pypy" pyally==1.1.2 pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" pycoingecko==2.2.0 -pycparser==2.21; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" -pydantic==1.9.0; python_full_version >= "3.6.1" +pycparser==2.21; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" and os_name == "nt" and implementation_name != "pypy" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" and os_name == "nt" and implementation_name != "pypy" +pydantic==1.8.2; python_full_version >= "3.6.1" pyex==0.5.0 pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" pygments==2.11.2; python_version >= "3.5" @@ -189,13 +195,15 @@ pyluach==1.4.0; python_version >= "3.7" and python_full_version >= "3.7.0" pymeeus==0.5.11; python_version >= "3.7" and python_version < "4" pymongo==3.11.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0") pynacl==1.5.0; python_version >= "3.6" -pyobjc-core==8.2; python_version >= "3.6" and sys_platform == "darwin" -pyobjc-framework-cocoa==8.2; python_version >= "3.6" and sys_platform == "darwin" +pyobjc-core==8.4.1; python_version >= "3.6" and sys_platform == "darwin" +pyobjc-framework-cocoa==8.4.1; python_version >= "3.6" and sys_platform == "darwin" +pyopenssl==22.0.0; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" pyotp==2.6.0; python_version >= "3" pyparsing==3.0.7; python_version >= "3.7" -pyportfolioopt==1.5.1; python_full_version >= "3.6.1" and python_full_version < "4.0.0" +pyportfolioopt==1.5.2; python_full_version >= "3.6.1" and python_full_version < "4.0.0" pyprind==2.11.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" pyrsistent==0.18.1; python_version >= "3.7" +pysocks==1.7.1; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" pytelegrambotapi==4.4.0 pytest-cov==3.0.0; python_version >= "3.6" pytest-mock==3.7.0; python_version >= "3.7" @@ -203,43 +211,45 @@ pytest-recording==0.12.0; python_version >= "3.5" pytest==6.2.5; python_version >= "3.6" python-binance==1.0.15 python-coinmarketcap==0.2 -python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.8" and python_full_version < "4.0.0" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") +python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") python-dotenv==0.19.2; python_version >= "3.5" pytrends==4.8.0 pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_full_version < "4.0.0" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7") -pyupgrade==2.31.0; python_full_version >= "3.6.1" +pytz==2022.1; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7") +pyupgrade==2.31.1; python_version >= "3.7" pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" and python_full_version >= "3.7.0" -pywinpty==1.1.6; os_name == "nt" and python_version >= "3.7" +pywinpty==2.0.5; os_name == "nt" and python_version >= "3.7" pyyaml==6.0; python_version >= "3.7" and python_full_version >= "3.6.1" pyzmq==22.3.0; python_full_version >= "3.7.0" and python_version >= "3.7" -qdldl==0.1.5.post0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6" +qdldl==0.1.5.post0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" qtconsole==5.2.2; python_version >= "3.6" qtpy==2.0.1; python_version >= "3.6" quandl==3.7.0; python_version >= "3.6" rapidfuzz==1.9.1; python_version >= "2.7" -regex==2022.1.18; python_full_version >= "3.6.2" and python_version >= "3.8" -reportlab==3.6.7; python_version >= "3.6" and python_version < "4" +regex==2022.3.2; python_version >= "3.6" +reportlab==3.6.8; python_version >= "3.6" and python_version < "4" requests-oauthlib==1.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" requests==2.27.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") rich==10.16.2; python_full_version >= "3.6.2" and python_full_version < "4.0.0" robin-stocks==2.1.0; python_version >= "3" +s3transfer==0.5.2; python_version >= "3.6" scikit-learn==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") screeninfo==0.6.7 -scs==3.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.6" +scs==3.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" seaborn==0.11.2; python_version >= "3.6" -selenium==3.141.0 +selenium==4.1.3; python_version >= "3.7" and python_version < "4.0" send2trash==1.8.0; python_version >= "3.7" sentiment-investor==2.1.0; python_version >= "3.8" and python_version < "4.0" setuptools-scm==6.4.2; python_version >= "3.7" -six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_full_version < "4.0.0" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.8" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.3.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8") -slack-bolt==1.11.6; python_version >= "3.6" +six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") +slack-bolt==1.13.0; python_version >= "3.6" slack-sdk==3.15.2; python_full_version >= "3.6.0" and python_version >= "3.6" smmap==5.0.0; python_version >= "3.7" -sniffio==1.2.0; python_full_version >= "3.6.2" and python_version >= "3.7" +sniffio==1.2.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.2" snowballstemmer==2.2.0; python_version >= "3.7" socketio-client-nexus==0.7.6 +sortedcontainers==2.4.0; python_version >= "3.7" and python_version < "4.0" soupsieve==2.3.1; python_version >= "3.6" and python_full_version > "3.0.0" sphinx-rtd-theme==0.5.2; python_version >= "3.7" sphinx==4.1.1; python_version >= "3.6" @@ -251,52 +261,56 @@ sphinxcontrib-qthelp==1.0.3; python_version >= "3.7" sphinxcontrib-serializinghtml==1.1.5; python_version >= "3.7" squarify==0.4.3 sseclient==0.0.27 -stack-data==0.2.0; python_version >= "3.8" +stack-data==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8" starlette==0.17.1; python_version >= "3.6" and python_full_version >= "3.6.1" statsmodels==0.13.2; python_version >= "3.7" stevedore==3.5.0; python_version >= "3.7" tabulate==0.8.9; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" temporal-cache==0.1.4 -tenacity==8.0.1; python_version >= "3.8" and python_version < "4.0" -terminado==0.13.1; python_version >= "3.7" -testpath==0.5.0; python_version >= "3.7" +tenacity==8.0.1; python_version >= "3.6" +terminado==0.13.3; python_version >= "3.7" +testpath==0.6.0; python_version >= "3.7" textwrap3==0.9.2; python_version >= "3.6" thepassiveinvestor==1.0.10 threadpoolctl==3.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" -tokenize-rt==4.2.1; python_full_version >= "3.6.1" +tokenize-rt==4.2.1; python_full_version >= "3.6.2" and python_version >= "3.7" toml==0.10.2; python_full_version >= "3.6.2" and python_version >= "3.6" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") -tomli==1.2.3; python_version >= "3.8" and python_full_version >= "3.6.2" +tomli==2.0.1; python_version >= "3.7" and python_full_version >= "3.6.2" toolz==0.11.2; python_version >= "3.7" and python_full_version >= "3.7.0" tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7" -tqdm==4.62.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" +tqdm==4.63.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" tradingview-ta==3.2.10; python_version >= "3.6" traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8" -types-python-dateutil==2.8.9 -types-pytz==2021.3.5 -types-pyyaml==6.0.4 -types-requests==2.27.11 -types-setuptools==57.4.9 -types-urllib3==1.26.9 +trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0" +trio==0.20.0; python_version >= "3.7" and python_version < "4.0" +types-python-dateutil==2.8.10 +types-pytz==2021.3.6 +types-pyyaml==6.0.5 +types-requests==2.27.14 +types-setuptools==57.4.11 +types-urllib3==1.26.11 typing-extensions==4.1.1; python_version < "3.10" and python_full_version >= "3.6.2" and python_version >= "3.6" -tzdata==2021.5; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") +tzdata==2022.1; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") tzlocal==4.1; python_version >= "3.6" ujson==5.1.0; python_version >= "3.7" -unidecode==1.3.3; python_version >= "3.7" +undetected-chromedriver==3.1.5.post4 +unidecode==1.3.4; python_version >= "3.7" update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0" -urllib3==1.26.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") +urllib3==1.26.9; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.7") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.6") user-agent==0.1.10 -uvicorn==0.17.5; python_version >= "3.7" +uvicorn==0.17.6; python_version >= "3.7" vadersentiment==3.3.2 valinvest==0.0.2; python_version >= "3.6" vcrpy==4.1.1; python_version >= "3.5" -virtualenv==20.13.1; python_full_version >= "3.6.1" -voila==0.3.2; python_version >= "3.7" +virtualenv==20.13.4; python_full_version >= "3.6.1" +voila==0.3.4; python_version >= "3.7" wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" webencodings==0.5.1; python_version >= "3.7" -websocket-client==1.2.3; python_version >= "3.8" and python_version < "4.0" +websocket-client==1.3.1; python_version >= "3.8" and python_version < "4.0" websockets==9.1; python_full_version >= "3.6.1" and python_version >= "3.7" -widgetsnbextension==3.5.2 +widgetsnbextension==3.6.0 wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") +wsproto==1.1.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" yarl==1.7.2; python_version >= "3.6" and python_full_version >= "3.8.0" yfinance==0.1.70 zope.interface==5.4.0; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5" diff --git a/bots/run_discordbot.py b/bots/run_discordbot.py old mode 100644 new mode 100755 index aff75137e43..7f357217457 --- a/bots/run_discordbot.py +++ b/bots/run_discordbot.py @@ -13,7 +13,7 @@ import disnake from disnake.ext import commands from fastapi import FastAPI, Request -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from bots.groupme.run_groupme import handle_groupme from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.loggers import setup_logging @@ -24,7 +24,6 @@ logger.info("START") logger.info("Python: %s", platform.python_version()) logger.info("OS: %s", platform.system()) - app = FastAPI() @@ -334,6 +333,37 @@ async def support(inter: disnake.CommandInteraction): await inter.response.send_modal(modal=MyModal()) +@gst_bot.slash_command() +async def stats( + self, + inter: disnake.AppCmdInter, +): + """Bot Stats""" + guildname = [] + for guild in gst_bot.guilds: + guildname.append(guild.name) + members = [] + for guild in gst_bot.guilds: + for member in guild.members: + members.append(member) + embed = disnake.Embed( + title="Bot Stats", + colour=cfg.COLOR, + ) + embed.add_field( + name="Servers", + value=f"```css\n{len(guildname):^20}\n```", + inline=False, + ) + embed.add_field( + name="Users", + value=f"```css\n{len(members):^20}\n```", + inline=False, + ) + + await inter.send(embed=embed) + + async def run(): try: await gst_bot.start(cfg.DISCORD_BOT_TOKEN) diff --git a/bots/slack/run_slack.py b/bots/slack/run_slack.py index bc5ea03a8de..0a2b866334f 100644 --- a/bots/slack/run_slack.py +++ b/bots/slack/run_slack.py @@ -4,9 +4,9 @@ import os from dotenv import load_dotenv from slack_bolt import App from slack_bolt.adapter.socket_mode import SocketModeHandler -from bots.common.helpers import non_slash from bots.common.commands_dict import commands +from bots.common.helpers import non_slash from bots.helpers import ShowView load_dotenv() diff --git a/bots/stocks/__init__.py b/bots/stocks/__init__.py old mode 100644 new mode 100755 index 4464b672971..d5290c02c2b --- a/bots/stocks/__init__.py +++ b/bots/stocks/__init__.py @@ -1,9 +1,13 @@ # flake8: noqa +# pylint: disable=unused-import from . import ( dark_pool_shorts, + disc, due_diligence, government, + insider, options, screener, + sia, technical_analysis, ) diff --git a/bots/stocks/candle.py b/bots/stocks/candle.py old mode 100644 new mode 100755 index 382e2b3f0ea..9aad8751880 --- a/bots/stocks/candle.py +++ b/bots/stocks/candle.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import overlap_model from gamestonk_terminal.decorators import log_start_end @@ -67,7 +66,7 @@ def candle_command( # Check that loading a stock was not successful if df_stock.empty: - return Exception(f"No data found for {ticker.upper()}.") + raise Exception(f"No data found for {ticker.upper()}.") # Output Data if interval != 1440: @@ -100,7 +99,7 @@ def candle_command( fig.update_layout( margin=dict(l=0, r=0, t=40, b=20), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, title=title, title_x=0.5, title_font_size=14, @@ -109,14 +108,14 @@ def candle_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=True) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=True) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": title, diff --git a/bots/stocks/dark_pool_shorts/dpotc.py b/bots/stocks/dark_pool_shorts/dpotc.py index 9fe25cfe38b..b65fb25573b 100644 --- a/bots/stocks/dark_pool_shorts/dpotc.py +++ b/bots/stocks/dark_pool_shorts/dpotc.py @@ -4,8 +4,7 @@ import plotly.graph_objects as go import yfinance as yf from plotly.subplots import make_subplots -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import finra_model @@ -17,7 +16,7 @@ def dpotc_command(ticker: str = ""): """Dark pools (ATS) vs OTC data [FINRA]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps dpotc %s", ticker) # Check for argument @@ -34,7 +33,7 @@ def dpotc_command(ticker: str = ""): ats, otc = finra_model.getTickerFINRAdata(ticker) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(ats.to_string()) logger.debug(otc.to_string()) @@ -146,20 +145,20 @@ def dpotc_command(ticker: str = ""): row=2, col=1, ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_xaxes(showspikes=True, spikesnap="cursor", spikemode="across") fig.update_yaxes(showspikes=True, spikethickness=2) fig.update_layout( margin=dict(l=20, r=0, t=10, b=20), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=f"Dark Pools (ATS) vs OTC (Non-ATS) Data for {ticker}", title_x=0.5, yaxis3_title="Shares per Trade", yaxis_title="Total Weekly Shares", xaxis2_title="Weeks", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( fixedrange=False, side="left", @@ -196,15 +195,15 @@ def dpotc_command(ticker: str = ""): # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": title, diff --git a/bots/stocks/dark_pool_shorts/ftd.py b/bots/stocks/dark_pool_shorts/ftd.py index cea59717d8c..fc9a4a19152 100644 --- a/bots/stocks/dark_pool_shorts/ftd.py +++ b/bots/stocks/dark_pool_shorts/ftd.py @@ -4,8 +4,7 @@ from datetime import datetime, timedelta import plotly.graph_objects as go from plotly.subplots import make_subplots -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import sec_model @@ -17,7 +16,7 @@ def ftd_command(ticker: str = "", start="", end=""): """Fails-to-deliver data [SEC]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps ftd %s %s %s", ticker, start, end) # Check for argument @@ -29,21 +28,21 @@ def ftd_command(ticker: str = "", start="", end=""): if start == "": start = datetime.now() - timedelta(days=365) else: - start = datetime.strptime(start, cfg.DATE_FORMAT) + start = datetime.strptime(start, imps.DATE_FORMAT) if end == "": end = datetime.now() else: - end = datetime.strptime(end, cfg.DATE_FORMAT) + end = datetime.strptime(end, imps.DATE_FORMAT) # Retrieve data ftds_data = sec_model.get_fails_to_deliver(ticker, start, end, 0) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(ftds_data.to_string()) - stock = helpers.load(ticker, start) + stock = imps.load(ticker, start) stock_ftd = stock[stock.index > start] stock_ftd = stock_ftd[stock_ftd.index < end] @@ -76,16 +75,16 @@ def ftd_command(ticker: str = "", start="", end=""): ), secondary_y=False, ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( margin=dict(l=0, r=20, t=30, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=f"{ticker} Failed-to-deliver", title_x=0.5, yaxis2_title="Stock Price", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis2=dict( side="left", fixedrange=False, @@ -130,15 +129,15 @@ def ftd_command(ticker: str = "", start="", end=""): # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: [SEC] Failure-to-deliver {ticker}", diff --git a/bots/stocks/dark_pool_shorts/hsi.py b/bots/stocks/dark_pool_shorts/hsi.py index 4004fb2a2d0..6740c1cca2a 100644 --- a/bots/stocks/dark_pool_shorts/hsi.py +++ b/bots/stocks/dark_pool_shorts/hsi.py @@ -2,8 +2,7 @@ import logging import disnake -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import shortinterest_model @@ -15,7 +14,7 @@ def hsi_command(num: int = 10): """Show top high short interest stocks of over 20% ratio [shortinterest.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps hsi %s", num) # Check for argument @@ -27,7 +26,7 @@ def hsi_command(num: int = 10): df = df.iloc[1:].head(n=num) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data @@ -56,10 +55,10 @@ def hsi_command(num: int = 10): disnake.Embed( title=title, description=initial_str, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) for column in df.columns.values: @@ -68,16 +67,16 @@ def hsi_command(num: int = 10): disnake.Embed( title=title, description=description, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") return { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/dark_pool_shorts/pos.py b/bots/stocks/dark_pool_shorts/pos.py index 31ecd6791bc..8af12ce4c08 100644 --- a/bots/stocks/dark_pool_shorts/pos.py +++ b/bots/stocks/dark_pool_shorts/pos.py @@ -1,13 +1,9 @@ import logging import os -import df2img import disnake -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import stockgrid_model @@ -19,7 +15,7 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): """Dark pool short position [Stockgrid]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps-pos %s %s", sort, num) # Check for argument @@ -40,7 +36,7 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): df = df.iloc[:num] # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data @@ -82,26 +78,26 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (40 * dindex)), + fig_size=(900, (40 * dindex)), col_width=[2, 3, 4, 3.5, 5, 4, 5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "left"]))) imagefile = "dps-pos.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -113,7 +109,7 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -123,13 +119,13 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -143,7 +139,7 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -151,18 +147,18 @@ def pos_command(sort="dpp_dollar", ascending: bool = False, num: int = 10): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(1400, (40 * dindex)), + fig_size=(900, (40 * dindex)), col_width=[2, 3, 4, 3.5, 5, 4, 5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = helpers.save_image("dps-pos.png", fig) + imagefile = imps.save_image("dps-pos.png", fig) output = { "title": title, diff --git a/bots/stocks/dark_pool_shorts/psi.py b/bots/stocks/dark_pool_shorts/psi.py index 9282b74aa76..a2389f9cc19 100644 --- a/bots/stocks/dark_pool_shorts/psi.py +++ b/bots/stocks/dark_pool_shorts/psi.py @@ -4,8 +4,7 @@ import plotly.graph_objects as go import yfinance as yf from plotly.subplots import make_subplots -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import stockgrid_model @@ -17,7 +16,7 @@ def psi_command(ticker: str = ""): """Price vs short interest volume [Stockgrid]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps psi %s", ticker) # Check for argument @@ -34,7 +33,7 @@ def psi_command(ticker: str = ""): df, prices = stockgrid_model.get_short_interest_volume(ticker) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data @@ -94,21 +93,21 @@ def psi_command(ticker: str = ""): col=1, secondary_y=False, ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_traces(hovertemplate="%{y:.2f}") fig.update_xaxes(showspikes=True, spikesnap="cursor", spikemode="across") fig.update_yaxes(showspikes=True, spikethickness=2) fig.update_layout( margin=dict(l=10, r=0, t=40, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=f"Price vs Short Volume Interest for {ticker}", title_x=0.45, yaxis_title="Stock Price ($)", yaxis2_title="FINRA Volume [M]", yaxis3_title="Short Vol. %", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( side="right", fixedrange=False, @@ -146,15 +145,15 @@ def psi_command(ticker: str = ""): # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: [Stockgrid] Price vs Short Interest Volume {ticker}", diff --git a/bots/stocks/dark_pool_shorts/shorted.py b/bots/stocks/dark_pool_shorts/shorted.py index 75be3f9aa74..c56092e2684 100644 --- a/bots/stocks/dark_pool_shorts/shorted.py +++ b/bots/stocks/dark_pool_shorts/shorted.py @@ -1,13 +1,9 @@ import logging import os -import df2img import disnake -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import yahoofinance_model @@ -19,7 +15,7 @@ def shorted_command(num: int = 10): """Show most shorted stocks [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps shorted %s", num) # Check for argument @@ -31,7 +27,7 @@ def shorted_command(num: int = 10): if df.empty: raise Exception("No available data found") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data @@ -39,6 +35,8 @@ def shorted_command(num: int = 10): df.dropna(how="all", axis=1, inplace=True) df = df.replace(float("NaN"), "") df = df.drop(columns=["PE Ratio (TTM)"]) + df.set_index("Symbol", inplace=True) + dindex = len(df.index) if dindex > 15: embeds: list = [] @@ -48,26 +46,26 @@ def shorted_command(num: int = 10): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (45 * dindex)), + fig_size=(900, (45 * dindex)), col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "left"]))) imagefile = "etf-holdings.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -79,7 +77,7 @@ def shorted_command(num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -89,13 +87,13 @@ def shorted_command(num: int = 10): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -109,7 +107,7 @@ def shorted_command(num: int = 10): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -117,18 +115,18 @@ def shorted_command(num: int = 10): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(1400, (45 * dindex)), + fig_size=(900, (45 * dindex)), col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["left", "center"]))) - imagefile = helpers.save_image("etf-holdings.png", fig) + imagefile = imps.save_image("etf-holdings.png", fig) output = { "title": title, diff --git a/bots/stocks/dark_pool_shorts/sidtc.py b/bots/stocks/dark_pool_shorts/sidtc.py index 3202643736f..31a2ea53c49 100644 --- a/bots/stocks/dark_pool_shorts/sidtc.py +++ b/bots/stocks/dark_pool_shorts/sidtc.py @@ -2,8 +2,7 @@ import logging import disnake -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import stockgrid_model @@ -15,7 +14,7 @@ def sidtc_command(sort="float", num: int = 10): """Short interest and days to cover [Stockgrid]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps-sidtc %s %s", sort, num) # Check for argument @@ -32,7 +31,7 @@ def sidtc_command(sort="float", num: int = 10): df = df.iloc[:num] # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data @@ -73,10 +72,10 @@ def sidtc_command(sort="float", num: int = 10): reports = [f"{initial_str}"] embeds.append( disnake.Embed( - title="Dark Pool Shorts", description=initial_str, colour=cfg.COLOR + title="Dark Pool Shorts", description=initial_str, colour=imps.COLOR ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) @@ -92,16 +91,16 @@ def sidtc_command(sort="float", num: int = 10): disnake.Embed( title=title, description=description, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") return { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/dark_pool_shorts/spos.py b/bots/stocks/dark_pool_shorts/spos.py index b27967ef75e..f0c89e864fd 100644 --- a/bots/stocks/dark_pool_shorts/spos.py +++ b/bots/stocks/dark_pool_shorts/spos.py @@ -4,8 +4,7 @@ import plotly.graph_objects as go import yfinance as yf from plotly.subplots import make_subplots -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.dark_pool_shorts import stockgrid_model @@ -17,7 +16,7 @@ def spos_command(ticker: str = ""): """Net short vs position [Stockgrid]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dps-spos %s", ticker) # Check for argument @@ -34,7 +33,7 @@ def spos_command(ticker: str = ""): df = stockgrid_model.get_net_short_position(ticker) # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data @@ -63,8 +62,8 @@ def spos_command(ticker: str = ""): ), secondary_y=False, ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) # Set y-axes titles fig.update_xaxes(dtick="M1", tickformat="%b %d\n%Y") @@ -72,12 +71,12 @@ def spos_command(ticker: str = ""): fig.update_traces(hovertemplate="%{y:.2s}") fig.update_layout( margin=dict(l=0, r=10, t=40, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=f"Net Short Vol. vs Position for {ticker}", title_x=0.5, yaxis_title="Net Short Vol. ($)", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( side="left", showgrid=False, @@ -114,15 +113,15 @@ def spos_command(ticker: str = ""): # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": title, diff --git a/bots/stocks/disc/__init__.py b/bots/stocks/disc/__init__.py new file mode 100755 index 00000000000..e69de29bb2d diff --git a/bots/stocks/disc/active.py b/bots/stocks/disc/active.py index e8d1c3f0fc1..0e822d0184d 100644 --- a/bots/stocks/disc/active.py +++ b/bots/stocks/disc/active.py @@ -1,14 +1,12 @@ import logging import os -import df2img import disnake +import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import lambda_long_number_format from gamestonk_terminal.stocks.discovery import yahoofinance_model logger = logging.getLogger(__name__) @@ -19,7 +17,7 @@ def active_command(num: int = 10): """Show most active [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("disc active %s", num) # Check for argument @@ -33,25 +31,43 @@ def active_command(num: int = 10): raise Exception("No available data found") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data title = "Stocks: Most Active [Yahoo Finance]" df.dropna(how="all", axis=1, inplace=True) df = df.replace(float("NaN"), "") - df = df.drop(columns=["PE Ratio (TTM)"]) + # Convert and format: str into float + for col in ["Volume", "Avg Vol (3 month)"]: + df[col] = [imps.unit_finder.sub(imps.unit_replacer, x) for x in df[col]] + df[col] = pd.to_numeric(df[col].astype(float)) + df[col] = df[col].map(lambda x: lambda_long_number_format(x, 2)) + + for col in ["Price (Intraday)", "Change"]: + df[col] = df[col].apply(lambda x: f"${x:.2f}") + + # Format "%% Change" columns then combine it into "Change" + df["% Change"] = df.apply(lambda x: f"({x['% Change']})", axis=1) + df["Change"] = df.apply(lambda x: f"{x['Change']} {x['% Change']}", axis=1) + + # Combine "Volume" columns + df["Volume"] = df.apply( + lambda x: f"{x['Volume']:>8} ({x['Avg Vol (3 month)']:>8})", + axis=1, + ) + + df = df.drop(columns=["PE Ratio (TTM)", "% Change", "Avg Vol (3 month)"]) df.set_index("Symbol", inplace=True) df.columns = [ "Name", "Price", "Change", - "% Change", - "Volume", - "Avg Vol", - "Market Cap", + "Volume (Avg)", + "Mkt Cap", ] + dindex = len(df.index) if dindex > 15: embeds: list = [] @@ -61,26 +77,26 @@ def active_command(num: int = 10): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) imagefile = "disc-active.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -92,7 +108,7 @@ def active_command(num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -102,13 +118,13 @@ def active_command(num: int = 10): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -122,7 +138,7 @@ def active_command(num: int = 10): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -130,18 +146,18 @@ def active_command(num: int = 10): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = helpers.save_image("disc-active.png", fig) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) + imagefile = imps.save_image("disc-active.png", fig) output = { "title": title, diff --git a/bots/stocks/disc/ford.py b/bots/stocks/disc/ford.py old mode 100644 new mode 100755 index 3d3daae6ba4..9a5caaced6c --- a/bots/stocks/disc/ford.py +++ b/bots/stocks/disc/ford.py @@ -1,13 +1,9 @@ import logging import os -import df2img import disnake -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.discovery import fidelity_model @@ -19,9 +15,9 @@ def ford_command(): """Display Orders by Fidelity Customers. [Source: Fidelity]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("disc ford") - order_header, df_orders = fidelity_model.get_orders() + order_header, df_orders = fidelity_model.get_orders() # pylint: disable=W0612 df_orders = df_orders.head(n=30).iloc[:, :-1] df_orders = df_orders.applymap(str) @@ -36,6 +32,9 @@ def ford_command(): ] + [["white"] * 3] ) + df_orders = df_orders.rename( + columns={"# Buy Orders": "# Buy's", "# Sell Orders": "# Sell's"} + ) df_orders.set_index("Symbol", inplace=True) df_orders = df_orders.apply(lambda x: x.str.slice(0, 30)) @@ -49,33 +48,35 @@ def ford_command(): while i < dindex: df_pg = df_orders.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (40 + (40 * dindex))), - col_width=[1, 2.3, 2.2, 4, 2, 2], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(900, (40 + (40 * dindex))), + col_width=[1, 2.4, 2.35, 4, 1.5, 1.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( cells=( dict( - align=["center"], + align=["center", "center", "center", "center", "right"], font=dict(color=font_color), ) ) ) imagefile = "disc-ford.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link = uploaded_image.link os.remove(imagefile_save) @@ -85,7 +86,7 @@ def ford_command(): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -95,13 +96,13 @@ def ford_command(): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -115,7 +116,7 @@ def ford_command(): ] return { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, diff --git a/bots/stocks/disc/topgainers.py b/bots/stocks/disc/topgainers.py old mode 100644 new mode 100755 index c6b7941ef75..b3ca1bff522 --- a/bots/stocks/disc/topgainers.py +++ b/bots/stocks/disc/topgainers.py @@ -1,14 +1,12 @@ import logging import os -import df2img import disnake +import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import lambda_long_number_format from gamestonk_terminal.stocks.discovery import yahoofinance_model logger = logging.getLogger(__name__) @@ -19,7 +17,7 @@ def gainers_command(num: int = 10): """Show top gainers [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("disc gainers %s", num) # Check for argument @@ -33,25 +31,43 @@ def gainers_command(num: int = 10): raise Exception("No available data found") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data title = "Stocks: Top Gainers [Yahoo Finance]" df.dropna(how="all", axis=1, inplace=True) df = df.replace(float("NaN"), "") - df = df.drop(columns=["PE Ratio (TTM)"]) + # Convert and format: str into float + for col in ["Volume", "Avg Vol (3 month)"]: + df[col] = [imps.unit_finder.sub(imps.unit_replacer, x) for x in df[col]] + df[col] = pd.to_numeric(df[col].astype(float)) + df[col] = df[col].map(lambda x: lambda_long_number_format(x, 2)) + + for col in ["Price (Intraday)", "Change"]: + df[col] = df[col].apply(lambda x: f"${x:.2f}") + + # Format "%% Change" columns then combine it into "Change" + df["% Change"] = df.apply(lambda x: f"({x['% Change']})", axis=1) + df["Change"] = df.apply(lambda x: f"{x['Change']} {x['% Change']}", axis=1) + + # Combine "Volume" columns + df["Volume"] = df.apply( + lambda x: f"{x['Volume']:>8} ({x['Avg Vol (3 month)']:>8})", + axis=1, + ) + + df = df.drop(columns=["PE Ratio (TTM)", "% Change", "Avg Vol (3 month)"]) df.set_index("Symbol", inplace=True) df.columns = [ "Name", "Price", "Change", - "% Change", - "Volume", - "Avg Vol", - "Market Cap", + "Volume (Avg)", + "Mkt Cap", ] + dindex = len(df.index) if dindex > 15: embeds: list = [] @@ -61,26 +77,26 @@ def gainers_command(num: int = 10): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) imagefile = "disc-gainers.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -92,7 +108,7 @@ def gainers_command(num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -102,13 +118,13 @@ def gainers_command(num: int = 10): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -122,7 +138,7 @@ def gainers_command(num: int = 10): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -130,18 +146,18 @@ def gainers_command(num: int = 10): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = helpers.save_image("disc-gainers.png", fig) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) + imagefile = imps.save_image("disc-gainers.png", fig) output = { "title": title, diff --git a/bots/stocks/disc/toplosers.py b/bots/stocks/disc/toplosers.py old mode 100644 new mode 100755 index 90e56a5e760..1d03b31cc51 --- a/bots/stocks/disc/toplosers.py +++ b/bots/stocks/disc/toplosers.py @@ -1,14 +1,12 @@ import logging import os -import df2img import disnake +import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import lambda_long_number_format from gamestonk_terminal.stocks.discovery import yahoofinance_model logger = logging.getLogger(__name__) @@ -19,7 +17,7 @@ def losers_command(num: int = 10): """Show top losers [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("disc losers %s", num) # Check for argument @@ -33,25 +31,43 @@ def losers_command(num: int = 10): raise Exception("No available data found") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data title = "Stocks: Top Losers [Yahoo Finance]" df.dropna(how="all", axis=1, inplace=True) df = df.replace(float("NaN"), "") - df = df.drop(columns=["PE Ratio (TTM)"]) + # Convert and format: str into float + for col in ["Volume", "Avg Vol (3 month)"]: + df[col] = [imps.unit_finder.sub(imps.unit_replacer, x) for x in df[col]] + df[col] = pd.to_numeric(df[col].astype(float)) + df[col] = df[col].map(lambda x: lambda_long_number_format(x, 2)) + + for col in ["Price (Intraday)", "Change"]: + df[col] = df[col].apply(lambda x: f"${x:.2f}") + + # Format "%% Change" columns then combine it into "Change" + df["% Change"] = df.apply(lambda x: f"({x['% Change']})", axis=1) + df["Change"] = df.apply(lambda x: f"{x['Change']} {x['% Change']}", axis=1) + + # Combine "Volume" columns + df["Volume"] = df.apply( + lambda x: f"{x['Volume']:>8} ({x['Avg Vol (3 month)']:>8})", + axis=1, + ) + + df = df.drop(columns=["PE Ratio (TTM)", "% Change", "Avg Vol (3 month)"]) df.set_index("Symbol", inplace=True) df.columns = [ "Name", "Price", "Change", - "% Change", - "Volume", - "Avg Vol", - "Market Cap", + "Volume (Avg)", + "Mkt Cap", ] + dindex = len(df.index) if dindex > 15: embeds: list = [] @@ -61,26 +77,26 @@ def losers_command(num: int = 10): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) imagefile = "disc-losers.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -92,7 +108,7 @@ def losers_command(num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -102,13 +118,13 @@ def losers_command(num: int = 10): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -122,7 +138,7 @@ def losers_command(num: int = 10): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -130,18 +146,18 @@ def losers_command(num: int = 10): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = helpers.save_image("disc-losers.png", fig) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) + imagefile = imps.save_image("disc-losers.png", fig) output = { "title": title, diff --git a/bots/stocks/disc/ugs.py b/bots/stocks/disc/ugs.py old mode 100644 new mode 100755 index e25209fdca4..b0e1412dc5a --- a/bots/stocks/disc/ugs.py +++ b/bots/stocks/disc/ugs.py @@ -1,14 +1,12 @@ import logging import os -import df2img import disnake +import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import lambda_long_number_format from gamestonk_terminal.stocks.discovery import yahoofinance_model logger = logging.getLogger(__name__) @@ -19,7 +17,7 @@ def ugs_command(num: int = 10): """Show most undervalued growth stock. [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("disc ugs %s", num) # Check for argument @@ -33,25 +31,43 @@ def ugs_command(num: int = 10): raise Exception("No available data found") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df.to_string()) # Output data title = "Stocks: Most Undervalued Growth Stock [Yahoo Finance]" df.dropna(how="all", axis=1, inplace=True) df = df.replace(float("NaN"), "") - df = df.drop(columns=["PE Ratio (TTM)"]) + # Convert and format: str into float + for col in ["Volume", "Avg Vol (3 month)"]: + df[col] = [imps.unit_finder.sub(imps.unit_replacer, x) for x in df[col]] + df[col] = pd.to_numeric(df[col].astype(float)) + df[col] = df[col].map(lambda x: lambda_long_number_format(x, 2)) + + for col in ["Price (Intraday)", "Change"]: + df[col] = df[col].apply(lambda x: f"${x:.2f}") + + # Format "%% Change" columns then combine it into "Change" + df["% Change"] = df.apply(lambda x: f"({x['% Change']})", axis=1) + df["Change"] = df.apply(lambda x: f"{x['Change']} {x['% Change']}", axis=1) + + # Combine "Volume" columns + df["Volume"] = df.apply( + lambda x: f"{x['Volume']:>8} ({x['Avg Vol (3 month)']:>8})", + axis=1, + ) + + df = df.drop(columns=["PE Ratio (TTM)", "% Change", "Avg Vol (3 month)"]) df.set_index("Symbol", inplace=True) df.columns = [ "Name", "Price", "Change", - "% Change", - "Volume", - "Avg Vol", - "Market Cap", + "Volume (Avg)", + "Mkt Cap", ] + dindex = len(df.index) if dindex > 15: embeds: list = [] @@ -61,26 +77,26 @@ def ugs_command(num: int = 10): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) imagefile = "disc-ugs.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -92,7 +108,7 @@ def ugs_command(num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -102,13 +118,13 @@ def ugs_command(num: int = 10): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -122,7 +138,7 @@ def ugs_command(num: int = 10): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -130,18 +146,18 @@ def ugs_command(num: int = 10): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(1400, (45 * dindex)), - col_width=[2, 9, 2.5, 2.5, 2.5, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(950, (45 * dindex)), + col_width=[2.1, 10, 2.5, 5, 5.2, 3], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = helpers.save_image("disc-ugs.png", fig) + fig.update_traces(cells=(dict(align=["center", "center", "right"]))) + imagefile = imps.save_image("disc-ugs.png", fig) output = { "title": title, diff --git a/bots/stocks/disc/upcoming.py b/bots/stocks/disc/upcoming.py index 4cee9220592..ec6a2f67c12 100644 --- a/bots/stocks/disc/upcoming.py +++ b/bots/stocks/disc/upcoming.py @@ -1,25 +1,22 @@ import logging import os -import df2img import disnake -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.discovery import seeking_alpha_model logger = logging.getLogger(__name__) +# pylint: disable=W0612,W0631 @log_start_end(log=logger) def earnings_command(): """Display Upcoming Earnings. [Source: Seeking Alpha]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("earnings") df_earnings = seeking_alpha_model.get_next_earnings(2) @@ -43,26 +40,26 @@ def earnings_command(): while i < dindex: df_pg = df_earn.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, fig_size=(800, (40 + (40 * dindex))), col_width=[1, 5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "left"]))) imagefile = "disc-upcoming.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -74,7 +71,7 @@ def earnings_command(): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -84,13 +81,13 @@ def earnings_command(): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -104,7 +101,7 @@ def earnings_command(): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -112,18 +109,18 @@ def earnings_command(): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_earn, fig_size=(800, (40 + (40 * dindex))), col_width=[1, 5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = helpers.save_image("disc-upcoming.png", fig) + imagefile = imps.save_image("disc-upcoming.png", fig) output = { "title": title, diff --git a/bots/stocks/due_diligence/analyst.py b/bots/stocks/due_diligence/analyst.py index ffbb3cdeaaf..9d0c5d27701 100644 --- a/bots/stocks/due_diligence/analyst.py +++ b/bots/stocks/due_diligence/analyst.py @@ -1,10 +1,8 @@ import logging -import df2img import numpy as np -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.due_diligence import finviz_model @@ -16,7 +14,7 @@ def analyst_command(ticker=""): """Displays analyst recommendations [Finviz]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dd analyst %s", ticker) # Check for argument @@ -38,17 +36,17 @@ def analyst_command(ticker=""): ) dindex = len(df.index) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, fig_size=(900, (40 + (40 * dindex))), col_width=[5, 5, 9, 8, 5, 6, 5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - imagefile = save_image("dd-analyst.png", fig) + imagefile = imps.save_image("dd-analyst.png", fig) return { "title": f"Stocks: [Finviz] Analyst Recommendations {ticker.upper()}", diff --git a/bots/stocks/due_diligence/arktrades.py b/bots/stocks/due_diligence/arktrades.py index eed0337725d..c3c53d813bf 100644 --- a/bots/stocks/due_diligence/arktrades.py +++ b/bots/stocks/due_diligence/arktrades.py @@ -1,14 +1,10 @@ import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots.config_discordbot import gst_imgur -from bots.helpers import save_image -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.due_diligence import ark_model @@ -16,11 +12,11 @@ logger = logging.getLogger(__name__) @log_start_end(log=logger) -def arktrades_command(ticker: str = "", num: int = 10): +def arktrades_command(ticker: str = "", num: int = 30): """Displays trades made by ark [cathiesark.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dd arktrades %s", ticker) if ticker: @@ -59,30 +55,32 @@ def arktrades_command(ticker: str = "", num: int = 10): embeds: list = [] - i, i2, end = 0, 0, 20 + i, i2, end = 0, 0, 15 df_pg, embeds_img, images_list = [], [], [] while i < len(df.index): df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, fig_size=(900, (40 + (40 * 20))), col_width=[5, 8, 4, 4, 3, 5, 5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - imagefile = save_image("dd-arktrades.png", fig) + imagefile = imps.save_image("dd-arktrades.png", fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link = uploaded_image.link os.remove(imagefile_save) @@ -92,23 +90,23 @@ def arktrades_command(ticker: str = "", num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 - i += 20 - end += 20 + i += 15 + end += 15 # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -122,7 +120,7 @@ def arktrades_command(ticker: str = "", num: int = 10): ] return { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, diff --git a/bots/stocks/due_diligence/borrowed.py b/bots/stocks/due_diligence/borrowed.py new file mode 100755 index 00000000000..9416406e3e8 --- /dev/null +++ b/bots/stocks/due_diligence/borrowed.py @@ -0,0 +1,149 @@ +import logging +import os + +import disnake +import pandas as pd +import requests + +from bots import imps +from gamestonk_terminal.decorators import log_start_end + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def borrowed_command(ticker: str = ""): + """Displays borrowed shares available and fee [Stocksera.com]""" + + # Debug user input + if imps.DEBUG: + logger.debug("dd borrowed %s", ticker) + + # Check for argument + if ticker == "": + raise Exception("Stock ticker is required") + + df = pd.DataFrame( + requests.get( + f"https://stocksera.pythonanywhere.com/api/borrowed_shares/{ticker}" + ).json() + ) + + if df.empty: + raise Exception("No Data Found") + + title = f"{ticker.upper()} Shares Available to Borrow [Stocksera]" + + df = df.head(1000) + df["changed"] = df["available"].astype(int).diff() + df = df[df["changed"] != 0.0] + df = df.drop(columns="changed") + + formats = {"fee": "{:.2f}%", "available": "{:,}"} + for col, value in formats.items(): + df[col] = df[col].map(lambda x: value.format(x)) # pylint: disable=W0640 + + df = df.rename(columns={"ticker": " ", "date_updated": "Updated"}) + df.set_index(" ", inplace=True) + df.columns = df.columns.str.capitalize() + + dindex = len(df.index) + if dindex > 15: + embeds: list = [] + # Output + i, i2, end = 0, 0, 15 + df_pg, embeds_img, images_list = pd.DataFrame(), [], [] + while i < dindex: + df_pg = df.iloc[i:end] + df_pg.append(df_pg) + fig = imps.plot_df( + df_pg, + fig_size=(550, (40 + (40 * len(df.index)))), + col_width=[1, 1, 1.5, 2], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces( + cells=(dict(align=["center", "right", "right", "center"])) + ) + imagefile = "dd_borrowed.png" + imagefile = imps.save_image(imagefile, fig) + + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile + images_list.append(imagefile) + else: + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) + image_link = uploaded_image.link + os.remove(imagefile_save) + + embeds_img.append( + f"{image_link}", + ) + embeds.append( + disnake.Embed( + title=title, + colour=imps.COLOR, + ), + ) + i2 += 1 + i += 15 + end += 15 + + # Author/Footer + for i in range(0, i2): + embeds[i].set_author( + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, + ) + embeds[i].set_footer( + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, + ) + + i = 0 + for i in range(0, i2): + embeds[i].set_image(url=embeds_img[i]) + + i += 1 + embeds[0].set_footer(text=f"Page 1 of {len(embeds)}") + choices = [ + disnake.SelectOption(label="Home", value="0", emoji="🟢"), + ] + + output = { + "view": imps.Menu, + "title": title, + "embed": embeds, + "choices": choices, + "embeds_img": embeds_img, + "images_list": images_list, + } + else: + fig = imps.plot_df( + df, + fig_size=(550, (40 + (40 * len(df.index)))), + col_width=[1, 1, 1.5, 2], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, + paper_bgcolor="rgba(0, 0, 0, 0)", + ) + fig.update_traces(cells=(dict(align=["center", "right", "right", "center"]))) + imagefile = "dd_borrowed.png" + imagefile = imps.save_image(imagefile, fig) + + output = { + "title": title, + "imagefile": imagefile, + } + + return output diff --git a/bots/stocks/due_diligence/customer.py b/bots/stocks/due_diligence/customer.py index ce92b3a735b..fec44e837f7 100644 --- a/bots/stocks/due_diligence/customer.py +++ b/bots/stocks/due_diligence/customer.py @@ -4,8 +4,7 @@ import disnake import requests from bs4 import BeautifulSoup -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end logger = logging.getLogger(__name__) @@ -16,7 +15,7 @@ def customer_command(ticker=""): """Displays customers of the company [CSIMarket]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dd customer %s", ticker) if not ticker: @@ -35,7 +34,7 @@ def customer_command(ticker=""): raise Exception("No customers found.") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(l_customer) customers, unique = [], [] @@ -72,10 +71,10 @@ def customer_command(ticker=""): disnake.Embed( title=title, description=customers, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") @@ -94,10 +93,10 @@ def customer_command(ticker=""): disnake.Embed( title=title, description=description, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) i += 60 @@ -106,7 +105,7 @@ def customer_command(ticker=""): # Output data output = { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/due_diligence/est.py b/bots/stocks/due_diligence/est.py index 24755aa1724..b82d7092906 100644 --- a/bots/stocks/due_diligence/est.py +++ b/bots/stocks/due_diligence/est.py @@ -1,13 +1,9 @@ import logging import os -import df2img import disnake -import bots.config_discordbot as cfg -from bots.config_discordbot import gst_imgur -from bots.helpers import save_image -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.due_diligence import business_insider_model @@ -19,7 +15,7 @@ def est_command(ticker: str = ""): """Displays earning estimates [Business Insider]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dd est %s", ticker) # Check for argument @@ -40,88 +36,88 @@ def est_command(ticker: str = ""): raise Exception("Enter a valid ticker") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(df_year_estimates.to_string()) logger.debug(df_quarter_earnings.to_string()) logger.debug(df_quarter_revenues.to_string()) images_list = [] dindex = len(df_year_estimates.index) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_year_estimates, fig_size=(900, (40 + (60 * dindex))), col_width=[9, 4, 4, 4, 4], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - imagefile = save_image("estimates.png", fig) + imagefile = imps.save_image("estimates.png", fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - link_estimates = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + link_estimates = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image(imagefile_save, title="something") link_estimates = uploaded_image.link os.remove(imagefile_save) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_quarter_earnings, fig_size=(900, (40 + (40 * 20))), col_width=[5, 5, 4, 4, 5, 4], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - imagefile = save_image("earnings.png", fig) + imagefile = imps.save_image("earnings.png", fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - link_earnings = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + link_earnings = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image(imagefile_save, title="something") link_earnings = uploaded_image.link os.remove(imagefile_save) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_quarter_revenues, fig_size=(900, (40 + (40 * 20))), col_width=[5, 5, 4, 4, 5, 4], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - imagefile = save_image("revenues.png", fig) + imagefile = imps.save_image("revenues.png", fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - link_revenues = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + link_revenues = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image(imagefile_save, title="something") link_revenues = uploaded_image.link os.remove(imagefile_save) embeds = [ disnake.Embed( title=f"**{ticker.upper()} Year Estimates**", - color=cfg.COLOR, + color=imps.COLOR, ), disnake.Embed( title=f"**{ticker.upper()} Quarter Earnings**", - colour=cfg.COLOR, + colour=imps.COLOR, ), disnake.Embed( title=f"**{ticker.upper()} Quarter Revenues**", - colour=cfg.COLOR, + colour=imps.COLOR, ), ] embeds[0].set_image(url=link_estimates) @@ -151,7 +147,7 @@ def est_command(ticker: str = ""): ] return { - "view": Menu, + "view": imps.Menu, "titles": titles, "embed": embeds, "choices": choices, diff --git a/bots/stocks/due_diligence/pt.py b/bots/stocks/due_diligence/pt.py index 81ffa489575..35efe7ca54f 100644 --- a/bots/stocks/due_diligence/pt.py +++ b/bots/stocks/due_diligence/pt.py @@ -4,8 +4,7 @@ from datetime import datetime, timedelta from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -import bots.helpers +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -19,7 +18,7 @@ def pt_command(ticker: str = "", raw: bool = False, start=""): """Displays price targets [Business Insider]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dd pt %s", ticker) # Check for argument @@ -29,13 +28,13 @@ def pt_command(ticker: str = "", raw: bool = False, start=""): if start == "": start = datetime.now() - timedelta(days=365) else: - start = datetime.strptime(start, cfg.DATE_FORMAT) + start = datetime.strptime(start, imps.DATE_FORMAT) if raw not in [True, False]: raise Exception("raw argument has to be true or false") df_analyst_data = business_insider_model.get_price_target_from_analysts(ticker) - stock = bots.helpers.load(ticker, start) + stock = imps.load(ticker, start) title = f"Stocks: [Business Insider] Price Targets {ticker}" if df_analyst_data.empty or stock.empty: raise Exception("Enter valid ticker") @@ -71,9 +70,10 @@ def pt_command(ticker: str = "", raw: bool = False, start=""): imagefile = "ta_pt.png" dataBytesIO = io.BytesIO() plt.savefig(dataBytesIO) + plt.close("all") dataBytesIO.seek(0) - imagefile = bots.helpers.image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) output = { "title": title, diff --git a/bots/stocks/due_diligence/sec.py b/bots/stocks/due_diligence/sec.py index bc8cd33567c..6dc81012346 100644 --- a/bots/stocks/due_diligence/sec.py +++ b/bots/stocks/due_diligence/sec.py @@ -1,6 +1,6 @@ import logging -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.due_diligence import marketwatch_model diff --git a/bots/stocks/due_diligence/supplier.py b/bots/stocks/due_diligence/supplier.py index c6542030975..e5d6940e519 100644 --- a/bots/stocks/due_diligence/supplier.py +++ b/bots/stocks/due_diligence/supplier.py @@ -4,8 +4,7 @@ import disnake import requests from bs4 import BeautifulSoup -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end logger = logging.getLogger(__name__) @@ -16,7 +15,7 @@ def supplier_command(ticker=""): """Displays suppliers of the company [CSIMarket]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("dd supplier %s", ticker) if not ticker: @@ -37,7 +36,7 @@ def supplier_command(ticker=""): raise Exception("No suppliers found.\n") # Debug user output - if cfg.DEBUG: + if imps.DEBUG: logger.debug(l_suppliers) suppliers, unique = [], [] @@ -69,10 +68,10 @@ def supplier_command(ticker=""): disnake.Embed( title=title, description=suppliers, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") @@ -91,10 +90,10 @@ def supplier_command(ticker=""): disnake.Embed( title=title, description=description, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) i += 30 @@ -103,7 +102,7 @@ def supplier_command(ticker=""): # Output data output = { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/government/contracts.py b/bots/stocks/government/contracts.py index f813d1af282..e5ba66753b4 100644 --- a/bots/stocks/government/contracts.py +++ b/bots/stocks/government/contracts.py @@ -5,8 +5,7 @@ from typing import Union import pandas as pd from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -22,7 +21,7 @@ def contracts_command( """Displays contracts associated with tickers [quiverquant.com]""" past_transaction_days = int(past_transaction_days) # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("gov contracts %s %s %s", ticker, past_transaction_days, raw) if ticker == "": @@ -58,7 +57,7 @@ def contracts_command( plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = helpers.image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": f"Stocks: [quiverquant.com] Contracts by {ticker}", diff --git a/bots/stocks/government/gtrades.py b/bots/stocks/government/gtrades.py index b792cf60d80..c68e40bf344 100644 --- a/bots/stocks/government/gtrades.py +++ b/bots/stocks/government/gtrades.py @@ -6,8 +6,7 @@ import matplotlib.dates as mdates import pandas as pd from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -25,7 +24,7 @@ def gtrades_command( ): """Displays government trades [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "gov gtrades %s %s %s %s", ticker, @@ -115,7 +114,7 @@ def gtrades_command( plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": "Stocks: [quiverquant.com] Government Trades", diff --git a/bots/stocks/government/histcont.py b/bots/stocks/government/histcont.py index 3558a732a7e..f68e519cff1 100644 --- a/bots/stocks/government/histcont.py +++ b/bots/stocks/government/histcont.py @@ -4,8 +4,7 @@ import logging import numpy as np from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -18,7 +17,7 @@ logger = logging.getLogger(__name__) def histcont_command(ticker=""): """Displays historical quarterly-contracts [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("gov histcont %s", ticker) if ticker == "": @@ -61,7 +60,7 @@ def histcont_command(ticker=""): plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": "Stocks: Historical Quarterly Government Contract ", diff --git a/bots/stocks/government/lastcontracts.py b/bots/stocks/government/lastcontracts.py index 680e9ba8f70..31f75e7d622 100644 --- a/bots/stocks/government/lastcontracts.py +++ b/bots/stocks/government/lastcontracts.py @@ -3,8 +3,7 @@ import logging import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.government import quiverquant_model @@ -16,7 +15,7 @@ def lastcontracts_command(past_transactions_days: int = 2, num: int = 20): """Displays last government contracts [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("gov lastcontracts %s %s", past_transactions_days, num) df_contracts = quiverquant_model.get_government_trading("contracts") @@ -59,24 +58,24 @@ def lastcontracts_command(past_transactions_days: int = 2, num: int = 20): disnake.Embed( title=title, description=initial_str, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) for column in df_contracts.columns.values: description = "```" + df_contracts[column].fillna("").to_string() + "```" embeds.append( - disnake.Embed(description=description, colour=cfg.COLOR,).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + disnake.Embed(description=description, colour=imps.COLOR,).set_author( + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") return { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/government/lasttrades.py b/bots/stocks/government/lasttrades.py index 5f6b569b1ba..be46700e7a7 100644 --- a/bots/stocks/government/lasttrades.py +++ b/bots/stocks/government/lasttrades.py @@ -2,8 +2,7 @@ import logging import disnake -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.government import quiverquant_model @@ -15,7 +14,7 @@ def lasttrades_command(gov_type="", past_days: int = 5, representative=""): """Displays trades made by the congress/senate/house [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "gov lasttrades %s %s %s", gov_type, @@ -107,25 +106,25 @@ def lasttrades_command(gov_type="", past_days: int = 5, representative=""): disnake.Embed( title=title, description=initial_str, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports = [f"{initial_str}"] for column in df_gov_rep.columns.values: description = "```" + df_gov_rep[column].fillna("").to_string() + "```" embeds.append( - disnake.Embed(description=description, colour=cfg.COLOR,).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + disnake.Embed(description=description, colour=imps.COLOR,).set_author( + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") output = { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, @@ -158,25 +157,25 @@ def lasttrades_command(gov_type="", past_days: int = 5, representative=""): disnake.Embed( title=title, description=initial_str, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports = [f"{initial_str}"] for column in df_gov.columns.values: description = "```" + df_gov[column].fillna("").to_string() + "```" embeds.append( - disnake.Embed(description=description, colour=cfg.COLOR,).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + disnake.Embed(description=description, colour=imps.COLOR,).set_author( + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{description}") output = { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/government/lobbying.py b/bots/stocks/government/lobbying.py index 6efcf10f56e..a0acf553ab2 100644 --- a/bots/stocks/government/lobbying.py +++ b/bots/stocks/government/lobbying.py @@ -3,8 +3,7 @@ from typing import Any, Dict import disnake -import bots.config_discordbot as cfg -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.government import quiverquant_model @@ -16,7 +15,7 @@ def lobbying_command(ticker="", num: int = 10): """Displays lobbying details [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("gov-lobbying %s", ticker) if ticker == "": @@ -53,11 +52,11 @@ def lobbying_command(ticker="", num: int = 10): embed = disnake.Embed( title=title, description=description, - colour=cfg.COLOR, + colour=imps.COLOR, ) embed.set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) output: Dict[str, Any] = { "title": title, @@ -75,10 +74,10 @@ def lobbying_command(ticker="", num: int = 10): disnake.Embed( title=title, description=descript, - colour=cfg.COLOR, + colour=imps.COLOR, ).set_author( - name=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) ) reports.append(f"{descript}") @@ -87,7 +86,7 @@ def lobbying_command(ticker="", num: int = 10): i += 1 output = { - "view": Menu, + "view": imps.Menu, "title": title, "description": reports, "embed": embeds, diff --git a/bots/stocks/government/qtrcontracts.py b/bots/stocks/government/qtrcontracts.py index 5338369073f..f56eede4429 100644 --- a/bots/stocks/government/qtrcontracts.py +++ b/bots/stocks/government/qtrcontracts.py @@ -4,8 +4,7 @@ import logging import numpy as np from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -18,7 +17,7 @@ logger = logging.getLogger(__name__) def qtrcontracts_command(num: int = 20, analysis=""): """Displays a look at government contracts [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("gov-qtrcontracts %s %s", num, analysis) possible_args = ["total", "upmom", "downmom"] @@ -88,7 +87,7 @@ def qtrcontracts_command(num: int = 20, analysis=""): plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) output = { "title": "Stocks: [quiverquant.com] Government Contracts", "imagefile": imagefile, diff --git a/bots/stocks/government/topbuys.py b/bots/stocks/government/topbuys.py index a512571330c..b7cf69e0140 100644 --- a/bots/stocks/government/topbuys.py +++ b/bots/stocks/government/topbuys.py @@ -5,8 +5,7 @@ from datetime import datetime, timedelta import pandas as pd from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -24,7 +23,7 @@ def topbuys_command( ): """Displays most purchased stocks by the congress/senate/house [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "gov-topbuys %s %s %s %s", gov_type, @@ -110,7 +109,7 @@ def topbuys_command( plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": f"Stocks: [quiverquant.com] Top purchases for {gov_type.upper()}", diff --git a/bots/stocks/government/toplobbying.py b/bots/stocks/government/toplobbying.py index 883cbe703b5..e1862e9fe39 100644 --- a/bots/stocks/government/toplobbying.py +++ b/bots/stocks/government/toplobbying.py @@ -4,8 +4,7 @@ import logging import pandas as pd from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -18,7 +17,7 @@ logger = logging.getLogger(__name__) def toplobbying_command(num: int = 10, raw: bool = False): """Displays top lobbying firms [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("gov-toplobbying %s %s", num, raw) # Retrieve Data @@ -42,9 +41,10 @@ def toplobbying_command(num: int = 10, raw: bool = False): dataBytesIO = io.BytesIO() plt.savefig(dataBytesIO) + plt.close("all") dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": "Stocks: [quiverquant.com] Top Lobbying Firms", diff --git a/bots/stocks/government/topsells.py b/bots/stocks/government/topsells.py index a8a568bd550..80a6b84f4df 100644 --- a/bots/stocks/government/topsells.py +++ b/bots/stocks/government/topsells.py @@ -5,8 +5,7 @@ from datetime import datetime, timedelta import pandas as pd from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import plot_autoscale @@ -24,7 +23,7 @@ def topsells_command( ): """Displays most sold stocks by the congress/senate/house [quiverquant.com]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "gov-topsells %s %s %s %s", gov_type, @@ -121,7 +120,7 @@ def topsells_command( plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": f"Stocks: [quiverquant.com] Top sells for {gov_type.upper()}", diff --git a/bots/stocks/insider/__init__.py b/bots/stocks/insider/__init__.py new file mode 100755 index 00000000000..e69de29bb2d diff --git a/bots/stocks/insider/lins.py b/bots/stocks/insider/lins.py index a468928a8a2..95f46f96cb3 100644 --- a/bots/stocks/insider/lins.py +++ b/bots/stocks/insider/lins.py @@ -1,14 +1,10 @@ import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots.config_discordbot import gst_imgur -from bots.helpers import save_image -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.insider import finviz_model @@ -16,7 +12,7 @@ logger = logging.getLogger(__name__) @log_start_end(log=logger) -def lins_command(ticker: str = "", num: int = 10): +def lins_command(ticker: str = "", num: int = 30): """Display insider activity for a given stock ticker. [Source: Finviz] Parameters @@ -26,7 +22,7 @@ def lins_command(ticker: str = "", num: int = 10): """ # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("disc-lins %s", num) d_finviz_insider = finviz_model.get_last_insider_activity(ticker) @@ -54,31 +50,48 @@ def lins_command(ticker: str = "", num: int = 10): embeds: list = [] - i, i2, end = 0, 0, 20 + i, i2, end = 0, 0, 15 df_pg, embeds_img, images_list = [], [], [] while i < len(df.index): df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1700, (40 + (45 * 20))), - col_width=[4, 13, 4, 4, 3.5, 5.3, 6, 8, 7], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(1400, (40 + (45 * 20))), + col_width=[4, 10, 4, 4, 3.5, 5.3, 6, 8, 7], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["center", "left"]))) - imagefile = save_image(f"disc-insider{i}.png", fig) + fig.update_traces( + cells=( + dict( + align=[ + "center", + "center", + "center", + "right", + "right", + "right", + "right", + "center", + ] + ) + ) + ) + imagefile = imps.save_image("disc-insider.png", fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link = uploaded_image.link os.remove(imagefile_save) @@ -88,23 +101,23 @@ def lins_command(ticker: str = "", num: int = 10): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 - i += 20 - end += 20 + i += 15 + end += 15 # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -118,7 +131,7 @@ def lins_command(ticker: str = "", num: int = 10): ] return { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, diff --git a/bots/stocks/options/cc_hist.py b/bots/stocks/options/cc_hist.py index e86bf15ed95..19b6cc98559 100644 --- a/bots/stocks/options/cc_hist.py +++ b/bots/stocks/options/cc_hist.py @@ -4,8 +4,7 @@ import plotly.graph_objects as go import yfinance as yf from plotly.subplots import make_subplots -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end logger = logging.getLogger(__name__) @@ -31,7 +30,7 @@ def cc_hist_command( """ # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.info("opt hist %s, %s, %s, %s", ticker, strike, opt_type, expiry) # Check for argument @@ -86,18 +85,18 @@ def cc_hist_command( ), secondary_y=False, ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( margin=dict(l=0, r=0, t=25, b=5), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, showlegend=False, title=title, title_x=0.1, title_font_size=14, yaxis2_title="Premium", yaxis_title="Volume", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( showgrid=False, fixedrange=False, @@ -137,15 +136,15 @@ def cc_hist_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": title, diff --git a/bots/stocks/options/hist.py b/bots/stocks/options/hist.py index 7e9ec5ac355..95ea4328175 100644 --- a/bots/stocks/options/hist.py +++ b/bots/stocks/options/hist.py @@ -3,8 +3,7 @@ import logging import plotly.graph_objects as go from plotly.subplots import make_subplots -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import syncretism_model @@ -35,7 +34,7 @@ def hist_command( """ # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.info( "opt grhist %s %s %s %s %s", ticker, strike, opt_type, expiry, greek ) @@ -85,17 +84,17 @@ def hist_command( yaxis="y3", ), ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( margin=dict(l=10, r=10, t=30, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.03, title_font_size=14, yaxis_title="Stock Price", - font=cfg.PLT_FONT, + font=imps.PLT_FONT, yaxis=dict( side="right", fixedrange=False, @@ -149,15 +148,15 @@ def hist_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"{ticker.upper()} {strike} {opt_type} expiring {expiry} Historical", diff --git a/bots/stocks/options/iv.py b/bots/stocks/options/iv.py index f91db9cb3fa..e2fa5f14679 100644 --- a/bots/stocks/options/iv.py +++ b/bots/stocks/options/iv.py @@ -1,9 +1,6 @@ import logging -import df2img - -import bots.config_discordbot as cfg -from bots.helpers import save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import barchart_model @@ -15,7 +12,7 @@ def iv_command(ticker: str = None): """Options IV""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("opt info %s", ticker) # Check for argument @@ -37,14 +34,14 @@ def iv_command(ticker: str = None): ] df.set_index(" ", inplace=True) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, fig_size=(600, 1500), col_width=[3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces( @@ -61,7 +58,7 @@ def iv_command(ticker: str = None): ) ), ) - imagefile = save_image("opt-info.png", fig) + imagefile = imps.save_image("opt-info.png", fig) return { "title": f"{ticker.upper()} Options: IV", diff --git a/bots/stocks/options/oi.py b/bots/stocks/options/oi.py index a455b93d5a3..76b9d31b079 100644 --- a/bots/stocks/options/oi.py +++ b/bots/stocks/options/oi.py @@ -3,8 +3,7 @@ import logging import pandas as pd import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import op_helpers, yfinance_model @@ -21,7 +20,7 @@ def oi_command( """Options OI""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("opt oi %s %s %s %s", ticker, expiry, min_sp, max_sp) # Check for argument @@ -102,15 +101,15 @@ def oi_command( name=f"Max Pain: {max_pain}", ) ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_xaxes( range=[min_strike, max_strike], constrain="domain", ) fig.update_layout( margin=dict(l=0, r=0, t=60, b=20), - template=cfg.PLT_SCAT_STYLE_TEMPLATE, + template=imps.PLT_SCAT_STYLE_TEMPLATE, title=f"Open Interest for {ticker.upper()} expiring {expiry}", title_x=0.5, legend_title="", @@ -131,15 +130,15 @@ def oi_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Open Interest for {ticker.upper()} expiring {expiry}", diff --git a/bots/stocks/options/opt_chain.py b/bots/stocks/options/opt_chain.py old mode 100644 new mode 100755 index d6b963d9e1b..2e642b2920d --- a/bots/stocks/options/opt_chain.py +++ b/bots/stocks/options/opt_chain.py @@ -1,15 +1,11 @@ import logging import os -import df2img import disnake import numpy as np import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import yfinance_model @@ -27,7 +23,7 @@ def chain_command( """Show calls/puts for given ticker and expiration""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "opt chain %s %s %s %s %s", ticker, expiry, opt_type, min_sp, max_sp ) @@ -78,10 +74,11 @@ def chain_command( formats = {"iv": "{:.2f}"} for col, f in formats.items(): df[col] = df[col].map(lambda x: f.format(x)) # pylint: disable=W0640 - df.set_index("strike", inplace=True) + df.columns = df.columns.str.capitalize() + df.set_index("Strike", inplace=True) title = ( - f"Stocks: {opt_type} Option Chain for {ticker.upper()} on {expiry} [yfinance]" + f"Stocks: {opt_type} Option Chain for {ticker.upper()} on\n{expiry} [yfinance]" ) embeds: list = [] @@ -91,25 +88,28 @@ def chain_command( while i < len(df.index): df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(1000, (40 + (40 * 20))), - col_width=[3, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(570, (40 + (40 * 20))), + col_width=[3.1, 3.1, 3.1, 3.5], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) + fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "opt-chain.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link = uploaded_image.link os.remove(imagefile_save) @@ -119,7 +119,7 @@ def chain_command( embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -129,13 +129,13 @@ def chain_command( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -149,7 +149,7 @@ def chain_command( ] return { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, diff --git a/bots/stocks/options/overview.py b/bots/stocks/options/overview.py index e5c10d32323..58b62d4ece7 100644 --- a/bots/stocks/options/overview.py +++ b/bots/stocks/options/overview.py @@ -4,16 +4,13 @@ import time from functools import reduce from multiprocessing import Pool -import df2img import disnake import numpy as np import pandas as pd import plotly.graph_objects as go import yfinance as yf -from bots import helpers -from bots.config_discordbot import gst_imgur -import bots.config_discordbot as cfg -from bots.menus.menu import Menu + +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import op_helpers, yfinance_model from gamestonk_terminal.stocks.options.barchart_model import get_options_info @@ -38,10 +35,7 @@ columns = [ ] -# pylint: disable=R0912 -# pylint: disable=R0913 -# pylint: disable=R0914 -# pylint: disable=R0915 +# pylint: disable=R0912,R0913,R0914,R0915 @log_start_end(log=logger) def options_run( ticker, @@ -102,15 +96,15 @@ def options_run( name=f"Max Pain: {max_pain}", ) ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_xaxes( range=[min_strike, max_strike], constrain="domain", ) fig.update_layout( margin=dict(l=0, r=0, t=60, b=20), - template=cfg.PLT_SCAT_STYLE_TEMPLATE, + template=imps.PLT_SCAT_STYLE_TEMPLATE, title=f"Open Interest for {ticker.upper()} expiring {expiry}", title_x=0.5, legend_title="", @@ -119,7 +113,7 @@ def options_run( xaxis=dict( rangeslider=dict(visible=False), ), - font=cfg.PLT_FONT, + font=imps.PLT_FONT, legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01), dragmode="pan", ) @@ -127,8 +121,8 @@ def options_run( imagefile = "opt-oi.png" plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) reports.append(plt_link) fig.update_layout( @@ -136,14 +130,16 @@ def options_run( height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link_oi = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link_oi = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image_oi = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image_oi = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link_oi = uploaded_image_oi.link os.remove(imagefile_save) @@ -179,14 +175,14 @@ def options_run( embeds.append( disnake.Embed( title=f"{ticker.upper()} Overview", - color=cfg.COLOR, + color=imps.COLOR, ), ) embeds.append( disnake.Embed( title=f"{pfix}Open Interest{sfix}", description=plt_link, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) choices.append( @@ -202,25 +198,27 @@ def options_run( while i < dindex: df_calls = calls_df.iloc[i:end] df_calls.append(df_calls) - figc = df2img.plot_dataframe( + figc = imps.plot_df( df_calls, fig_size=(1000, (40 + (40 * 20))), col_width=[3, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) imagefile = "opt-calls.png" - imagefile = helpers.save_image(imagefile, figc) + imagefile = imps.save_image(imagefile, figc) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link = uploaded_image.link os.remove(imagefile_save) @@ -233,7 +231,7 @@ def options_run( embeds.append( disnake.Embed( title=f"{pfix}Calls{sfix}", - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -279,25 +277,27 @@ def options_run( while i < dindex: df_puts = puts_df.iloc[i:end] df_puts.append(df_puts) - figp = df2img.plot_dataframe( + figp = imps.plot_df( df_puts, fig_size=(1000, (40 + (40 * 20))), col_width=[3, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) imagefile = "opt-puts.png" - imagefile = helpers.save_image(imagefile, figp) + imagefile = imps.save_image(imagefile, figp) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image(imagefile_save, title="something") + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( + imagefile_save, title="something" + ) image_link = uploaded_image.link os.remove(imagefile_save) @@ -310,7 +310,7 @@ def options_run( embeds.append( disnake.Embed( title=f"{pfix}Puts{sfix}", - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -331,13 +331,13 @@ def options_run( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) # Set images to Pages @@ -352,7 +352,7 @@ def options_run( if url: embeds[0].set_thumbnail(url=f"{url}") else: - embeds[0].set_thumbnail(url=cfg.AUTHOR_ICON_URL) + embeds[0].set_thumbnail(url=imps.AUTHOR_ICON_URL) # Overview Section if "^" not in ticker: @@ -424,7 +424,7 @@ def options_data( ): # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("opt overview %s %s %s %s", ticker, expiry, min_sp, max_sp) # Check for argument @@ -546,7 +546,7 @@ def overview_command( description = f"```\n{''.join(reports)}\n```" return { - "view": Menu, + "view": imps.Menu, "titles": titles, "description": description, "embed": embeds, diff --git a/bots/stocks/options/unu.py b/bots/stocks/options/unu.py old mode 100644 new mode 100755 index db93bf07a11..1f488d4d4f1 --- a/bots/stocks/options/unu.py +++ b/bots/stocks/options/unu.py @@ -1,16 +1,12 @@ import logging import os -import df2img import disnake import numpy as np import pandas as pd import requests -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import get_user_agent @@ -22,7 +18,7 @@ def unu_command(num: int = 20): """Unusual Options""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("opt unu %s", num) pages = np.arange(0, num // 20 + 1) @@ -90,26 +86,28 @@ def unu_command(num: int = 20): while i < dindex: df_pg = df.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg, - fig_size=(850, (40 + (40 * dindex))), - col_width=[3, 4, 3, 3, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(650, (40 + (40 * dindex))), + col_width=[3, 4, 3, 2.5, 2.5, 2, 2], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align=["left"]))) + fig.update_traces( + cells=(dict(align=["center", "center", "right", "center", "right"])) + ) imagefile = "opt-unu.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -121,7 +119,7 @@ def unu_command(num: int = 20): embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -131,13 +129,13 @@ def unu_command(num: int = 20): # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -151,7 +149,7 @@ def unu_command(num: int = 20): ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -159,18 +157,20 @@ def unu_command(num: int = 20): "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df, - fig_size=(850, (40 + (40 * dindex))), - col_width=[3, 4, 3, 3, 3, 3, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + fig_size=(650, (40 + (40 * dindex))), + col_width=[3, 4, 3, 2.5, 2.5, 2, 2], + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) - fig.update_traces(cells=(dict(align="left"))) - imagefile = helpers.save_image("opt-unu.png", fig) + fig.update_traces( + cells=(dict(align=["center", "center", "right", "center", "right"])) + ) + imagefile = imps.save_image("opt-unu.png", fig) output = { "title": "Unusual Options", diff --git a/bots/stocks/options/vol.py b/bots/stocks/options/vol.py index 12c3a0a35ec..4ae4e7f5f98 100644 --- a/bots/stocks/options/vol.py +++ b/bots/stocks/options/vol.py @@ -3,8 +3,7 @@ import logging import pandas as pd import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import yfinance_model @@ -21,7 +20,7 @@ def vol_command( """Options VOL""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("opt-vol %s %s %s %s", ticker, expiry, min_sp, max_sp) # Check for argument @@ -84,15 +83,15 @@ def vol_command( name="Current Price", ) ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_xaxes( range=[min_strike, max_strike], constrain="domain", ) fig.update_layout( margin=dict(l=0, r=0, t=60, b=20), - template=cfg.PLT_SCAT_STYLE_TEMPLATE, + template=imps.PLT_SCAT_STYLE_TEMPLATE, title=f"Volume for {ticker.upper()} expiring {expiry}", title_x=0.5, legend_title="", @@ -113,15 +112,15 @@ def vol_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Volume for {ticker.upper()} expiring {expiry}", diff --git a/bots/stocks/options/vsurf.py b/bots/stocks/options/vsurf.py index d3a9ef97b87..429b385e0c0 100644 --- a/bots/stocks/options/vsurf.py +++ b/bots/stocks/options/vsurf.py @@ -4,8 +4,7 @@ import numpy as np import plotly.graph_objects as go from scipy.spatial import Delaunay -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.options import yfinance_model @@ -26,7 +25,7 @@ def vsurf_command( """ # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("opt vsurf %s %s", ticker, z) # Check for argument @@ -67,7 +66,7 @@ def vsurf_command( j=J, k=K, intensity=Z, - colorscale=cfg.PLT_3DMESH_COLORSCALE, + colorscale=imps.PLT_3DMESH_COLORSCALE, hovertemplate="DTE: %{y}
Strike: %{x}
" + z + ": %{z}", @@ -77,8 +76,8 @@ def vsurf_command( ) ] ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( scene=dict( xaxis=dict( @@ -96,31 +95,31 @@ def vsurf_command( ) fig.update_layout( margin=dict(l=0, r=0, t=40, b=20), - template=cfg.PLT_3DMESH_STYLE_TEMPLATE, + template=imps.PLT_3DMESH_STYLE_TEMPLATE, title=f"{label} Surface for {ticker.upper()}", title_x=0.5, - hoverlabel=cfg.PLT_3DMESH_HOVERLABEL, + hoverlabel=imps.PLT_3DMESH_HOVERLABEL, scene_camera=dict( up=dict(x=0, y=0, z=2), center=dict(x=0, y=0, z=-0.3), eye=dict(x=1.25, y=1.25, z=0.69), ), - scene=cfg.PLT_3DMESH_SCENE, + scene=imps.PLT_3DMESH_SCENE, ) imagefile = "opt-vsurf.png" # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"{label} Surface for {ticker.upper()}", diff --git a/bots/stocks/quote.py b/bots/stocks/quote.py old mode 100644 new mode 100755 index ae9021bb57b..53bac2f4d22 --- a/bots/stocks/quote.py +++ b/bots/stocks/quote.py @@ -1,9 +1,6 @@ import logging -import df2img - -import bots.config_discordbot as cfg -from bots.helpers import quote, save_image +from bots import imps from gamestonk_terminal.decorators import log_start_end logger = logging.getLogger(__name__) @@ -14,26 +11,26 @@ def quote_command(ticker: str = None): """Ticker Quote""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("quote %s", ticker) # Check for argument if ticker is None: raise Exception("Stock ticker is required") - df = quote(ticker) - fig = df2img.plot_dataframe( + df = imps.quote(ticker) + fig = imps.plot_df( df, fig_size=(600, 1500), col_width=[2, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align="left"))) - imagefile = save_image("quote.png", fig) + imagefile = imps.save_image("quote.png", fig) return { "title": f"{ticker.upper()} Quote", diff --git a/bots/stocks/screener/financial.py b/bots/stocks/screener/financial.py index bf49e44f4ed..5912e33d6b3 100644 --- a/bots/stocks/screener/financial.py +++ b/bots/stocks/screener/financial.py @@ -2,14 +2,10 @@ import difflib import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import lambda_long_number_format @@ -27,7 +23,7 @@ def financial_command(preset="template", sort="", limit: int = 5, ascend: bool = raise Exception("Invalid preset selected!") # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-financial %s %s %s %s", preset, sort, limit, ascend) # Check for argument @@ -84,26 +80,26 @@ def financial_command(preset="template", sort="", limit: int = 5, ascend: bool = while i < dindex: df_pg = df_screen.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "scr_financial.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -115,7 +111,7 @@ def financial_command(preset="template", sort="", limit: int = 5, ascend: bool = embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -125,13 +121,13 @@ def financial_command(preset="template", sort="", limit: int = 5, ascend: bool = # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -145,7 +141,7 @@ def financial_command(preset="template", sort="", limit: int = 5, ascend: bool = ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -153,18 +149,18 @@ def financial_command(preset="template", sort="", limit: int = 5, ascend: bool = "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_screen.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = helpers.save_image("scr_financial.png", fig) + imagefile = imps.save_image("scr_financial.png", fig) output = { "title": title, diff --git a/bots/stocks/screener/historical.py b/bots/stocks/screener/historical.py index e4f4670c98e..e6351a22d08 100644 --- a/bots/stocks/screener/historical.py +++ b/bots/stocks/screener/historical.py @@ -9,8 +9,7 @@ from finvizfinance.screener import ticker from matplotlib import pyplot as plt from pandas.plotting import register_matplotlib_converters -import bots.config_discordbot as cfg -from bots.helpers import image_border +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def historical_command(signal: str = "", start=""): """Displays historical price comparison between similar companies [Yahoo Finance]""" # Debug user input - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-historical %s %s", signal, start) # Check for argument @@ -64,7 +63,7 @@ def historical_command(signal: str = "", start=""): if start == "": start = datetime.now() - timedelta(days=365) else: - start = datetime.strptime(start, cfg.DATE_FORMAT) + start = datetime.strptime(start, imps.DATE_FORMAT) # Output Data l_min = [] @@ -137,7 +136,7 @@ def historical_command(signal: str = "", start=""): plt.savefig(dataBytesIO) dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + imagefile = imps.image_border(imagefile, base64=dataBytesIO) return { "title": "Stocks: [Yahoo Finance] Historical Screener", diff --git a/bots/stocks/screener/overview.py b/bots/stocks/screener/overview.py index 5e4e0834664..6ab85ec0702 100644 --- a/bots/stocks/screener/overview.py +++ b/bots/stocks/screener/overview.py @@ -2,14 +2,10 @@ import difflib import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import lambda_long_number_format @@ -29,7 +25,7 @@ def overview_command( raise Exception("Invalid preset selected!") # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-overview %s %s %s %s", preset, sort, limit, ascend) # Check for argument @@ -90,26 +86,26 @@ def overview_command( while i < dindex: df_pg = df_screen.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg.transpose(), fig_size=(800, 720), col_width=[1.5, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "scr_overview.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -121,7 +117,7 @@ def overview_command( embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -131,13 +127,13 @@ def overview_command( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -151,7 +147,7 @@ def overview_command( ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -159,18 +155,18 @@ def overview_command( "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_screen.transpose(), fig_size=(800, 720), col_width=[1.5, 3], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = helpers.save_image("scr_overview.png", fig) + imagefile = imps.save_image("scr_overview.png", fig) output = { "title": title, diff --git a/bots/stocks/screener/ownership.py b/bots/stocks/screener/ownership.py index 52620ecafb6..e78dc14d17d 100644 --- a/bots/stocks/screener/ownership.py +++ b/bots/stocks/screener/ownership.py @@ -2,14 +2,10 @@ import difflib import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import lambda_long_number_format @@ -29,7 +25,7 @@ def ownership_command( raise Exception("Invalid preset selected!") # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-ownership %s %s %s %s", preset, sort, limit, ascend) # Check for argument @@ -90,26 +86,26 @@ def ownership_command( while i < dindex: df_pg = df_screen.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "scr_ownership.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -121,7 +117,7 @@ def ownership_command( embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -131,13 +127,13 @@ def ownership_command( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -151,7 +147,7 @@ def ownership_command( ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -159,18 +155,18 @@ def ownership_command( "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_screen.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = helpers.save_image("scr_ownership.png", fig) + imagefile = imps.save_image("scr_ownership.png", fig) output = { "title": title, diff --git a/bots/stocks/screener/performance.py b/bots/stocks/screener/performance.py index 1a4f8f38f2b..820c0ebe961 100644 --- a/bots/stocks/screener/performance.py +++ b/bots/stocks/screener/performance.py @@ -2,14 +2,10 @@ import difflib import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import lambda_long_number_format @@ -29,7 +25,7 @@ def performance_command( raise Exception("Invalid preset selected!") # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-performance %s %s %s %s", preset, sort, limit, ascend) # Check for argument @@ -110,26 +106,26 @@ def performance_command( while i < dindex: df_pg = df_screen.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "scr_performance.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -141,7 +137,7 @@ def performance_command( embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -151,13 +147,13 @@ def performance_command( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -171,7 +167,7 @@ def performance_command( ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -179,18 +175,18 @@ def performance_command( "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_screen.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = helpers.save_image("scr_performance.png", fig) + imagefile = imps.save_image("scr_performance.png", fig) output = { "title": title, diff --git a/bots/stocks/screener/presets_custom.py b/bots/stocks/screener/presets_custom.py index d950ff7266b..f1f5c9ada16 100644 --- a/bots/stocks/screener/presets_custom.py +++ b/bots/stocks/screener/presets_custom.py @@ -1,6 +1,6 @@ import logging -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end diff --git a/bots/stocks/screener/presets_default.py b/bots/stocks/screener/presets_default.py index 1a085c481f9..ff0d12fe55f 100644 --- a/bots/stocks/screener/presets_default.py +++ b/bots/stocks/screener/presets_default.py @@ -1,6 +1,6 @@ import logging -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end diff --git a/bots/stocks/screener/screener_options.py b/bots/stocks/screener/screener_options.py index 77eca2cb11a..58bad89e49d 100644 --- a/bots/stocks/screener/screener_options.py +++ b/bots/stocks/screener/screener_options.py @@ -1,6 +1,6 @@ import os -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg presets_path = os.path.join( cfg.GST_PATH, "gamestonk_terminal", "stocks", "screener", "presets", "" diff --git a/bots/stocks/screener/technical.py b/bots/stocks/screener/technical.py index a74bf53d0b2..8d677b9fb8d 100644 --- a/bots/stocks/screener/technical.py +++ b/bots/stocks/screener/technical.py @@ -2,14 +2,10 @@ import difflib import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import lambda_long_number_format @@ -29,7 +25,7 @@ def technical_command( raise Exception("Invalid preset selected!") # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-technical %s %s %s %s", preset, sort, limit, ascend) # Check for argument @@ -89,26 +85,26 @@ def technical_command( while i < dindex: df_pg = df_screen.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "scr_technical.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -120,7 +116,7 @@ def technical_command( embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -130,13 +126,13 @@ def technical_command( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -150,7 +146,7 @@ def technical_command( ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -158,18 +154,18 @@ def technical_command( "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_screen.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = helpers.save_image("scr_technical.png", fig) + imagefile = imps.save_image("scr_technical.png", fig) output = { "title": title, diff --git a/bots/stocks/screener/valuation.py b/bots/stocks/screener/valuation.py index 85e5636b9a5..bb75cf2139b 100644 --- a/bots/stocks/screener/valuation.py +++ b/bots/stocks/screener/valuation.py @@ -2,14 +2,10 @@ import difflib import logging import os -import df2img import disnake import pandas as pd -import bots.config_discordbot as cfg -from bots import helpers -from bots.config_discordbot import gst_imgur -from bots.menus.menu import Menu +from bots import imps from bots.stocks.screener import screener_options as so from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import lambda_long_number_format @@ -29,7 +25,7 @@ def valuation_command( raise Exception("Invalid preset selected!") # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug("scr-valuation %s %s %s %s", preset, sort, limit, ascend) # Check for argument @@ -90,26 +86,26 @@ def valuation_command( while i < dindex: df_pg = df_screen.iloc[i:end] df_pg.append(df_pg) - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_pg.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) imagefile = "scr_valuation.png" - imagefile = helpers.save_image(imagefile, fig) + imagefile = imps.save_image(imagefile, fig) - if cfg.IMAGES_URL or cfg.IMGUR_CLIENT_ID != "REPLACE_ME": - image_link = cfg.IMAGES_URL + imagefile + if imps.IMAGES_URL or imps.IMGUR_CLIENT_ID != "REPLACE_ME": + image_link = imps.IMAGES_URL + imagefile images_list.append(imagefile) else: - imagefile_save = cfg.IMG_DIR / imagefile - uploaded_image = gst_imgur.upload_image( + imagefile_save = imps.IMG_DIR / imagefile + uploaded_image = imps.gst_imgur.upload_image( imagefile_save, title="something" ) image_link = uploaded_image.link @@ -121,7 +117,7 @@ def valuation_command( embeds.append( disnake.Embed( title=title, - colour=cfg.COLOR, + colour=imps.COLOR, ), ) i2 += 1 @@ -131,13 +127,13 @@ def valuation_command( # Author/Footer for i in range(0, i2): embeds[i].set_author( - name=cfg.AUTHOR_NAME, - url=cfg.AUTHOR_URL, - icon_url=cfg.AUTHOR_ICON_URL, + name=imps.AUTHOR_NAME, + url=imps.AUTHOR_URL, + icon_url=imps.AUTHOR_ICON_URL, ) embeds[i].set_footer( - text=cfg.AUTHOR_NAME, - icon_url=cfg.AUTHOR_ICON_URL, + text=imps.AUTHOR_NAME, + icon_url=imps.AUTHOR_ICON_URL, ) i = 0 @@ -151,7 +147,7 @@ def valuation_command( ] output = { - "view": Menu, + "view": imps.Menu, "title": title, "embed": embeds, "choices": choices, @@ -159,18 +155,18 @@ def valuation_command( "images_list": images_list, } else: - fig = df2img.plot_dataframe( + fig = imps.plot_df( df_screen.transpose(), fig_size=(800, 720), col_width=[2, 1.5], - tbl_header=cfg.PLT_TBL_HEADER, - tbl_cells=cfg.PLT_TBL_CELLS, - font=cfg.PLT_TBL_FONT, - row_fill_color=cfg.PLT_TBL_ROW_COLORS, + tbl_header=imps.PLT_TBL_HEADER, + tbl_cells=imps.PLT_TBL_CELLS, + font=imps.PLT_TBL_FONT, + row_fill_color=imps.PLT_TBL_ROW_COLORS, paper_bgcolor="rgba(0, 0, 0, 0)", ) fig.update_traces(cells=(dict(align=["center", "right"]))) - imagefile = helpers.save_image("scr_valuation.png", fig) + imagefile = imps.save_image("scr_valuation.png", fig) output = { "title": title, diff --git a/bots/stocks/sia/__init__.py b/bots/stocks/sia/__init__.py new file mode 100755 index 00000000000..e69de29bb2d diff --git a/bots/stocks/sia/cpic.py b/bots/stocks/sia/cpic.py index a76e3be6643..ecda4954432 100644 --- a/bots/stocks/sia/cpic.py +++ b/bots/stocks/sia/cpic.py @@ -5,8 +5,7 @@ import numpy as np import pandas as pd import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.stocks.sector_industry_analysis import financedatabase_model @@ -14,7 +13,6 @@ def cpic_command( industry: str, mktcap: str = "", exclude_exchanges: bool = False, - export: str = "", raw: bool = False, max_countries_to_display: int = 15, min_pct_to_display_country: float = 0.015, @@ -29,8 +27,6 @@ def cpic_command( Select market cap of companies to consider from Small, Mid and Large exclude_exchanges : bool Exclude international exchanges - export: str - Format to export data as raw: bool Output all raw data max_countries_to_display: int @@ -70,7 +66,7 @@ def cpic_command( if raw: output = { - "title": "Consumer Prices Index", + "title": "Stocks - Sector and Industry Analysis", "description": f"{df}", } else: @@ -150,8 +146,8 @@ def cpic_command( line=dict(color="#F5EFF3", width=0.8), ), ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( margin=dict(l=40, r=0, t=80, b=40), title=dict( @@ -161,9 +157,9 @@ def cpic_command( xanchor="center", yanchor="top", ), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, colorway=colors, - font=cfg.PLT_FONT, + font=imps.PLT_FONT, ) elif len(companies_per_country) == 1: @@ -177,18 +173,18 @@ def cpic_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) output = { - "title": "Consumer Prices Index", + "title": "Stocks - Sector and Industry Analysis", "description": plt_link, "imagefile": imagefile, } diff --git a/bots/stocks/sia/cps.py b/bots/stocks/sia/cps.py index 30292369b55..d61d71f1f48 100644 --- a/bots/stocks/sia/cps.py +++ b/bots/stocks/sia/cps.py @@ -5,8 +5,7 @@ import numpy as np import pandas as pd import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.stocks.sector_industry_analysis import financedatabase_model @@ -14,7 +13,6 @@ def cps_command( country: str, mktcap: str = "", exclude_exchanges: bool = True, - export: str = "", raw: bool = False, max_sectors_to_display: int = 15, min_pct_to_display_sector: float = 0.015, @@ -29,8 +27,6 @@ def cps_command( Select market cap of companies to consider from Small, Mid and Large exclude_exchanges : bool Exclude international exchanges - export: str - Format to export data as raw: bool Output all raw data max_sectors_to_display: int @@ -72,7 +68,7 @@ def cps_command( if raw: output = { - "title": "Consumer Prices Index", + "title": "Stocks - Sector and Industry Analysis", "description": f"{df}", } else: @@ -147,8 +143,8 @@ def cps_command( line=dict(color="#F5EFF3", width=0.8), ), ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( margin=dict(l=40, r=0, t=80, b=40), title=dict( @@ -158,9 +154,9 @@ def cps_command( xanchor="center", yanchor="top", ), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, colorway=colors, - font=cfg.PLT_FONT, + font=imps.PLT_FONT, ) elif len(companies_per_sector) == 1: @@ -174,18 +170,18 @@ def cps_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) output = { - "title": "Consumer Prices Index", + "title": "Stocks - Sector and Industry Analysis", "description": plt_link, "imagefile": imagefile, } diff --git a/bots/stocks/sia/metric.py b/bots/stocks/sia/metric.py old mode 100644 new mode 100755 index b40205ab9ec..2044696efa3 --- a/bots/stocks/sia/metric.py +++ b/bots/stocks/sia/metric.py @@ -6,11 +6,12 @@ import numpy as np import plotly.graph_objects as go import yfinance -import bots.config_discordbot as cfg -from bots import helpers +from bots import imps from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.sector_industry_analysis import financedatabase_model +# pylint: disable=R1720,R0912 + logger = logging.getLogger(__name__) @@ -69,7 +70,14 @@ def metric_command( ) if similar_cmd: industry = similar_cmd[0] - mktcap = "" + if "price" in data: + mktcap = data["price"]["marketCap"] + if mktcap < 2_000_000_000: + mktcap = "Small" + elif mktcap > 10_000_000_000: + mktcap = "Large" + else: + mktcap = "Mid" stocks_data = financedatabase_model.get_stocks_data( country, sector, industry, mktcap, exclude_exchanges @@ -139,7 +147,7 @@ def metric_command( fig = go.Figure() i = 0 - for name, metric, ticker in zip( + for name, metric, tickers in zip( company_name[::-1], company_metric[::-1], company_ticker[::-1] ): if len(name.split(" ")) > 6 and len(name) > 40: @@ -148,13 +156,13 @@ def metric_command( ) df_name = [] df_metric = [] - df_ticker = [] + df_tickers = [] df_name.append(name) df_metric.append(metric) - df_ticker.append(ticker) + df_tickers.append(tickers) fig.add_trace( go.Bar( - name=ticker, + name=tickers, y=[name], x=[metric], orientation="h", @@ -205,32 +213,32 @@ def metric_command( line_width=3, line=dict(color="grey", dash="dash"), ) - if cfg.PLT_WATERMARK: - fig.add_layout_image(cfg.PLT_WATERMARK) + if imps.PLT_WATERMARK: + fig.add_layout_image(imps.PLT_WATERMARK) fig.update_layout( margin=dict(l=40, r=0, t=100, b=20), - template=cfg.PLT_CANDLE_STYLE_TEMPLATE, + template=imps.PLT_CANDLE_STYLE_TEMPLATE, title=title, colorway=colors, - font=cfg.PLT_FONT, + font=imps.PLT_FONT, legend={"traceorder": "reversed"}, ) imagefile = "sia_metrics.png" # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { - "title": "Consumer Prices Index", + "title": "Stocks - Sector and Industry Analysis", "description": plt_link, "imagefile": imagefile, } diff --git a/bots/stocks/technical_analysis/ad.py b/bots/stocks/technical_analysis/ad.py old mode 100644 new mode 100755 index 10448ec9077..87a0385702a --- a/bots/stocks/technical_analysis/ad.py +++ b/bots/stocks/technical_analysis/ad.py @@ -1,8 +1,8 @@ import logging import plotly.graph_objects as go -from bots import helpers, load_candle -import bots.config_discordbot as cfg + +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import volume_model from gamestonk_terminal.decorators import log_start_end @@ -26,7 +26,7 @@ def ad_command( """Displays chart with accumulation/distribution line [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta ad %s %s %s %s %s %s %s %s %s", @@ -57,7 +57,7 @@ def ad_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = df_ta.join(volume_model.ad(df_stock, is_open)) @@ -84,15 +84,15 @@ def ad_command( [{"secondary_y": False}], ], ) - title = f"{plot['plt_title']} A/D" + title = f"{plot['plt_title']} A/D" fig = plot["fig"] fig.add_trace( go.Scatter( name="A/D", - mode="lines", x=df_ta.index, - y=df_ta["AD"], + y=df_ta.iloc[:, 6].values, + mode="lines", line=dict(width=2), opacity=1, ), @@ -111,8 +111,8 @@ def ad_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -122,15 +122,15 @@ def ad_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Accumulation/Distribution Line {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/adosc.py b/bots/stocks/technical_analysis/adosc.py old mode 100644 new mode 100755 index 51675bba3d1..0f4e642f9b1 --- a/bots/stocks/technical_analysis/adosc.py +++ b/bots/stocks/technical_analysis/adosc.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import volume_model from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def adosc_command( """Displays chart with chaikin oscillator [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta adosc %s %s %s %s %s", @@ -68,7 +67,7 @@ def adosc_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = df_ta.join(volume_model.adosc(df_stock, is_open, fast, slow)) @@ -95,7 +94,7 @@ def adosc_command( [{"secondary_y": False}], ], ) - title = f"{plot['plt_title']} AD Oscillator" + title = f"{plot['plt_title']} AD Oscillator" fig = plot["fig"] fig.add_trace( @@ -103,7 +102,7 @@ def adosc_command( name="AD Osc [M]", mode="lines", x=df_ta.index, - y=df_ta[f"ADOSC_{fast}_{slow}"], + y=df_ta.iloc[:, 6].values, line=dict(width=2), opacity=1, ), @@ -112,8 +111,8 @@ def adosc_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -123,15 +122,15 @@ def adosc_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Accumulation/Distribution Oscillator {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/adx.py b/bots/stocks/technical_analysis/adx.py old mode 100644 new mode 100755 index 2e5724dc514..def99bcc846 --- a/bots/stocks/technical_analysis/adx.py +++ b/bots/stocks/technical_analysis/adx.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import trend_indicators_model from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def adx_command( """Displays chart with average directional movement index [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta adx %s %s %s %s %s %s %s %s %s %s %s", @@ -61,7 +60,7 @@ def adx_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") if not length.lstrip("-").isnumeric(): raise Exception("Number has to be an integer") @@ -76,7 +75,7 @@ def adx_command( df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") ta_data = trend_indicators_model.adx( df_stock["High"], @@ -107,7 +106,7 @@ def adx_command( row_width=[0.4, 0.6], specs=[[{"secondary_y": True}], [{"secondary_y": False}]], ) - title = f"{plot['plt_title']} Average Directional Movement Index" + title = f"{plot['plt_title']} Average Directional Movement Index" fig = plot["fig"] fig.add_trace( @@ -115,7 +114,7 @@ def adx_command( name=f"ADX ({length})", mode="lines", x=df_ta.index, - y=df_ta[f"ADX_{length}"], + y=df_ta.iloc[:, 6].values, opacity=1, line=dict(width=2), ), @@ -128,7 +127,7 @@ def adx_command( name=f"+DI ({length})", mode="lines", x=df_ta.index, - y=df_ta[f"DMP_{length}"], + y=df_ta.iloc[:, 7].values, opacity=1, line=dict(width=1), ), @@ -141,7 +140,7 @@ def adx_command( name=f"-DI ({length})", mode="lines", x=df_ta.index, - y=df_ta[f"DMN_{length}"], + y=df_ta.iloc[:, 8].values, opacity=1, line=dict(width=1), ), @@ -161,26 +160,26 @@ def adx_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, - title_x=0.02, - title_font_size=14, + title_x=0.01, + title_font_size=12, dragmode="pan", ) imagefile = "ta_adx.png" # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Average-Directional-Movement-Index {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/aroon.py b/bots/stocks/technical_analysis/aroon.py old mode 100644 new mode 100755 index e636784cc98..25dfe76ec5f --- a/bots/stocks/technical_analysis/aroon.py +++ b/bots/stocks/technical_analysis/aroon.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import trend_indicators_model from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def aroon_command( """Displays chart with aroon indicator [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "ta aroon %s %s %s %s %s, %s, %s, %s, %s, %s", ticker, @@ -66,7 +65,7 @@ def aroon_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = df_ta.join( @@ -96,14 +95,14 @@ def aroon_command( [{"secondary_y": False}], ], ) - title = f"{plot['plt_title']} Aroon ({length})" + title = f"{plot['plt_title']} Aroon ({length})" fig = plot["fig"] fig.add_trace( go.Scatter( name="Aroon DOWN", x=df_ta.index, - y=df_ta[f"AROOND_{length}"], + y=df_ta.iloc[:, 6].values, opacity=1, ), row=2, @@ -114,7 +113,7 @@ def aroon_command( go.Scatter( name="Aroon UP", x=df_ta.index, - y=df_ta[f"AROONU_{length}"], + y=df_ta.iloc[:, 7].values, opacity=1, ), row=2, @@ -125,7 +124,7 @@ def aroon_command( go.Scatter( name="Aroon OSC", x=df_ta.index, - y=df_ta[f"AROONOSC_{length}"], + y=df_ta.iloc[:, 8].values, opacity=1, ), row=3, @@ -144,8 +143,8 @@ def aroon_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -157,14 +156,14 @@ def aroon_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Aroon-Indicator {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/bbands.py b/bots/stocks/technical_analysis/bbands.py old mode 100644 new mode 100755 index c7394932633..8e67f2fbfb6 --- a/bots/stocks/technical_analysis/bbands.py +++ b/bots/stocks/technical_analysis/bbands.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import volatility_model from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def bbands_command( """Displays chart with bollinger bands [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta bbands %s %s %s %s %s %s %s %s %s %s", @@ -88,7 +87,7 @@ def bbands_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = df_ta.join( @@ -100,14 +99,14 @@ def bbands_command( df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)] plot = load_candle.candle_fig(df_ta, ticker, interval, extended_hours, news) - title = f"{plot['plt_title']} Bollinger Bands ({ma_mode.upper()})" + title = f"{plot['plt_title']} Bollinger Bands ({ma_mode.upper()})" fig = plot["fig"] fig.add_trace( go.Scatter( name=f"BBU_{length}_{n_std}", x=df_ta.index, - y=df_ta[f"BBU_{length}_{n_std}"], + y=df_ta.iloc[:, 8].values, opacity=1, mode="lines", line_color="indigo", @@ -120,7 +119,7 @@ def bbands_command( go.Scatter( name=f"BBL_{length}_{n_std}", x=df_ta.index, - y=df_ta[f"BBL_{length}_{n_std}"], + y=df_ta.iloc[:, 6].values, opacity=1, mode="lines", line_color="indigo", @@ -135,7 +134,7 @@ def bbands_command( go.Scatter( name=f"BBM_{length}_{n_std}", x=df_ta.index, - y=df_ta[f"BBM_{length}_{n_std}"], + y=df_ta.iloc[:, 7].values, opacity=1, line=dict( width=1.5, @@ -148,8 +147,8 @@ def bbands_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -159,15 +158,15 @@ def bbands_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Bollinger-Bands {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/cci.py b/bots/stocks/technical_analysis/cci.py old mode 100644 new mode 100755 index 750f6054282..463ff41695e --- a/bots/stocks/technical_analysis/cci.py +++ b/bots/stocks/technical_analysis/cci.py @@ -1,8 +1,8 @@ import logging import plotly.graph_objects as go -from bots import helpers, load_candle -import bots.config_discordbot as cfg + +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import momentum_model from gamestonk_terminal.decorators import log_start_end @@ -26,7 +26,7 @@ def cci_command( """Displays chart with commodity channel index [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "ta cci %s %s %s %s %s %s %s %s %s %s", ticker, @@ -57,7 +57,7 @@ def cci_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") # pylint try: @@ -72,7 +72,7 @@ def cci_command( df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") ta_data = momentum_model.cci( df_ta["High"], df_ta["Low"], df_ta["Adj Close"], length, scalar @@ -98,17 +98,17 @@ def cci_command( row_width=[0.4, 0.6], specs=[[{"secondary_y": True}], [{"secondary_y": False}]], ) - title = f"{plot['plt_title']} Commodity-Channel-Index" + title = f"{plot['plt_title']} Commodity-Channel-Index" fig = plot["fig"] - dmin = df_ta[f"CCI_{length}_{scalar}"].values.min() - dmax = df_ta[f"CCI_{length}_{scalar}"].values.max() + dmin = df_ta.iloc[:, 6].values.values.min() + dmax = df_ta.iloc[:, 6].values.values.max() fig.add_trace( go.Scatter( name=f"CCI ({length}) ({scalar})", mode="lines", x=df_ta.index, - y=df_ta[f"CCI_{length}_{scalar}"], + y=df_ta.iloc[:, 6].values, opacity=1, ), row=2, @@ -156,8 +156,8 @@ def cci_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.02, title_font_size=14, @@ -167,15 +167,15 @@ def cci_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Commodity-Channel-Index {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/cg.py b/bots/stocks/technical_analysis/cg.py old mode 100644 new mode 100755 index 9570ecad72a..49b96659aaf --- a/bots/stocks/technical_analysis/cg.py +++ b/bots/stocks/technical_analysis/cg.py @@ -5,9 +5,8 @@ from datetime import datetime, timedelta import numpy as np from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -import bots.helpers -from bots.helpers import image_border +from bots import config_discordbot as cfg +from bots.helpers import image_border, load from gamestonk_terminal.common.technical_analysis import momentum_model from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end @@ -49,7 +48,7 @@ def cg_command(ticker="", length="14", start="", end=""): length = float(length) ticker = ticker.upper() - df_stock = bots.helpers.load(ticker, start) + df_stock = load(ticker, start) if df_stock.empty: raise Exception("Stock ticker is invalid") @@ -63,17 +62,17 @@ def cg_command(ticker="", length="14", start="", end=""): fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI) ax = axes[0] ax.set_title(f"{ticker} Centre of Gravity") - ax.plot(df_stock.index, df_stock["Adj Close"].values, "k", lw=1) + ax.plot(df_stock.index, df_stock["Adj Close"].values, lw=1) ax.set_xlim(df_stock.index[0], df_stock.index[-1]) ax.set_ylabel("Share Price ($)") ax.grid(b=True, which="major", color="#666666", linestyle="-") ax2 = axes[1] - ax2.plot(df_ta.index, df_ta.values, "b", lw=2, label="CG") + ax2.plot(df_ta.index, df_ta.values, lw=2, label="CG") # shift cg 1 bar forward for signal signal = df_ta.values signal = np.roll(signal, 1) - ax2.plot(df_ta.index, signal, "g", lw=1, label="Signal") + ax2.plot(df_ta.index, signal, lw=1, label="Signal") ax2.set_xlim(df_stock.index[0], df_stock.index[-1]) ax2.grid(b=True, which="major", color="#666666", linestyle="-") @@ -83,6 +82,7 @@ def cg_command(ticker="", length="14", start="", end=""): imagefile = "ta_cg.png" dataBytesIO = io.BytesIO() plt.savefig(dataBytesIO) + plt.close("all") dataBytesIO.seek(0) imagefile = image_border(imagefile, base64=dataBytesIO) diff --git a/bots/stocks/technical_analysis/donchian.py b/bots/stocks/technical_analysis/donchian.py old mode 100644 new mode 100755 index 90e00296264..58bde040c32 --- a/bots/stocks/technical_analysis/donchian.py +++ b/bots/stocks/technical_analysis/donchian.py @@ -4,9 +4,8 @@ from datetime import datetime, timedelta from matplotlib import pyplot as plt -import bots.config_discordbot as cfg -import bots.helpers -from bots.helpers import image_border +from bots import config_discordbot as cfg +from bots.helpers import image_border, load from gamestonk_terminal.common.technical_analysis import volatility_model from gamestonk_terminal.config_plot import PLOT_DPI from gamestonk_terminal.decorators import log_start_end @@ -54,7 +53,7 @@ def donchian_command( lower_length = float(lower_length) ticker = ticker.upper() - df_stock = bots.helpers.load(ticker, start) + df_stock = load(ticker, start) if df_stock.empty: raise Exception("Stock ticker is invalid") @@ -67,10 +66,10 @@ def donchian_command( # Output Data fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) - ax.plot(df_stock.index, df_stock["Adj Close"].values, color="k", lw=3) - ax.plot(df_ta.index, df_ta.iloc[:, 0].values, "b", lw=1.5, label="upper") - ax.plot(df_ta.index, df_ta.iloc[:, 1].values, "b", lw=1.5, ls="--") - ax.plot(df_ta.index, df_ta.iloc[:, 2].values, "b", lw=1.5, label="lower") + ax.plot(df_stock.index, df_stock["Adj Close"].values, lw=3) + ax.plot(df_ta.index, df_ta.iloc[:, 0].values, lw=1.5, label="upper") + ax.plot(df_ta.index, df_ta.iloc[:, 1].values, lw=1.5, ls="--") + ax.plot(df_ta.index, df_ta.iloc[:, 2].values, lw=1.5, label="lower") ax.set_title(f"{ticker} donchian") ax.set_xlim(df_stock.index[0], df_stock.index[-1]) ax.set_xlabel("Time") @@ -82,7 +81,6 @@ def donchian_command( df_ta.iloc[:, 0].values, df_ta.iloc[:, 2].values, alpha=0.1, - color="b", ) ax.grid(b=True, which="major", color="#666666", linestyle="-") @@ -94,8 +92,9 @@ def donchian_command( dataBytesIO = io.BytesIO() plt.savefig(dataBytesIO) - dataBytesIO.seek(0) + plt.close("all") + dataBytesIO.seek(0) imagefile = image_border(imagefile, base64=dataBytesIO) return { diff --git a/bots/stocks/technical_analysis/fib.py b/bots/stocks/technical_analysis/fib.py old mode 100644 new mode 100755 index e58e5320341..281dfdd1eb4 --- a/bots/stocks/technical_analysis/fib.py +++ b/bots/stocks/technical_analysis/fib.py @@ -1,100 +1,184 @@ -import io import logging -from datetime import datetime, timedelta -from matplotlib import pyplot as plt +import plotly.graph_objects as go -import bots.config_discordbot as cfg -import bots.helpers -from bots.helpers import image_border -from gamestonk_terminal import config_plot as cfp +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import custom_indicators_model from gamestonk_terminal.decorators import log_start_end -from gamestonk_terminal.helper_funcs import plot_autoscale logger = logging.getLogger(__name__) @log_start_end(log=logger) -def fib_command(ticker="", start="", end=""): +def fib_command( + ticker="", + interval: int = 15, + past_days: int = 0, + start: str = "", + end: str = "", + extended_hours: bool = False, + heikin_candles: bool = False, + news: bool = False, +): """Displays chart with fibonacci retracement [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: + # pylint: disable=logging-too-many-args logger.debug( - "ta-fib %s %s %s", + "ta fib %s %s %s %s %s %s %s %s", ticker, + interval, + past_days, start, end, + extended_hours, + heikin_candles, + news, ) - # Check for argument - if ticker == "": - raise Exception("Stock ticker is required") - - if start == "": - start = datetime.now() - timedelta(days=365) - f_start = None - else: - start = datetime.strptime(start, cfg.DATE_FORMAT) - f_start = start - - if end == "": - end = datetime.now() - f_end = None - else: - end = datetime.strptime(end, cfg.DATE_FORMAT) - f_end = None - - ticker = ticker.upper() - df_stock = bots.helpers.load(ticker, start) - if df_stock.empty: - raise Exception("Stock ticker is invalid") + past_days = past_days if (interval != 1440) or (start != "") else 365 # Retrieve Data - df_stock = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] + (df_stock, start, end, bar_start,) = load_candle.stock_data( + ticker=ticker, + interval=interval, + past_days=past_days, + extended_hours=extended_hours, + start=start, + end=end, + heikin_candles=heikin_candles, + ) + + if df_stock.empty: + raise Exception(f"No data found for {ticker.upper()}.") + + df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] + + # Output Data + if interval != 1440: + df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)] - start = start.strftime("%Y-%m-%d") - end = end.strftime("%Y-%m-%d") ( df_fib, min_date, max_date, min_pr, max_pr, - ) = custom_indicators_model.calculate_fib_levels(df_stock, 120, f_start, f_end) + ) = custom_indicators_model.calculate_fib_levels(df_ta, 12, bar_start, None) levels = df_fib.Price - fig, ax = plt.subplots(figsize=(plot_autoscale()), dpi=cfp.PLOT_DPI) - ax.plot(df_stock["Adj Close"], "b") - ax.plot([min_date, max_date], [min_pr, max_pr], c="k") + # Output Data + fibs = [ + "0", + "0.235", + "0.382", + "0.5", + "0.618", + "0.65", + "1", + ] + plot = load_candle.candle_fig( + df_ta, + ticker, + interval, + extended_hours, + news, + bar=bar_start, + int_bar=interval, + shared_xaxes=True, + vertical_spacing=0.07, + ) + title = f"{plot['plt_title']} Fibonacci-Retracement-Levels" + lvl_text: str = "right" if min_date > max_date else "left" + fig = plot["fig"] - for i in levels: - ax.axhline(y=i, c="g", alpha=0.5) + fig.add_trace( + go.Scatter( + x=[min_date, max_date], + y=[min_pr, max_pr], + opacity=1, + mode="lines", + line=imps.PLT_FIB_COLORWAY[8], + showlegend=False, + ), + row=1, + col=1, + secondary_y=True, + ) - for i in range(5): - ax.fill_between(df_stock.index, levels[i], levels[i + 1], alpha=0.6) + for i in range(6): + fig.add_trace( + go.Scatter( + name=fibs[i], + x=[min_date, max_date], + y=[levels[i], levels[i]], + opacity=0.2, + mode="lines", + line_color=imps.PLT_FIB_COLORWAY[i], + showlegend=False, + ), + row=1, + col=1, + secondary_y=True, + ) + fig.add_trace( + go.Scatter( + name=fibs[i + 1], + x=[min_date, max_date], + y=[levels[i + 1], levels[i + 1]], + opacity=0.2, + mode="lines", + fill="tonexty", + line_color=imps.PLT_FIB_COLORWAY[i + 1], + showlegend=False, + ), + row=1, + col=1, + secondary_y=True, + ) - ax.set_ylabel("Price") - ax.set_title(f"Fibonacci Support for {ticker.upper()}") - ax.set_xlim(df_stock.index[0], df_stock.index[-1]) - - ax1 = ax.twinx() - ax1.set_ylim(ax.get_ylim()) - ax1.set_yticks(levels) - ax1.set_yticklabels([0, 0.235, 0.382, 0.5, 0.618, 1]) - - plt.gcf().autofmt_xdate() - fig.tight_layout(pad=1) + for i in range(7): + fig.add_trace( + go.Scatter( + name=fibs[i], + x=[min_date], + y=[levels[i]], + opacity=0.9, + mode="text", + text=fibs[i], + textposition=f"middle {lvl_text}" if i != 5 else f"bottom {lvl_text}", + textfont=dict(imps.PLT_FIB_COLORWAY[7], color=imps.PLT_FIB_COLORWAY[i]), + showlegend=False, + ), + row=1, + col=1, + secondary_y=True, + ) + fig.update_layout( + margin=dict(l=0, r=0, t=50, b=20), + template=imps.PLT_TA_STYLE_TEMPLATE, + title=title, + title_x=0.02, + title_font_size=14, + dragmode="pan", + ) imagefile = "ta_fib.png" - dataBytesIO = io.BytesIO() - plt.savefig(dataBytesIO) - dataBytesIO.seek(0) - imagefile = image_border(imagefile, base64=dataBytesIO) + # Check if interactive settings are enabled + plt_link = "" + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) + + fig.update_layout( + width=800, + height=500, + ) + imagefile = imps.image_border(imagefile, fig=fig) return { - "title": f"Stocks: Fibonacci-Retracement-Levels {ticker}", + "title": f"Stocks: Fibonacci-Retracement-Levels {ticker.upper()}", + "description": plt_link, "imagefile": imagefile, } diff --git a/bots/stocks/technical_analysis/fisher.py b/bots/stocks/technical_analysis/fisher.py old mode 100644 new mode 100755 index 49de9968896..3dc8ab7c090 --- a/bots/stocks/technical_analysis/fisher.py +++ b/bots/stocks/technical_analysis/fisher.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import momentum_model from gamestonk_terminal.decorators import log_start_end @@ -26,7 +25,7 @@ def fisher_command( """Displays chart with fisher transformation [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "ta fisher %s %s %s %s %s %s %s %s %s", ticker, @@ -56,7 +55,7 @@ def fisher_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") if not length.lstrip("-").isnumeric(): raise Exception("Number has to be an integer") @@ -65,7 +64,7 @@ def fisher_command( df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_ta.join(momentum_model.fisher(df_stock["High"], df_stock["Low"], length)) @@ -88,17 +87,17 @@ def fisher_command( row_width=[0.4, 0.6], specs=[[{"secondary_y": True}], [{"secondary_y": False}]], ) - title = f"{plot['plt_title']} Fisher Transform" + title = f"{plot['plt_title']} Fisher Transform" fig = plot["fig"] - dmin = df_ta[f"FISHERTs_{length}_1"].min() - dmax = df_ta[f"FISHERTs_{length}_1"].max() + dmin = df_ta.iloc[:, 7].values.min() + dmax = df_ta.iloc[:, 7].values.max() fig.add_trace( go.Scatter( name="Fisher", mode="lines", x=df_ta.index, - y=df_ta[f"FISHERT_{length}_1"], + y=df_ta.iloc[:, 6].values, opacity=1, ), row=2, @@ -109,7 +108,7 @@ def fisher_command( name="Signal", mode="lines", x=df_ta.index, - y=df_ta[f"FISHERTs_{length}_1"], + y=df_ta.iloc[:, 7].values, opacity=1, ), row=2, @@ -157,8 +156,8 @@ def fisher_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.02, title_font_size=14, @@ -168,15 +167,15 @@ def fisher_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Fisher-Transform {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/kc.py b/bots/stocks/technical_analysis/kc.py old mode 100644 new mode 100755 index df7a2ebe755..99a95454ac5 --- a/bots/stocks/technical_analysis/kc.py +++ b/bots/stocks/technical_analysis/kc.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import volatility_model from gamestonk_terminal.decorators import log_start_end @@ -29,7 +28,7 @@ def kc_command( """Displays chart with keltner channel [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta kc %s %s %s %s %s %s %s %s %s %s %s %s", @@ -70,12 +69,12 @@ def kc_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") ta_data = volatility_model.kc( df_stock["High"], @@ -103,14 +102,14 @@ def kc_command( shared_xaxes=True, vertical_spacing=0.07, ) - title = f"{plot['plt_title']} Keltner Channels ({ma_mode.upper()})" + title = f"{plot['plt_title']} Keltner Channels ({ma_mode.upper()})" fig = plot["fig"] fig.add_trace( go.Scatter( name="upper", x=df_ta.index, - y=df_ta[f"KCUs_{length}_{scalar}"], + y=df_ta.iloc[:, 8].values, opacity=1, mode="lines", line_color="indigo", @@ -124,7 +123,7 @@ def kc_command( go.Scatter( name="lower", x=df_ta.index, - y=df_ta[f"KCLs_{length}_{scalar}"], + y=df_ta.iloc[:, 6].values, opacity=1, mode="lines", line_color="indigo", @@ -140,7 +139,7 @@ def kc_command( go.Scatter( name="mid", x=df_ta.index, - y=df_ta[f"KCBs_{length}_{scalar}"], + y=df_ta.iloc[:, 7].values, opacity=1, line=dict( width=1.5, @@ -154,8 +153,8 @@ def kc_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.02, title_font_size=14, @@ -165,14 +164,14 @@ def kc_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Keltner-Channel {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/ma.py b/bots/stocks/technical_analysis/ma.py old mode 100644 new mode 100755 index f61bd63334d..553f6ab0e9b --- a/bots/stocks/technical_analysis/ma.py +++ b/bots/stocks/technical_analysis/ma.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import overlap_model from gamestonk_terminal.decorators import log_start_end @@ -27,7 +26,7 @@ def ma_command( ): """Displays chart with selected Moving Average [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta ma %s %s %s %s %s %s %s %s %s %s %s", @@ -68,7 +67,7 @@ def ma_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") if window == "": window = [20, 50] @@ -76,7 +75,7 @@ def ma_command( window_temp = list() for wind in window.split(","): try: - window_temp.append(float(wind)) + window_temp.append(int(wind)) except Exception as e: raise Exception("Window needs to be a float") from e window = window_temp @@ -84,15 +83,13 @@ def ma_command( df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") for win in window: ema_data = overlap_ma[ma_mode]( values=df_ta["Adj Close"], length=win, offset=offset ) df_ta = df_ta.join(ema_data) - if ma_mode == "zlma": - ma_mode = "ZL_EMA" # Output Data if interval != 1440: @@ -107,16 +104,16 @@ def ma_command( bar=bar_start, int_bar=interval, ) - title = f"{plot['plt_title']} Moving Average ({ma_mode.upper()})" + title = f"{plot['plt_title']} Moving Average ({ma_mode.upper()})" fig = plot["fig"] - for win in window: + for i in range(6, df_ta.shape[1]): fig.add_trace( go.Scatter( - name=f"{ma_mode.upper()} {win}", + name=f"{df_ta.iloc[:, i].name}", mode="lines", x=df_ta.index, - y=df_ta[f"{ma_mode.upper()}_{win}"], + y=df_ta.iloc[:, i].values, opacity=1, ), secondary_y=True, @@ -126,8 +123,8 @@ def ma_command( fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -137,15 +134,14 @@ def ma_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Moving Average {ma_mode.upper()}", diff --git a/bots/stocks/technical_analysis/macd.py b/bots/stocks/technical_analysis/macd.py old mode 100644 new mode 100755 index 47aab46bea0..05ab56b9b9e --- a/bots/stocks/technical_analysis/macd.py +++ b/bots/stocks/technical_analysis/macd.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import momentum_model from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def macd_command( """Displays chart with moving average convergence/divergence [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta macd %s %s %s %s %s %s %s %s %s %s %s", @@ -61,7 +60,7 @@ def macd_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") if not fast.lstrip("-").isnumeric(): raise Exception("Number has to be an integer") @@ -76,7 +75,7 @@ def macd_command( df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") ta_data = momentum_model.macd(df_stock["Adj Close"], fast, slow, signal) df_ta = df_ta.join(ta_data) @@ -100,14 +99,14 @@ def macd_command( row_width=[0.4, 0.6], specs=[[{"secondary_y": True}], [{"secondary_y": False}]], ) - title = f"{plot['plt_title']} MACD {fast} {slow} {signal}" + title = f"{plot['plt_title']} MACD {fast} {slow} {signal}" fig = plot["fig"] fig.add_trace( go.Bar( name="MACD Histogram", x=df_ta.index, - y=df_ta[f"MACDh_{fast}_{slow}_{signal}"], + y=df_ta.iloc[:, 7].values, opacity=(plot["bar_opacity"] + 0.3), marker_color="#d81aea", ), @@ -120,7 +119,7 @@ def macd_command( name="MACD Line", mode="lines", x=df_ta.index, - y=df_ta[f"MACD_{fast}_{slow}_{signal}"], + y=df_ta.iloc[:, 6].values, opacity=0.8, line=dict(color="#00e6c3"), ), @@ -133,7 +132,7 @@ def macd_command( name="Signal Line", mode="lines", x=df_ta.index, - y=df_ta[f"MACDs_{fast}_{slow}_{signal}"], + y=df_ta.iloc[:, 8].values, opacity=1, line=dict(color="#9467bd"), ), @@ -143,8 +142,8 @@ def macd_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.02, title_font_size=14, @@ -154,15 +153,15 @@ def macd_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Moving-Average-Convergence-Divergence {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/obv.py b/bots/stocks/technical_analysis/obv.py old mode 100644 new mode 100755 index 7ba6d154b45..24981fb9654 --- a/bots/stocks/technical_analysis/obv.py +++ b/bots/stocks/technical_analysis/obv.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import volume_model from gamestonk_terminal.decorators import log_start_end @@ -24,7 +23,7 @@ def obv_command( """Displays chart with on balance volume [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "ta obv %s %s %s %s %s %s %s", ticker, @@ -52,7 +51,7 @@ def obv_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = df_ta.join(volume_model.obv(df_ta)) @@ -79,7 +78,7 @@ def obv_command( [{"secondary_y": False}], ], ) - title = f"{plot['plt_title']} OBV" + title = f"{plot['plt_title']} OBV" fig = plot["fig"] fig.add_trace( @@ -87,7 +86,7 @@ def obv_command( name="OBV", mode="lines", x=df_ta.index, - y=df_ta["OBV"], + y=df_ta.iloc[:, 6].values, line=dict(width=2), opacity=1, ), @@ -97,8 +96,8 @@ def obv_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -110,15 +109,15 @@ def obv_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: On-Balance-Volume {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/recom.py b/bots/stocks/technical_analysis/recom.py index 74dbe7c28c4..42f96b4193b 100644 --- a/bots/stocks/technical_analysis/recom.py +++ b/bots/stocks/technical_analysis/recom.py @@ -1,6 +1,6 @@ import logging -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.technical_analysis import tradingview_model diff --git a/bots/stocks/technical_analysis/rsi.py b/bots/stocks/technical_analysis/rsi.py old mode 100644 new mode 100755 index cd9faa5db36..fefea6bf1fd --- a/bots/stocks/technical_analysis/rsi.py +++ b/bots/stocks/technical_analysis/rsi.py @@ -1,8 +1,8 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle + +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import momentum_model from gamestonk_terminal.decorators import log_start_end @@ -27,7 +27,7 @@ def rsi_command( """Displays chart with relative strength index [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: # pylint: disable=logging-too-many-args logger.debug( "ta rsi %s %s %s %s %s %s", @@ -69,7 +69,7 @@ def rsi_command( ) if df_stock.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = df_ta.join(momentum_model.rsi(df_ta["Adj Close"], length, scalar, drift)) @@ -96,7 +96,7 @@ def rsi_command( [{"secondary_y": False}], ], ) - title = f"{plot['plt_title']} RSI" + title = f"{plot['plt_title']} RSI" fig = plot["fig"] fig.add_trace( @@ -104,7 +104,7 @@ def rsi_command( name=f"RSI {length}", mode="lines", x=df_ta.index, - y=df_ta[f"RSI_{length}"], + y=df_ta.iloc[:, 6].values, opacity=1, ), row=2, @@ -153,8 +153,8 @@ def rsi_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.1, title_font_size=14, @@ -166,15 +166,15 @@ def rsi_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Relative-Strength-Index {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/stoch.py b/bots/stocks/technical_analysis/stoch.py old mode 100644 new mode 100755 index fa5226b2892..5b2acb3ddab --- a/bots/stocks/technical_analysis/stoch.py +++ b/bots/stocks/technical_analysis/stoch.py @@ -2,8 +2,7 @@ import logging import plotly.graph_objects as go -import bots.config_discordbot as cfg -from bots import helpers, load_candle +from bots import imps, load_candle from gamestonk_terminal.common.technical_analysis import momentum_model from gamestonk_terminal.decorators import log_start_end @@ -28,7 +27,7 @@ def stoch_command( """Displays chart with stochastic relative strength average [Yahoo Finance]""" # Debug - if cfg.DEBUG: + if imps.DEBUG: logger.debug( "ta stoch %s %s %s %s %s %s %s %s %s %s %s", ticker, @@ -72,7 +71,7 @@ def stoch_command( df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] if df_ta.empty: - return Exception("No Data Found") + raise Exception("No Data Found") df_ta = df_ta.join( momentum_model.stoch( @@ -104,14 +103,14 @@ def stoch_command( row_width=[0.4, 0.6], specs=[[{"secondary_y": True}], [{"secondary_y": False}]], ) - title = f"{plot['plt_title']} STOCH RSI" + title = f"{plot['plt_title']} STOCH RSI" fig = plot["fig"] fig.add_trace( go.Scatter( name=f"%K {fast_k}, {slow_d}, {slow_k}", x=df_ta.index, - y=df_ta[f"STOCHk_{fast_k}_{slow_d}_{slow_k}"], + y=df_ta.iloc[:, 6].values, line=dict(width=1.8), mode="lines", opacity=1, @@ -123,7 +122,7 @@ def stoch_command( go.Scatter( name=f"%D {fast_k}, {slow_d}, {slow_k}", x=df_ta.index, - y=df_ta[f"STOCHd_{fast_k}_{slow_d}_{slow_k}"], + y=df_ta.iloc[:, 7].values, line=dict(width=1.8, dash="dash"), opacity=1, ), @@ -172,8 +171,8 @@ def stoch_command( ) fig.update_layout( margin=dict(l=0, r=0, t=50, b=20), - template=cfg.PLT_TA_STYLE_TEMPLATE, - colorway=cfg.PLT_TA_COLORWAY, + template=imps.PLT_TA_STYLE_TEMPLATE, + colorway=imps.PLT_TA_COLORWAY, title=title, title_x=0.01, title_font_size=14, @@ -183,14 +182,14 @@ def stoch_command( # Check if interactive settings are enabled plt_link = "" - if cfg.INTERACTIVE: - plt_link = helpers.inter_chart(fig, imagefile, callback=False) + if imps.INTERACTIVE: + plt_link = imps.inter_chart(fig, imagefile, callback=False) fig.update_layout( width=800, height=500, ) - imagefile = helpers.image_border(imagefile, fig=fig) + imagefile = imps.image_border(imagefile, fig=fig) return { "title": f"Stocks: Stochastic-Relative-Strength-Index {ticker.upper()}", diff --git a/bots/stocks/technical_analysis/summary.py b/bots/stocks/technical_analysis/summary.py index 46e1633a5ef..dde6a55151c 100644 --- a/bots/stocks/technical_analysis/summary.py +++ b/bots/stocks/technical_analysis/summary.py @@ -1,6 +1,6 @@ import logging -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.technical_analysis import finbrain_model diff --git a/bots/stocks/technical_analysis/view.py b/bots/stocks/technical_analysis/view.py old mode 100644 new mode 100755 index 96d80173a3d..7867572d0a8 --- a/bots/stocks/technical_analysis/view.py +++ b/bots/stocks/technical_analysis/view.py @@ -3,7 +3,7 @@ import logging from PIL import Image -import bots.config_discordbot as cfg +from bots import config_discordbot as cfg from bots.helpers import image_border from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.stocks.technical_analysis import finviz_model diff --git a/gamestonk_terminal/api.py b/gamestonk_terminal/api.py index de8edb6cd7a..0f3b52d9ee4 100644 --- a/gamestonk_terminal/api.py +++ b/gamestonk_terminal/api.py @@ -10,5 +10,6 @@ from .etf import etf_api as etf from .forex import forex_api as forex from .mutual_funds import mutual_fund_api as funds from .portfolio import portfolio_api as portfolio +from .jupyter import widget_helpers as widgets from .config_terminal import theme diff --git a/gamestonk_terminal/common/quantitative_analysis/qa_view.py b/gamestonk_terminal/common/quantitative_analysis/qa_view.py index 28ef4d1a138..6d3a480d939 100644 --- a/gamestonk_terminal/common/quantitative_analysis/qa_view.py +++ b/gamestonk_terminal/common/quantitative_analysis/qa_view.py @@ -980,12 +980,19 @@ def display_line( s=100, ) + data_type = data.name + ax.set_ylabel(data_type) ax.set_xlim(data.index[0], data.index[-1]) + ax.ticklabel_format(style="plain", axis="y") + ax.get_yaxis().set_major_formatter( + matplotlib.ticker.FuncFormatter(lambda x, _: lambda_long_number_format(x)) + ) if title: ax.set_title(title) if draw: LineAnnotateDrawer(ax).draw_lines_and_annotate() + theme.style_primary_axis(ax) if external_axes is None: diff --git a/gamestonk_terminal/common/technical_analysis/custom_indicators_model.py b/gamestonk_terminal/common/technical_analysis/custom_indicators_model.py index 8181fb1a261..218001750d8 100644 --- a/gamestonk_terminal/common/technical_analysis/custom_indicators_model.py +++ b/gamestonk_terminal/common/technical_analysis/custom_indicators_model.py @@ -77,7 +77,7 @@ def calculate_fib_levels( max_pr = data_to_use.max() max_date = data_to_use.idxmax() - fib_levels = [0, 0.235, 0.382, 0.5, 0.618, 1] + fib_levels = [0, 0.235, 0.382, 0.5, 0.618, 0.65, 1] price_dif = max_pr - min_pr levels = [round(max_pr - price_dif * f_lev, 2) for f_lev in fib_levels] diff --git a/gamestonk_terminal/common/technical_analysis/custom_indicators_view.py b/gamestonk_terminal/common/technical_analysis/custom_indicators_view.py index 34026105255..08e140548ad 100644 --- a/gamestonk_terminal/common/technical_analysis/custom_indicators_view.py +++ b/gamestonk_terminal/common/technical_analysis/custom_indicators_view.py @@ -99,13 +99,13 @@ def fibonacci_retracement( for i in levels: ax1.axhline(y=i, alpha=0.5) - for i in range(5): + for i in range(6): ax1.fill_between(plot_data.index, levels[i], levels[i + 1], alpha=0.15) ax1.set_xlim(plot_data.index[0], plot_data.index[-1]) ax1.set_title(f"Fibonacci Support for {s_ticker.upper()}") ax1.set_yticks(levels) - ax1.set_yticklabels([0, 0.235, 0.382, 0.5, 0.618, 1]) + ax1.set_yticklabels([0, 0.235, 0.382, 0.5, 0.618, 0.65, 1]) theme.style_primary_axis( ax1, data_index=plot_data.index.to_list(), diff --git a/gamestonk_terminal/common/technical_analysis/trend_indicators_view.py b/gamestonk_terminal/common/technical_analysis/trend_indicators_view.py index d8a5910cc90..d70385da53c 100644 --- a/gamestonk_terminal/common/technical_analysis/trend_indicators_view.py +++ b/gamestonk_terminal/common/technical_analysis/trend_indicators_view.py @@ -77,7 +77,7 @@ def display_adx( ax1.plot(plot_data.index, plot_data["Close"].values) ax1.set_title(f"Average Directional Movement Index (ADX) on {s_ticker}") ax1.set_xlim(plot_data.index[0], plot_data.index[-1]) - ax1.set_ylabel("Share Price ($)") + ax1.set_ylabel("Price") theme.style_primary_axis( ax1, data_index=plot_data.index.to_list(), @@ -167,7 +167,7 @@ def display_aroon( ax1.plot(plot_data.index, plot_data["Adj Close"].values) ax1.set_title(f"Aroon on {s_ticker}") ax1.set_xlim(plot_data.index[0], plot_data.index[-1]) - ax1.set_ylabel("Share Price ($)") + ax1.set_ylabel("Price") theme.style_primary_axis( ax1, data_index=plot_data.index.to_list(), diff --git a/gamestonk_terminal/config_terminal.py b/gamestonk_terminal/config_terminal.py index a5b73ac65f7..b123a2c4428 100644 --- a/gamestonk_terminal/config_terminal.py +++ b/gamestonk_terminal/config_terminal.py @@ -24,29 +24,21 @@ PAPERMILL_NOTEBOOK_REPORT_PORT = ( # Logging section -# Logging settings -# 0 - NOTSET -# 10 - DEBUG for terminal, DEBUG for libraries -# 15 - DEBUG for terminal, INFO for libraries -# 20 - INFO - - -LOGGING_VERBOSITY = 20 - -if tmp_verbosity := os.getenv("GT_LOGGING_VERBOSITY"): - try: - LOGGING_VERBOSITY = int(tmp_verbosity) - except ValueError: - LOGGING_VERBOSITY = 20 - -# stdout, stderr, file, noop +# USE IN LOG LINES + FOR FOLDER NAME INSIDE S3 BUCKET +LOGGING_APP_NAME = os.getenv("GT_LOGGING_APP_NAME") or "gst" +# D | H | M | S +LOGGING_FREQUENCY = os.getenv("GT_LOGGING_FREQUENCY") or "H" +# stdout,stderr,noop,file LOGGING_HANDLERS = os.getenv("GT_LOGGING_HANDLERS") or "file" - -LOGGING_ID = os.getenv("GT_LOGGING_ID") or None - -LOGGING_FILE = "" - -LOGGING_VERSION = os.getenv("GT_LOGGING_VERSION") or "ver:1.0.0" +# CRITICAL = 50 +# FATAL = CRITICAL +# ERROR = 40 +# WARNING = 30 +# WARN = WARNING +# INFO = 20 +# DEBUG = 10 +# NOTSET = 0 +LOGGING_VERBOSITY = int(os.getenv("GT_LOGGING_VERBOSITY") or 20) # API Keys section diff --git a/gamestonk_terminal/cryptocurrency/crypto_api.py b/gamestonk_terminal/cryptocurrency/crypto_api.py index bef76b3a1fc..8688547a929 100644 --- a/gamestonk_terminal/cryptocurrency/crypto_api.py +++ b/gamestonk_terminal/cryptocurrency/crypto_api.py @@ -1,4 +1,7 @@ """Crypto context API.""" +import os +from gamestonk_terminal.helper_classes import ModelsNamespace as _models + # flake8: noqa # pylint: disable=unused-import @@ -7,6 +10,7 @@ from .cryptocurrency_helpers import display_all_coins as coins from .cryptocurrency_helpers import load from .cryptocurrency_helpers import find from .cryptocurrency_helpers import plot_chart as chart +from .cryptocurrency_helpers import plot_candles as candle # Context menus from .defi import defi_api as defi @@ -25,3 +29,6 @@ try: except Exception: # print("Prediction API is not available.") pass + +# Models +models = _models(os.path.abspath(os.path.dirname(__file__))) diff --git a/gamestonk_terminal/cryptocurrency/cryptocurrency_helpers.py b/gamestonk_terminal/cryptocurrency/cryptocurrency_helpers.py index 04cbbeb7c46..af6e9ad488a 100644 --- a/gamestonk_terminal/cryptocurrency/cryptocurrency_helpers.py +++ b/gamestonk_terminal/cryptocurrency/cryptocurrency_helpers.py @@ -1008,7 +1008,7 @@ def plot_candles( } # This plot has 2 axes - if not external_axes: + if external_axes is None: candle_chart_kwargs["returnfig"] = True candle_chart_kwargs["figratio"] = (10, 7) candle_chart_kwargs["figscale"] = 1.10 @@ -1036,7 +1036,7 @@ def plot_candles( (ax, volume) = external_axes candle_chart_kwargs["volume"] = volume else: - ax = external_axes + ax = external_axes[0] candle_chart_kwargs["ax"] = ax diff --git a/gamestonk_terminal/economy/econdb_model.py b/gamestonk_terminal/economy/econdb_model.py index acf53ef0962..18a1d78a78d 100644 --- a/gamestonk_terminal/economy/econdb_model.py +++ b/gamestonk_terminal/economy/econdb_model.py @@ -4,13 +4,15 @@ __docformat__ = "numpy" # pylint: disable=no-member import logging -from typing import Dict, Any +from typing import Dict, Any, Tuple, Union from urllib.error import HTTPError from datetime import datetime import pandas as pd import pandas_datareader.data as web +import requests import yfinance as yf +from pandas import DataFrame from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.rich_config import console @@ -202,246 +204,216 @@ COUNTRY_CURRENCIES = { PARAMETERS = { "RGDP": { "name": "Real gross domestic product", - "scale": "Millions", "period": "Quarterly", "description": "Inflation-adjusted measure that reflects the value of all goods and services produced by " "an economy in a given year (chain-linked series).", }, "RPRC": { "name": "Real private consumption", - "scale": "Millions", "period": "Quarterly", "description": "All purchases made by consumers adjusted by inflation (chain-linked series).", }, "RPUC": { "name": "Real public consumption", - "scale": "Millions", "period": "Quarterly", "description": "All purchases made by the government adjusted by inflation (chain-linked series).", }, "RGFCF": { "name": "Real gross fixed capital formation", - "scale": "Millions", "period": "Quarterly", "description": "The acquisition of produced assets adjusted by inflation (chain-linked series).", }, "REXP": { "name": "Real exports of goods and services", - "scale": "Millions", "period": "Quarterly", "description": "Transactions in goods and services from residents to non-residents adjusted for " "inflation (chain-linked series)", }, "RIMP": { "name": "Real imports of goods and services", - "scale": "Millions", "period": "Quarterly", "description": "Transactions in goods and services to residents from non-residents adjusted for " "inflation (chain-linked series)", }, "GDP": { "name": "Gross domestic product", - "scale": "Millions", "period": "Quarterly", "description": "Measure that reflects the value of all goods and services produced by " "an economy in a given year (chain-linked series).", }, "PRC": { "name": "Private consumption", - "scale": "Millions", "period": "Quarterly", "description": "All purchases made by consumers (chain-linked series).", }, "PUC": { "name": "Public consumption", - "scale": "Millions", "period": "Quarterly", "description": "All purchases made by the government (chain-linked series)", }, "GFCF": { "name": "Gross fixed capital formation", - "scale": "Millions", "period": "Quarterly", "description": "The acquisition of produced assets (chain-linked series).", }, "EXP": { "name": "Exports of goods and services", - "scale": "Millions", "period": "Quarterly", "description": "Transactions in goods and services from residents to non-residents (chain-linked series)", }, "IMP": { "name": "Imports of goods and services", - "scale": "Millions", "period": "Quarterly", "description": "Transactions in goods and services to residents from non-residents (chain-linked series)", }, "CPI": { "name": "Consumer price index", - "scale": "Index", "period": "Monthly", "description": "Purchasing power defined with base 2015 for Europe with varying bases for others. See: " "https://www.econdb.com/main-indicators", }, "PPI": { "name": "Producer price index", - "scale": "Index", "period": "Monthly", "description": "Change in selling prices with base 2015 for Europe with varying bases for others. See: " "https://www.econdb.com/main-indicators", }, "CORE": { "name": "Core consumer price index", - "scale": "Index", "period": "Monthly", "description": "Purchasing power excluding food and energy defined with base 2015 for Europe with varying " "bases for others. See: https://www.econdb.com/main-indicators", }, "URATE": { "name": "Unemployment", - "scale": "Percentage", "period": "Monthly", "description": "Monthly average % of the working-age population that is unemployed.", }, "EMP": { "name": "Employment", - "scale": "Thousands", "period": "Quarterly", "description": "The employed population within a country (in thousands).", }, "ACOIO": { "name": "Active population", - "scale": "Thousands", "period": "Quarterly", "description": "The active population, unemployed and employed, in thousands.", }, "EMRATIO": { "name": "Employment to working age population", - "scale": "Percentage", "period": "Quarterly", "description": "Unlike the unemployment rate, the employment-to-population ratio includes unemployed " "people not looking for jobs.", }, "RETA": { "name": "Retail trade", - "scale": "Index", "period": "Monthly", "description": "Turnover of sales in wholesale and retail trade", }, "CONF": { "name": "Consumer confidence index", - "scale": "Index", "period": "Monthly", "description": "Measures how optimistic or pessimistic consumers are regarding their expected financial " "situation.", }, "IP": { "name": "Industrial production", - "scale": "Index", "period": "Monthly", "description": "Measures monthly changes in the price-adjusted output of industry.", }, "CP": { "name": "Construction production", - "scale": "Index", "period": "Monthly", "description": "Measures monthly changes in the price-adjusted output of construction.", }, "GBAL": { "name": "Government balance", - "scale": "Millions", "period": "Quarterly", "description": "The government balance (or EMU balance) is the overall difference between government " "revenues and spending.", }, "GREV": { "name": "General government total revenue", - "scale": "Millions", "period": "Quarterly", "description": "The total amount of revenues collected by governments is determined by past and " "current political decisions.", }, "GSPE": { "name": "General government total expenditure", - "scale": "Millions", "period": "Quarterly", "description": "Total expenditure consists of total expense and the net acquisition of " "non-financial assets. ", }, "GDEBT": { "name": "Government debt", - "scale": "Millions", "period": "Quarterly", "description": "The financial liabilities of the government.", }, "CA": { "name": "Current account balance", - "scale": "Millions", "period": "Monthly", "description": "A record of a country's international transactions with the rest of the world", }, "TBFR": { "name": "Trade balance", - "scale": "Millions", "period": "Monthly", "description": "The difference between the monetary value of a nation's exports and imports over a " "certain time period.", }, "NIIP": { "name": "Net international investment position", - "scale": "Millions", "period": "Quarterly", "description": "Measures the gap between a nation's stock of foreign assets and a foreigner's stock " "of that nation's assets", }, "IIPA": { "name": "Net international investment position (Assets)", - "scale": "Millions", "period": "Quarterly", "description": "A nation's stock of foreign assets.", }, "IIPL": { "name": "Net international investment position (Liabilities)", - "scale": "Millions", "period": "Quarterly", "description": "A foreigner's stock of the nation's assets.", }, "Y10YD": { "name": "Long term yield (10-year)", - "scale": "Percentage", "period": "Monthly", "description": "The 10-year yield is used as a proxy for mortgage rates. It's also seen as a " "sign of investor sentiment about the country's economy.", }, "M3YD": { "name": "3 month yield", - "scale": "Percentage", "period": "Monthly", "description": "The yield received for investing in a government issued treasury security " "that has a maturity of 3 months", }, "HOU": { "name": "House price index", - "scale": "Index", "period": "Monthly", "description": "House price index defined with base 2015 for Europe with varying " "bases for others. See: https://www.econdb.com/main-indicators", }, "OILPROD": { "name": "Oil production", - "scale": "Thousands", "period": "Monthly", "description": "The amount of oil barrels produced per day in a month within a country.", }, "POP": { "name": "Population", - "scale": "Thousands", "period": "Monthly", "description": "The population of a country. This can be in thousands or, " "when relatively small, in actual units.", }, } +SCALES = { + "Thousands": 1_000, + "Millions": 1_000_000, + "Hundreds of millions": 100_000_000, + "Units": 1, +} + TREASURIES: Dict = { "frequencies": { "annually": 203, @@ -499,10 +471,10 @@ TREASURIES: Dict = { def get_macro_data( parameter: str, country: str, - start_date="1900-01-01", + start_date=pd.to_datetime("1900-01-01"), end_date=datetime.today().date(), convert_currency=False, -) -> pd.Series: +) -> Tuple[Any, Union[str, Any]]: """Query the EconDB database to find specific macro data about a company [Source: EconDB] Parameters @@ -522,37 +494,54 @@ def get_macro_data( ---------- pd.Series A series with the requested macro data of the chosen country + units + The units of the macro data, e.g. 'Bbl/day" for oil. """ country = country.replace(" ", "_") if country not in COUNTRY_CODES: - return console.print(f"No data available for the country {country}.") + console.print(f"No data available for the country {country}.") + return pd.Series(), "" if parameter not in PARAMETERS: - return console.print(f"The parameter {parameter} is not an option.") + console.print(f"The parameter {parameter} is not an option for {country}.") + return pd.Series(), "" country_code = COUNTRY_CODES[country] country_currency = COUNTRY_CURRENCIES[country] try: - df = pd.read_csv( - f"https://www.econdb.com/api/series/{parameter}{country_code}/?format=csv", - index_col="Date", - parse_dates=["Date"], - squeeze=True, + r = requests.get( + f"https://www.econdb.com/series/context/?tickers={parameter}{country_code}" ) + data = r.json()[0] + scale = data["td"]["scale"] + units = data["td"]["units"] - df = df.dropna() + df = pd.DataFrame(data["dataarray"]) + df = ( + df.set_index(pd.to_datetime(df["date"]))[f"{parameter}{country_code}"] + * SCALES[scale] + ) + df = df.sort_index().dropna() if df.empty: - return console.print( + console.print( f"No data available for {parameter} ({PARAMETERS[parameter]['name']}) " f"of country {country.replace('_', ' ')}" ) + return pd.Series(), "" if start_date or end_date: df = df.loc[start_date:end_date] - if convert_currency and country_currency != convert_currency: + if ( + convert_currency + and country_currency != convert_currency + and units in COUNTRY_CURRENCIES.values() + ): + if units in COUNTRY_CURRENCIES.values(): + units = convert_currency + currency_data = yf.download( f"{country_currency}{convert_currency}=X", start=df.index[0], @@ -581,7 +570,7 @@ def get_macro_data( f"There is no data available for the combination {parameter} and {country}." ) - return df + return df, units @log_start_end(log=logger) @@ -591,7 +580,7 @@ def get_aggregated_macro_data( start_date: str = "1900-01-01", end_date=datetime.today().date(), convert_currency=False, -) -> pd.DataFrame: +) -> Tuple[DataFrame, Dict[Any, Dict[Any, Any]]]: """This functions groups the data queried from the EconDB database [Source: EconDB] Parameters @@ -611,16 +600,27 @@ def get_aggregated_macro_data( ---------- pd.DataFrame A DataFrame with the requested macro data of all chosen countries + Dictionary + A dictionary containing the units of each country's parameter (e.g. EUR) """ country_data: Dict[Any, Dict[Any, pd.Series]] = {} + units: Dict[Any, Dict[Any, Any]] = {} for country in countries: country_data[country] = {} + units[country] = {} for parameter in parameters: - country_data[country][parameter] = get_macro_data( + ( + country_data[country][parameter], + units[country][parameter], + ) = get_macro_data( parameter, country, start_date, end_date, convert_currency ) + if country_data[country][parameter].empty: + del country_data[country][parameter] + del units[country][parameter] + country_data_df = ( pd.DataFrame.from_dict(country_data, orient="index").stack().to_frame() ) @@ -628,7 +628,7 @@ def get_aggregated_macro_data( country_data_df[0].values.tolist(), index=country_data_df.index ).T - return country_data_df + return country_data_df, units @log_start_end(log=logger) diff --git a/gamestonk_terminal/economy/econdb_view.py b/gamestonk_terminal/economy/econdb_view.py index 08c20be19c6..0fb2fc5ff5a 100644 --- a/gamestonk_terminal/economy/econdb_view.py +++ b/gamestonk_terminal/economy/econdb_view.py @@ -60,7 +60,7 @@ def show_macro_data( ---------- Plots the Series. """ - country_data_df = econdb_model.get_aggregated_macro_data( + country_data_df, units = econdb_model.get_aggregated_macro_data( parameters, countries, start_date, end_date, convert_currency ) @@ -71,42 +71,43 @@ def show_macro_data( maximum_value = country_data_df.max().max() - if not convert_currency: - convert_currency = "in" - - if maximum_value > 1_000_000_000: + if maximum_value > 1_000_000_000_000: + df_rounded = country_data_df / 1_000_000_000_000 + denomination = " [in Trillions]" + elif maximum_value > 1_000_000_000: df_rounded = country_data_df / 1_000_000_000 - denomination = f"[{convert_currency} Billions]" + denomination = " [in Billions]" elif maximum_value > 1_000_000: df_rounded = country_data_df / 1_000_000 - denomination = f"[{convert_currency} Millions]" + denomination = " [in Millions]" elif maximum_value > 1_000: df_rounded = country_data_df / 1_000 - denomination = f"[{convert_currency} Thousands]" + denomination = " [in Thousands]" else: df_rounded = country_data_df denomination = "" legend = [] for column in df_rounded.columns: + parameter_units = f"Units: {units[column[0]][column[1]]}" country_label = column[0].replace("_", " ") parameter_label = econdb_model.PARAMETERS[column[1]]["name"] if len(parameters) > 1 and len(countries) > 1: ax.plot(df_rounded[column]) - ax.set_title(f"Macro data {denomination}", wrap=True, fontsize=12) - legend.append(f"{country_label} [{parameter_label}]") + ax.set_title(f"Macro data{denomination}", wrap=True, fontsize=12) + legend.append(f"{country_label} [{parameter_label}, {parameter_units}]") elif len(parameters) > 1: ax.plot(df_rounded[column]) - ax.set_title(f"{country_label} {denomination}", wrap=True, fontsize=12) - legend.append(parameter_label) + ax.set_title(f"{country_label}{denomination}", wrap=True, fontsize=12) + legend.append(f"{parameter_label} [{parameter_units}]") elif len(countries) > 1: ax.plot(df_rounded[column]) - ax.set_title(f"{parameter_label} {denomination}", wrap=True, fontsize=12) - legend.append(country_label) + ax.set_title(f"{parameter_label}{denomination}", wrap=True, fontsize=12) + legend.append(f"{country_label} [{parameter_units}]") else: ax.plot(df_rounded[column]) ax.set_title( - f"{parameter_label} of {country_label} {denomination}", + f"{parameter_label} of {country_label}{denomination} [{parameter_units}]", wrap=True, fontsize=12, ) diff --git a/gamestonk_terminal/economy/economy_controller.py b/gamestonk_terminal/economy/economy_controller.py index 10cbc0d5d53..e6ec0227cf4 100644 --- a/gamestonk_terminal/economy/economy_controller.py +++ b/gamestonk_terminal/economy/economy_controller.py @@ -53,6 +53,7 @@ class EconomyController(BaseController): "fred", "index", "treasury", + "yield", "plot", "options", "valuation", @@ -153,6 +154,7 @@ class EconomyController(BaseController): self.current_series: Dict = dict() self.fred_query: pd.Series = pd.Series() self.DATASETS: Dict[Any, pd.DataFrame] = dict() + self.UNITS: Dict[Any, Dict[Any, Any]] = dict() self.FRED_TITLES: Dict = dict() if session and gtff.USE_PROMPT_TOOLKIT: @@ -231,6 +233,7 @@ Macro Data fred collect macro data from FRED based on a series ID [src][Source: FRED][/src] index find and plot any (major) index on the market [src][Source: Yahoo Finance][/src] treasury obtain U.S. treasury rates [src][Source: EconDB][/src] + yield show the U.S. Treasury yield curve [src][Source: FRED][/src] plot plot data from the above commands together options show the available options for 'plot' or show/export the data @@ -563,7 +566,7 @@ Performance & Valuations pd.DataFrame.from_dict(econdb_model.PARAMETERS, orient="index"), show_index=True, index_name="Parameter", - headers=["Name", "Scale", "Period", "Description"], + headers=["Name", "Period", "Description"], ) elif ns_parser.show_countries: print_rich_table( @@ -573,7 +576,10 @@ Performance & Valuations ) elif ns_parser.parameters and ns_parser.countries: if ns_parser.store: - self.DATASETS["macro"] = econdb_model.get_aggregated_macro_data( + ( + self.DATASETS["macro"], + self.UNITS, + ) = econdb_model.get_aggregated_macro_data( parameters=ns_parser.parameters, countries=ns_parser.countries, start_date=ns_parser.start_date, @@ -799,6 +805,15 @@ Performance & Valuations default=False, ) + parser.add_argument( + "-r", + "--returns", + help="Flag to show compounded returns over interval.", + dest="returns", + action="store_true", + default=False, + ) + if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-i") ns_parser = parse_known_args_and_warn( @@ -840,6 +855,7 @@ Performance & Valuations store=ns_parser.store, raw=ns_parser.raw, export=ns_parser.export, + returns=ns_parser.returns, ) self.update_runtime_choices() @@ -970,6 +986,28 @@ Performance & Valuations self.update_runtime_choices() + @log_start_end(log=logger) + def call_yield(self, other_args: List[str]): + """Process treasury command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="yield", + description="Generate the US yield curve for a specific date. The yield curve shows the bond rates" + " at different maturities.", + ) + parser.add_argument( + "-d", + "--date", + type=valid_date, + help="Date to get the curve for. If not supplied, the most recent entry from FRED will be used.", + dest="date", + default=None, + ) + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + fred_view.display_yield_curve(ns_parser.date) + @log_start_end(log=logger) def call_plot(self, other_args: List[str]): """Process plot command""" @@ -1030,17 +1068,20 @@ Performance & Valuations if key == "macro": split = variable.split("_") if len(split) == 2: - country, parameter = split + country, parameter_abbreviation = split else: country = f"{split[0]} {split[1]}" - parameter = split[2] + parameter_abbreviation = split[2] - parameter = econdb_model.PARAMETERS[parameter][ - "name" - ] - dataset_yaxis1[f"{country} [{parameter}]"] = data[ - variable + parameter = econdb_model.PARAMETERS[ + parameter_abbreviation + ]["name"] + units = self.UNITS[country.replace(" ", "_")][ + parameter_abbreviation ] + dataset_yaxis1[ + f"{country} [{parameter}, Units: {units}]" + ] = data[variable] elif key == "fred": dataset_yaxis1[self.FRED_TITLES[variable]] = data[ variable @@ -1073,17 +1114,20 @@ Performance & Valuations if key == "macro": split = variable.split("_") if len(split) == 2: - country, parameter = split + country, parameter_abbreviation = split else: country = f"{split[0]} {split[1]}" - parameter = split[2] + parameter_abbreviation = split[2] - parameter = econdb_model.PARAMETERS[parameter][ - "name" - ] - dataset_yaxis2[f"{country} [{parameter}]"] = data[ - variable + parameter = econdb_model.PARAMETERS[ + parameter_abbreviation + ]["name"] + units = self.UNITS[country.replace(" ", "_")][ + parameter_abbreviation ] + dataset_yaxis2[ + f"{country} [{parameter}, Units: {units}]" + ] = data[variable] elif key == "fred": dataset_yaxis2[self.FRED_TITLES[variable]] = data[ variable diff --git a/gamestonk_terminal/economy/fred_model.py b/gamestonk_terminal/economy/fred_model.py index 9a443706463..17f3f037992 100644 --- a/gamestonk_terminal/economy/fred_model.py +++ b/gamestonk_terminal/economy/fred_model.py @@ -2,7 +2,8 @@ __docformat__ = "numpy" import logging -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Optional +from datetime import datetime, timedelta from requests import HTTPError import fred @@ -196,3 +197,65 @@ def get_aggregated_series_data( ) return data + + +@log_start_end(log=logger) +def get_yield_curve(date: Optional[datetime]) -> Tuple[pd.DataFrame, str]: + """Gets yield curve data from FRED + + Parameters + ---------- + date: Optional[datetime] + Date to get curve for. If None, gets most recent date + + Returns + ------- + pd.DataFrame: + Dataframe of yields and maturities + str + Date for which the yield curve is obtained + """ + fredapi_client = Fred(cfg.API_FRED_KEY) + fred_series = { + "1Month": "DGS1MO", + "3Month": "DGS3MO", + "6Month": "DGS6MO", + "1Year": "DGS1", + "2Year": "DGS2", + "3Year": "DGS3", + "5Year": "DGS5", + "7Year": "DGS7", + "10Year": "DGS10", + "20Year": "DGS20", + "30Year": "DGS30", + } + df = pd.DataFrame() + + if date is None: + date_to_get = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d") + else: + date_to_get = date.strftime("%Y-%m-%d") + + for key, s_id in fred_series.items(): + df = pd.concat( + [ + df, + pd.DataFrame( + fredapi_client.get_series(s_id, date_to_get), columns=[key] + ), + ], + axis=1, + ) + + if date is None: + date_of_yield = df.index[-1] + rates = pd.DataFrame(df.iloc[-1, :].values, columns=["Rate"]) + else: + date_of_yield = date + series = df[df.index == date] + if series.empty: + return pd.DataFrame(), date.strftime("%Y-%m-%d") + rates = pd.DataFrame(series.values.T, columns=["Rate"]) + + rates["Maturity"] = [1 / 12, 1 / 4, 1 / 2, 1, 2, 3, 5, 7, 10, 20, 30] + return rates, date_of_yield diff --git a/gamestonk_terminal/economy/fred_view.py b/gamestonk_terminal/economy/fred_view.py index e8c38603b35..58c9874ab30 100644 --- a/gamestonk_terminal/economy/fred_view.py +++ b/gamestonk_terminal/economy/fred_view.py @@ -5,6 +5,7 @@ import logging import os import textwrap from typing import Dict, Optional, List +from datetime import datetime import matplotlib.pyplot as plt import numpy as np @@ -177,3 +178,40 @@ def display_fred_series( "fred", data, ) + + +@log_start_end(log=logger) +@check_api_key(["API_FRED_KEY"]) +def display_yield_curve(date: datetime, external_axes: Optional[List[plt.Axes]] = None): + """Display yield curve based on US Treasury rates for a specified date. + + Parameters + ---------- + date: datetime + Date to get yield curve for + external_axes: Optional[List[plt.Axes]] + External axes to plot data on + """ + rates, date_of_yield = fred_model.get_yield_curve(date) + if rates.empty: + console.print( + f"[red]Yield data not found for {date.strftime('%Y-%m-%d')}[/red].\n" + ) + return + if external_axes is None: + _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) + + else: + if len(external_axes) != 1: + logger.error("Expected list of 3 axis items") + console.print("[red]Expected list of 3 axis items./n[/red]") + return + (ax,) = external_axes + + ax.plot(rates.Maturity, rates.Rate, "-o") + ax.set_xlabel("Maturity") + ax.set_ylabel("Rate (%)") + theme.style_primary_axis(ax) + if external_axes is None: + ax.set_title(f"US Yield Curve for {date_of_yield.strftime('%Y-%m-%d')} ") + theme.visualize_output() diff --git a/gamestonk_terminal/economy/yfinance_view.py b/gamestonk_terminal/economy/yfinance_view.py index d5e8ca0b373..82c7a156393 100644 --- a/gamestonk_terminal/economy/yfinance_view.py +++ b/gamestonk_terminal/economy/yfinance_view.py @@ -18,6 +18,7 @@ from gamestonk_terminal.helper_funcs import ( plot_autoscale, print_rich_table, export_data, + reindex_dates, ) logger = logging.getLogger(__name__) @@ -30,6 +31,7 @@ def show_indices( start_date: int = None, end_date: int = None, column: str = "Adj Close", + returns: bool = False, store: bool = False, raw: bool = False, external_axes: Optional[List[plt.axes]] = None, @@ -50,6 +52,8 @@ def show_indices( The end date, format "YEAR-MONTH-DAY", i.e. 2020-06-05. column : str Which column to load in, by default this is the Adjusted Close. + returns: bool + Flag to show cumulative returns on index store : bool Whether to prevent plotting the data. raw : bool @@ -68,6 +72,11 @@ def show_indices( for index in indices: indices_data[index] = get_index(index, interval, start_date, end_date, column) + if returns: + indices_data = indices_data.pct_change().dropna() + indices_data = indices_data + 1 + indices_data = indices_data.cumprod() + if not store: if external_axes is None: _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) @@ -81,9 +90,12 @@ def show_indices( label = index if not indices_data[index].empty: - ax.plot(indices_data[index], label=label) + data_to_percent = 100 * (indices_data[index].values - 1) + ax.plot(data_to_percent, label=label) ax.set_title("Indices") + if returns: + ax.set_ylabel("Performance (%)") ax.legend( bbox_to_anchor=(0, 0.40, 1, -0.52), loc="upper right", @@ -93,8 +105,17 @@ def show_indices( ncol=2, ) - theme.style_primary_axis(ax) - + if returns: + indices_data.index.name = "date" + plot_data = reindex_dates(indices_data) + theme.style_primary_axis( + ax, + data_index=plot_data.index.to_list(), + tick_labels=plot_data["date"].to_list(), + ) + ax.set_xlim(plot_data.index[0], plot_data.index[-1]) + else: + theme.style_primary_axis(ax) if external_axes is None: theme.visualize_output() diff --git a/gamestonk_terminal/feature_flags.py b/gamestonk_terminal/feature_flags.py index c6330752c34..80cca187b42 100644 --- a/gamestonk_terminal/feature_flags.py +++ b/gamestonk_terminal/feature_flags.py @@ -67,8 +67,5 @@ ENABLE_CHECK_API = strtobool(os.getenv("GTFF_ENABLE_CHECK_API", "True")) # Send logs to data lake LOG_COLLECTION = strtobool(os.getenv("GTFF_LOG_COLLECTION", "False")) -# Send only file size -ONLY_SEND_FILE_SIZE = strtobool(os.getenv("GTFF_ONLY_SEND_FILE_SIZE", "False")) - # Provide export folder path. If empty that means default. EXPORT_FOLDER_PATH = os.getenv("GTFF_EXPORT_FOLDER_PATH", "") diff --git a/gamestonk_terminal/forex/av_model.py b/gamestonk_terminal/forex/av_model.py index 94b89704f8d..3e79c19bb5d 100644 --- a/gamestonk_terminal/forex/av_model.py +++ b/gamestonk_terminal/forex/av_model.py @@ -18,7 +18,7 @@ logger = logging.getLogger(__name__) @log_start_end(log=logger) def get_currency_list() -> List: """Load AV currency codes from a local file.""" - path = os.path.join(os.path.dirname(__file__), "av_forex_currencies.csv") + path = os.path.join(os.path.dirname(__file__), "data/av_forex_currencies.csv") return list(pd.read_csv(path)["currency code"]) @@ -137,6 +137,8 @@ def get_historical( # If the returned data was unsuccessful if "Error Message" in r.json(): console.print(r.json()["Error Message"]) + elif "Note" in r.json(): + console.print(r.json()["Note"]) else: # check if json is empty if not r.json(): diff --git a/gamestonk_terminal/forex/av_view.py b/gamestonk_terminal/forex/av_view.py index c8c4a3ac365..77c374e97f4 100644 --- a/gamestonk_terminal/forex/av_view.py +++ b/gamestonk_terminal/forex/av_view.py @@ -2,17 +2,13 @@ __docformat__ = "numpy" import logging -from typing import Optional, List -import matplotlib.pyplot as plt -import mplfinance as mpf import pandas as pd -from gamestonk_terminal.config_terminal import theme from gamestonk_terminal.decorators import check_api_key from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.forex import av_model -from gamestonk_terminal.helper_funcs import plot_autoscale, print_rich_table +from gamestonk_terminal.helper_funcs import print_rich_table from gamestonk_terminal.rich_config import console logger = logging.getLogger(__name__) @@ -32,7 +28,15 @@ def display_quote(to_symbol: str, from_symbol: str): """ quote = av_model.get_quote(to_symbol, from_symbol) - if not quote: + if not bool(quote) or "Note" in quote: + if "Note" in quote: + console.print(quote["Note"]) + console.print( + "\n[red]" + + "No historical data loaded.\n" + + "Make sure 'av' supports the requested pair and you aren't hitting your API call limits." + + "[/red]\n" + ) return df = pd.DataFrame.from_dict(quote) @@ -44,63 +48,3 @@ def display_quote(to_symbol: str, from_symbol: str): title=f"[bold]{from_symbol}/{to_symbol} Quote [/bold]", ) console.print("") - - -@log_start_end(log=logger) -def display_candle( - data: pd.DataFrame, - to_symbol: str, - from_symbol: str, - external_axes: Optional[List[plt.Axes]] = None, -): - """Show candle plot for fx data. - - Parameters - ---------- - data : pd.DataFrame - Loaded fx historical data - to_symbol : str - To forex symbol - from_symbol : str - From forex symbol - external_axes: Optional[List[plt.Axes]] - External axes (1 axis are expected in the list), by default None - """ - candle_chart_kwargs = { - "type": "candle", - "style": theme.mpf_style, - "mav": (20, 50), - "volume": False, - "xrotation": theme.xticks_rotation, - "scale_padding": {"left": 0.3, "right": 1, "top": 0.8, "bottom": 0.8}, - "update_width_config": { - "candle_linewidth": 0.6, - "candle_width": 0.8, - "volume_linewidth": 0.8, - "volume_width": 0.8, - }, - "warn_too_much_data": 10000, - } - # This plot has 2 axes - if not external_axes: - candle_chart_kwargs["returnfig"] = True - candle_chart_kwargs["figratio"] = (10, 7) - candle_chart_kwargs["figscale"] = 1.10 - candle_chart_kwargs["figsize"] = plot_autoscale() - fig, ax = mpf.plot(data, **candle_chart_kwargs) - fig.suptitle( - f"{from_symbol}/{to_symbol}", - x=0.055, - y=0.965, - horizontalalignment="left", - ) - theme.visualize_output(force_tight_layout=False) - ax[0].legend() - else: - if len(external_axes) != 1: - logger.error("Expected list of 1 axis items.") - console.print("[red]Expected list of 1 axis items./n[/red]") - return - (ax1,) = external_axes - candle_chart_kwargs["ax"] = ax1 - mpf.plot(data, **candle_chart_kwargs) diff --git a/gamestonk_terminal/forex/av_forex_currencies.csv b/gamestonk_terminal/forex/data/av_forex_currencies.csv similarity index 100% rename from gamestonk_terminal/forex/av_forex_currencies.csv rename to gamestonk_terminal/forex/data/av_forex_currencies.csv diff --git a/gamestonk_terminal/forex/data/yahoofinance_forex.json b/gamestonk_terminal/forex/data/yahoofinance_forex.json new file mode 100644 index 00000000000..4e5535413b0 --- /dev/null +++ b/gamestonk_terminal/forex/data/yahoofinance_forex.json @@ -0,0 +1,20418 @@ +[ + { + "id": "AED=X", + "currency": "AED", + "exchange": "CCY", + "name": "USD/AED", + "from_symbol": "USD", + "to_symbol": "AED" + }, + { + "id": "AEDAUD=X", + "currency": "AUD", + "exchange": "CCY", + "name": "AED/AUD", + "from_symbol": "AED", + "to_symbol": "AUD" + }, + { + "id": "AEDBRX=X", + "currency": null, + "exchange": "CCY", + "name": "AED/BRX", + "from_symbol": "AED", + "to_symbol": "BRX" + }, + { + "id": "AEDCAD=X", + "currency": "CAD", + "exchange": "CCY", + "name": "AED/CAD", + "from_symbol": "AED", + "to_symbol": "CAD" + }, + { + "id": "AEDCHF=X", + "currency": "CHF", + "exchange": "CCY", + "name": "AED/CHF", + "from_symbol": "AED", + "to_symbol": "CHF" + }, + { + "id": "AEDEUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "AED/EUR", + "from_symbol": "AED", + "to_symbol": "EUR" + }, + { + "id": "AEDGBP=X", + "currency": "GBP", + "exchange": "CCY", + "name": "AED/GBP", + "from_symbol": "AED", + "to_symbol": "GBP" + }, + { + "id": "AEDHKD=X", + "currency": "HKD", + "exchange": "CCY", + "name": "AED/HKD", + "from_symbol": "AED", + "to_symbol": "HKD" + }, + { + "id": "AEDINR=X", + "currency": "INR", + "exchange": "CCY", + "name": "AED/INR", + "from_symbol": "AED", + "to_symbol": "INR" + }, + { + "id": "AEDJPY=X", + "currency": "JPY", + "exchange": "CCY", + "name": "AED/JPY", + "from_symbol": "AED", + "to_symbol": "JPY" + }, + { + "id": "AEDKRW=X", + "currency": "KRW", + "exchange": "CCY", + "name": "AED/KRW", + "from_symbol": "AED", + "to_symbol": "KRW" + }, + { + "id": "AEDKWD=X", + "currency": "KWD", + "exchange": "CCY", + "name": "AED/KWD", + "from_symbol": "AED", + "to_symbol": "KWD" + }, + { + "id": "AEDLKR=X", + "currency": "LKR", + "exchange": "CCY", + "name": "AED/LKR", + "from_symbol": "AED", + "to_symbol": "LKR" + }, + { + "id": "AEDMAD=X", + "currency": "MAD", + "exchange": "CCY", + "name": "AED/MAD", + "from_symbol": "AED", + "to_symbol": "MAD" + }, + { + "id": "AEDMUR=X", + "currency": "MUR", + "exchange": "CCY", + "name": "AED/MUR", + "from_symbol": "AED", + "to_symbol": "MUR" + }, + { + "id": "AEDMYX=X", + "currency": null, + "exchange": "CCY", + "name": "AED/MYX", + "from_symbol": "AED", + "to_symbol": "MYX" + }, + { + "id": "AEDNOK=X", + "currency": "NOK", + "exchange": "CCY", + "name": "AED/NOK", + "from_symbol": "AED", + "to_symbol": "NOK" + }, + { + "id": "AEDNZD=X", + "currency": "NZD", + "exchange": "CCY", + "name": "AED/NZD", + "from_symbol": "AED", + "to_symbol": "NZD" + }, + { + "id": "AEDOMR=X", + "currency": "OMR", + "exchange": "CCY", + "name": "AED/OMR", + "from_symbol": "AED", + "to_symbol": "OMR" + }, + { + "id": "AEDPKR=X", + "currency": "PKR", + "exchange": "CCY", + "name": "AED/PKR", + "from_symbol": "AED", + "to_symbol": "PKR" + }, + { + "id": "AEDQAR=X", + "currency": "QAR", + "exchange": "CCY", + "name": "AED/QAR", + "from_symbol": "AED", + "to_symbol": "QAR" + }, + { + "id": "AEDSAR=X", + "currency": "SAR", + "exchange": "CCY", + "name": "AED/SAR", + "from_symbol": "AED", + "to_symbol": "SAR" + }, + { + "id": "AEDSEK=X", + "currency": "SEK", + "exchange": "CCY", + "name": "AED/SEK", + "from_symbol": "AED", + "to_symbol": "SEK" + }, + { + "id": "AEDSGD=X", + "currency": "SGD", + "exchange": "CCY", + "name": "AED/SGD", + "from_symbol": "AED", + "to_symbol": "SGD" + }, + { + "id": "AEDTHX=X", + "currency": null, + "exchange": "CCY", + "name": "AED/THX", + "from_symbol": "AED", + "to_symbol": "THX" + }, + { + "id": "AEDTRY=X", + "currency": "TRY", + "exchange": "CCY", + "name": "AED/TRY", + "from_symbol": "AED", + "to_symbol": "TRY" + }, + { + "id": "AEDUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "AED/USD", + "from_symbol": "AED", + "to_symbol": "USD" + }, + { + "id": "AEDXDR=X", + "currency": null, + "exchange": "CCY", + "name": "AED/XDR", + "from_symbol": "AED", + "to_symbol": "XDR" + }, + { + "id": "AEDZAR=X", + "currency": "ZAR", + "exchange": "CCY", + "name": "AED/ZAR", + "from_symbol": "AED", + "to_symbol": "ZAR" + }, + { + "id": "AFN=X", + "currency": "AFN", + "exchange": "CCY", + "name": "USD/AFN", + "from_symbol": "USD", + "to_symbol": "AFN" + }, + { + "id": "AFNBRX=X", + "currency": null, + "exchange": "CCY", + "name": "AFN/BRX", + "from_symbol": "AFN", + "to_symbol": "BRX" + }, + { + "id": "AFNUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "AFN/USD", + "from_symbol": "AFN", + "to_symbol": "USD" + }, + { + "id": "ALL=X", + "currency": "ALL", + "exchange": "CCY", + "name": "USD/ALL", + "from_symbol": "USD", + "to_symbol": "ALL" + }, + { + "id": "ALLBRX=X", + "currency": null, + "exchange": "CCY", + "name": "ALL/BRX", + "from_symbol": "ALL", + "to_symbol": "BRX" + }, + { + "id": "ALLUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "ALL/USD", + "from_symbol": "ALL", + "to_symbol": "USD" + }, + { + "id": "AMD=X", + "currency": "AMD", + "exchange": "CCY", + "name": "USD/AMD", + "from_symbol": "USD", + "to_symbol": "AMD" + }, + { + "id": "AMDBRX=X", + "currency": null, + "exchange": "CCY", + "name": "AMD/BRX", + "from_symbol": "AMD", + "to_symbol": "BRX" + }, + { + "id": "AMDRUX=X", + "currency": null, + "exchange": "CCY", + "name": "AMD/RUX", + "from_symbol": "AMD", + "to_symbol": "RUX" + }, + { + "id": "ANG=X", + "currency": "ANG", + "exchange": "CCY", + "name": "USD/ANG", + "from_symbol": "USD", + "to_symbol": "ANG" + }, + { + "id": "ANGBRX=X", + "currency": null, + "exchange": "CCY", + "name": "ANG/BRX", + "from_symbol": "ANG", + "to_symbol": "BRX" + }, + { + "id": "ANGEUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "ANG/EUR", + "from_symbol": "ANG", + "to_symbol": "EUR" + }, + { + "id": "ANGSRD=X", + "currency": null, + "exchange": "CCY", + "name": "ANG/SRD", + "from_symbol": "ANG", + "to_symbol": "SRD" + }, + { + "id": "ANGUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "ANG/USD", + "from_symbol": "ANG", + "to_symbol": "USD" + }, + { + "id": "ANGZAR=X", + "currency": "ZAR", + "exchange": "CCY", + "name": "ANG/ZAR", + "from_symbol": "ANG", + "to_symbol": "ZAR" + }, + { + "id": "AOA=X", + "currency": "AOA", + "exchange": "CCY", + "name": "USD/AOA", + "from_symbol": "USD", + "to_symbol": "AOA" + }, + { + "id": "AOABRX=X", + "currency": null, + "exchange": "CCY", + "name": "AOA/BRX", + "from_symbol": "AOA", + "to_symbol": "BRX" + }, + { + "id": "AOAUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "AOA/USD", + "from_symbol": "AOA", + "to_symbol": "USD" + }, + { + "id": "ARS=X", + "currency": "ARS", + "exchange": "CCY", + "name": "USD/ARS", + "from_symbol": "USD", + "to_symbol": "ARS" + }, + { + "id": "ARSBRL=X", + "currency": "BRL", + "exchange": "CCY", + "name": "ARS/BRL", + "from_symbol": "ARS", + "to_symbol": "BRL" + }, + { + "id": "ARSBRX=X", + "currency": null, + "exchange": "CCY", + "name": "ARS/BRX", + "from_symbol": "ARS", + "to_symbol": "BRX" + }, + { + "id": "ARSEUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "ARS/EUR", + "from_symbol": "ARS", + "to_symbol": "EUR" + }, + { + "id": "ARSGBP=X", + "currency": "GBP", + "exchange": "CCY", + "name": "ARS/GBP", + "from_symbol": "ARS", + "to_symbol": "GBP" + }, + { + "id": "ARSJPY=X", + "currency": "JPY", + "exchange": "CCY", + "name": "ARS/JPY", + "from_symbol": "ARS", + "to_symbol": "JPY" + }, + { + "id": "ARSUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "ARS/USD", + "from_symbol": "ARS", + "to_symbol": "USD" + }, + { + "id": "ARSXCU=X", + "currency": null, + "exchange": "CCY", + "name": "ARS/XCU", + "from_symbol": "ARS", + "to_symbol": "XCU" + }, + { + "id": "AUD=X", + "currency": "AUD", + "exchange": "CCY", + "name": "USD/AUD", + "from_symbol": "USD", + "to_symbol": "AUD" + }, + { + "id": "AUDAED=X", + "currency": "AED", + "exchange": "CCY", + "name": "AUD/AED", + "from_symbol": "AUD", + "to_symbol": "AED" + }, + { + "id": "AUDANG=X", + "currency": "ANG", + "exchange": "CCY", + "name": "AUD/ANG", + "from_symbol": "AUD", + "to_symbol": "ANG" + }, + { + "id": "AUDARS=X", + "currency": "ARS", + "exchange": "CCY", + "name": "AUD/ARS", + "from_symbol": "AUD", + "to_symbol": "ARS" + }, + { + "id": "AUDAWG=X", + "currency": "AWG", + "exchange": "CCY", + "name": "AUD/AWG", + "from_symbol": "AUD", + "to_symbol": "AWG" + }, + { + "id": "AUDBAM=X", + "currency": "BAM", + "exchange": "CCY", + "name": "AUD/BAM", + "from_symbol": "AUD", + "to_symbol": "BAM" + }, + { + "id": "AUDBBD=X", + "currency": "BBD", + "exchange": "CCY", + "name": "AUD/BBD", + "from_symbol": "AUD", + "to_symbol": "BBD" + }, + { + "id": "AUDBHD=X", + "currency": "BHD", + "exchange": "CCY", + "name": "AUD/BHD", + "from_symbol": "AUD", + "to_symbol": "BHD" + }, + { + "id": "AUDBMD=X", + "currency": "BMD", + "exchange": "CCY", + "name": "AUD/BMD", + "from_symbol": "AUD", + "to_symbol": "BMD" + }, + { + "id": "AUDBOB=X", + "currency": "BOB", + "exchange": "CCY", + "name": "AUD/BOB", + "from_symbol": "AUD", + "to_symbol": "BOB" + }, + { + "id": "AUDBRL=X", + "currency": "BRL", + "exchange": "CCY", + "name": "AUD/BRL", + "from_symbol": "AUD", + "to_symbol": "BRL" + }, + { + "id": "AUDBRX=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/BRX", + "from_symbol": "AUD", + "to_symbol": "BRX" + }, + { + "id": "AUDBSD=X", + "currency": "BSD", + "exchange": "CCY", + "name": "AUD/BSD", + "from_symbol": "AUD", + "to_symbol": "BSD" + }, + { + "id": "AUDBWP=X", + "currency": "BWP", + "exchange": "CCY", + "name": "AUD/BWP", + "from_symbol": "AUD", + "to_symbol": "BWP" + }, + { + "id": "AUDBZD=X", + "currency": "BZD", + "exchange": "CCY", + "name": "AUD/BZD", + "from_symbol": "AUD", + "to_symbol": "BZD" + }, + { + "id": "AUDCAD=X", + "currency": "CAD", + "exchange": "CCY", + "name": "AUD/CAD", + "from_symbol": "AUD", + "to_symbol": "CAD" + }, + { + "id": "AUDCAX=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/CAX", + "from_symbol": "AUD", + "to_symbol": "CAX" + }, + { + "id": "AUDCHF=X", + "currency": "CHF", + "exchange": "CCY", + "name": "AUD/CHF", + "from_symbol": "AUD", + "to_symbol": "CHF" + }, + { + "id": "AUDCLP=X", + "currency": "CLP", + "exchange": "CCY", + "name": "AUD/CLP", + "from_symbol": "AUD", + "to_symbol": "CLP" + }, + { + "id": "AUDCNH=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/CNH", + "from_symbol": "AUD", + "to_symbol": "CNH" + }, + { + "id": "AUDCNY=X", + "currency": "CNY", + "exchange": "CCY", + "name": "AUD/CNY", + "from_symbol": "AUD", + "to_symbol": "CNY" + }, + { + "id": "AUDCOP=X", + "currency": "COP", + "exchange": "CCY", + "name": "AUD/COP", + "from_symbol": "AUD", + "to_symbol": "COP" + }, + { + "id": "AUDCRC=X", + "currency": "CRC", + "exchange": "CCY", + "name": "AUD/CRC", + "from_symbol": "AUD", + "to_symbol": "CRC" + }, + { + "id": "AUDCZK=X", + "currency": "CZK", + "exchange": "CCY", + "name": "AUD/CZK", + "from_symbol": "AUD", + "to_symbol": "CZK" + }, + { + "id": "AUDCZX=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/CZX", + "from_symbol": "AUD", + "to_symbol": "CZX" + }, + { + "id": "AUDDKK=X", + "currency": "DKK", + "exchange": "CCY", + "name": "AUD/DKK", + "from_symbol": "AUD", + "to_symbol": "DKK" + }, + { + "id": "AUDDKX=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/DKX", + "from_symbol": "AUD", + "to_symbol": "DKX" + }, + { + "id": "AUDDOP=X", + "currency": "DOP", + "exchange": "CCY", + "name": "AUD/DOP", + "from_symbol": "AUD", + "to_symbol": "DOP" + }, + { + "id": "AUDEGP=X", + "currency": "EGP", + "exchange": "CCY", + "name": "AUD/EGP", + "from_symbol": "AUD", + "to_symbol": "EGP" + }, + { + "id": "AUDERN=X", + "currency": "ERN", + "exchange": "CCY", + "name": "AUD/ERN", + "from_symbol": "AUD", + "to_symbol": "ERN" + }, + { + "id": "AUDEUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "AUD/EUR", + "from_symbol": "AUD", + "to_symbol": "EUR" + }, + { + "id": "AUDFJD=X", + "currency": "FJD", + "exchange": "CCY", + "name": "AUD/FJD", + "from_symbol": "AUD", + "to_symbol": "FJD" + }, + { + "id": "AUDGBP=X", + "currency": "GBP", + "exchange": "CCY", + "name": "AUD/GBP", + "from_symbol": "AUD", + "to_symbol": "GBP" + }, + { + "id": "AUDGHS=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/GHS", + "from_symbol": "AUD", + "to_symbol": "GHS" + }, + { + "id": "AUDGIP=X", + "currency": "GIP", + "exchange": "CCY", + "name": "AUD/GIP", + "from_symbol": "AUD", + "to_symbol": "GIP" + }, + { + "id": "AUDGTQ=X", + "currency": "GTQ", + "exchange": "CCY", + "name": "AUD/GTQ", + "from_symbol": "AUD", + "to_symbol": "GTQ" + }, + { + "id": "AUDHKD=X", + "currency": "HKD", + "exchange": "CCY", + "name": "AUD/HKD", + "from_symbol": "AUD", + "to_symbol": "HKD" + }, + { + "id": "AUDHNL=X", + "currency": "HNL", + "exchange": "CCY", + "name": "AUD/HNL", + "from_symbol": "AUD", + "to_symbol": "HNL" + }, + { + "id": "AUDHRK=X", + "currency": "HRK", + "exchange": "CCY", + "name": "AUD/HRK", + "from_symbol": "AUD", + "to_symbol": "HRK" + }, + { + "id": "AUDHRX=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/HRX", + "from_symbol": "AUD", + "to_symbol": "HRX" + }, + { + "id": "AUDHUF=X", + "currency": "HUF", + "exchange": "CCY", + "name": "AUD/HUF", + "from_symbol": "AUD", + "to_symbol": "HUF" + }, + { + "id": "AUDHUX=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/HUX", + "from_symbol": "AUD", + "to_symbol": "HUX" + }, + { + "id": "AUDIDR=X", + "currency": "IDR", + "exchange": "CCY", + "name": "AUD/IDR", + "from_symbol": "AUD", + "to_symbol": "IDR" + }, + { + "id": "AUDILS=X", + "currency": "ILS", + "exchange": "CCY", + "name": "AUD/ILS", + "from_symbol": "AUD", + "to_symbol": "ILS" + }, + { + "id": "AUDINR=X", + "currency": "INR", + "exchange": "CCY", + "name": "AUD/INR", + "from_symbol": "AUD", + "to_symbol": "INR" + }, + { + "id": "AUDISK=X", + "currency": "ISK", + "exchange": "CCY", + "name": "AUD/ISK", + "from_symbol": "AUD", + "to_symbol": "ISK" + }, + { + "id": "AUDJMD=X", + "currency": "JMD", + "exchange": "CCY", + "name": "AUD/JMD", + "from_symbol": "AUD", + "to_symbol": "JMD" + }, + { + "id": "AUDJOD=X", + "currency": "JOD", + "exchange": "CCY", + "name": "AUD/JOD", + "from_symbol": "AUD", + "to_symbol": "JOD" + }, + { + "id": "AUDJPY=X", + "currency": "JPY", + "exchange": "CCY", + "name": "AUD/JPY", + "from_symbol": "AUD", + "to_symbol": "JPY" + }, + { + "id": "AUDKES=X", + "currency": "KES", + "exchange": "CCY", + "name": "AUD/KES", + "from_symbol": "AUD", + "to_symbol": "KES" + }, + { + "id": "AUDKRW=X", + "currency": "KRW", + "exchange": "CCY", + "name": "AUD/KRW", + "from_symbol": "AUD", + "to_symbol": "KRW" + }, + { + "id": "AUDKWD=X", + "currency": "KWD", + "exchange": "CCY", + "name": "AUD/KWD", + "from_symbol": "AUD", + "to_symbol": "KWD" + }, + { + "id": "AUDKYD=X", + "currency": "KYD", + "exchange": "CCY", + "name": "AUD/KYD", + "from_symbol": "AUD", + "to_symbol": "KYD" + }, + { + "id": "AUDLKR=X", + "currency": "LKR", + "exchange": "CCY", + "name": "AUD/LKR", + "from_symbol": "AUD", + "to_symbol": "LKR" + }, + { + "id": "AUDMAD=X", + "currency": "MAD", + "exchange": "CCY", + "name": "AUD/MAD", + "from_symbol": "AUD", + "to_symbol": "MAD" + }, + { + "id": "AUDMGA=X", + "currency": null, + "exchange": "CCY", + "name": "AUD/MGA", + "from_symbol": "AUD", + "to_symbol": "MGA" + }, + { + "id": "AUDMXN=X", + "currency": "MXN", + "exchange": "CCY", + "name": "AUD/MXN", + "from_symbol": "AUD", + "to_symbol": "MXN" + }, + { + "id": "AUDMYR=X", + "currency": "MYR", + "exchange": "CCY", + "name": "AUD/MYR", + "from_symbol": "AUD", + "to_symbol": "MYR" + }, + { + "id": "AUDMYX=X", + 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"exchange": "CCY", + "name": "XCU/IDR", + "from_symbol": "XCU", + "to_symbol": "IDR" + }, + { + "id": "XCUILS=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/ILS", + "from_symbol": "XCU", + "to_symbol": "ILS" + }, + { + "id": "XCUINR=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/INR", + "from_symbol": "XCU", + "to_symbol": "INR" + }, + { + "id": "XCUJPY=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/JPY", + "from_symbol": "XCU", + "to_symbol": "JPY" + }, + { + "id": "XCUKRW=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/KRW", + "from_symbol": "XCU", + "to_symbol": "KRW" + }, + { + "id": "XCUKWD=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/KWD", + "from_symbol": "XCU", + "to_symbol": "KWD" + }, + { + "id": "XCUMXN=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/MXN", + "from_symbol": "XCU", + "to_symbol": "MXN" + }, + { + "id": "XCUMYR=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/MYR", + "from_symbol": "XCU", + "to_symbol": "MYR" + }, + { + "id": "XCUNOK=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/NOK", + "from_symbol": "XCU", + "to_symbol": "NOK" + }, + { + "id": "XCUNZD=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/NZD", + "from_symbol": "XCU", + "to_symbol": "NZD" + }, + { + "id": "XCUPLN=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/PLN", + "from_symbol": "XCU", + "to_symbol": "PLN" + }, + { + "id": "XCURUB=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/RUB", + "from_symbol": "XCU", + "to_symbol": "RUB" + }, + { + "id": "XCUSEK=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/SEK", + "from_symbol": "XCU", + "to_symbol": "SEK" + }, + { + "id": "XCUSGD=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/SGD", + "from_symbol": "XCU", + "to_symbol": "SGD" + }, + { + "id": "XCUTRY=X", + "currency": null, + "exchange": "CCY", + "name": "XCU/TRY", + "from_symbol": "XCU", + "to_symbol": "TRY" + }, 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"exchange": "CCY", + "name": "XDR/ISX", + "from_symbol": "XDR", + "to_symbol": "ISX" + }, + { + "id": "XDRPLX=X", + "currency": null, + "exchange": "CCY", + "name": "XDR/PLX", + "from_symbol": "XDR", + "to_symbol": "PLX" + }, + { + "id": "XDRRUX=X", + "currency": null, + "exchange": "CCY", + "name": "XDR/RUX", + "from_symbol": "XDR", + "to_symbol": "RUX" + }, + { + "id": "XDRUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "XDR/USD", + "from_symbol": "XDR", + "to_symbol": "USD" + }, + { + "id": "XDRXCU=X", + "currency": null, + "exchange": "CCY", + "name": "XDR/XCU", + "from_symbol": "XDR", + "to_symbol": "XCU" + }, + { + "id": "XOF=X", + "currency": "XOF", + "exchange": "CCY", + "name": "USD/XOF", + "from_symbol": "USD", + "to_symbol": "XOF" + }, + { + "id": "XOFBRX=X", + "currency": null, + "exchange": "CCY", + "name": "XOF/BRX", + "from_symbol": "XOF", + "to_symbol": "BRX" + }, + { + "id": "XOFEUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "XOF/EUR", + "from_symbol": "XOF", + "to_symbol": "EUR" + }, + { + "id": "XOFGBP=X", + "currency": "GBP", + "exchange": "CCY", + "name": "XOF/GBP", + "from_symbol": "XOF", + "to_symbol": "GBP" + }, + { + "id": "XOFUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "XOF/USD", + "from_symbol": "XOF", + "to_symbol": "USD" + }, + { + "id": "XPF=X", + "currency": "XPF", + "exchange": "CCY", + "name": "USD/XPF", + "from_symbol": "USD", + "to_symbol": "XPF" + }, + { + "id": "XPFBRX=X", + "currency": null, + "exchange": "CCY", + "name": "XPF/BRX", + "from_symbol": "XPF", + "to_symbol": "BRX" + }, + { + "id": "YER=X", + "currency": "YER", + "exchange": "CCY", + "name": "USD/YER", + "from_symbol": "USD", + "to_symbol": "YER" + }, + { + "id": "YERBRX=X", + "currency": null, + "exchange": "CCY", + "name": "YER/BRX", + "from_symbol": "YER", + "to_symbol": "BRX" + }, + { + "id": "ZAC=X", + "currency": "ZAC", + "exchange": "CCY", + "name": "USD/ZAC", + "from_symbol": "USD", + "to_symbol": "ZAC" + }, + { + "id": "ZAR=X", + "currency": "ZAR", + "exchange": "CCY", + "name": "USD/ZAR", + "from_symbol": "USD", + "to_symbol": "ZAR" + }, + { + "id": "ZARAED=X", + "currency": "AED", + "exchange": "CCY", + "name": "ZAR/AED", + "from_symbol": "ZAR", + "to_symbol": "AED" + }, + { + "id": "ZARANG=X", + "currency": "ANG", + "exchange": "CCY", + "name": "ZAR/ANG", + "from_symbol": "ZAR", + "to_symbol": "ANG" + }, + { + "id": "ZARAOA=X", + "currency": "AOA", + "exchange": "CCY", + "name": "ZAR/AOA", + "from_symbol": "ZAR", + "to_symbol": "AOA" + }, + { + "id": "ZARAUD=X", + "currency": "AUD", + "exchange": "CCY", + "name": "ZAR/AUD", + "from_symbol": "ZAR", + "to_symbol": "AUD" + }, + { + "id": "ZARBRL=X", + "currency": "BRL", + "exchange": "CCY", + "name": "ZAR/BRL", + "from_symbol": "ZAR", + "to_symbol": "BRL" + }, + { + "id": "ZARBRX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/BRX", + "from_symbol": "ZAR", + "to_symbol": "BRX" + }, + { + "id": "ZARBWP=X", + "currency": "BWP", + "exchange": "CCY", + "name": "ZAR/BWP", + "from_symbol": "ZAR", + "to_symbol": "BWP" + }, + { + "id": "ZARCAD=X", + "currency": "CAD", + "exchange": "CCY", + "name": "ZAR/CAD", + "from_symbol": "ZAR", + "to_symbol": "CAD" + }, + { + "id": "ZARCAX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/CAX", + "from_symbol": "ZAR", + "to_symbol": "CAX" + }, + { + "id": "ZARCHF=X", + "currency": "CHF", + "exchange": "CCY", + "name": "ZAR/CHF", + "from_symbol": "ZAR", + "to_symbol": "CHF" + }, + { + "id": "ZARCNY=X", + "currency": "CNY", + "exchange": "CCY", + "name": "ZAR/CNY", + "from_symbol": "ZAR", + "to_symbol": "CNY" + }, + { + "id": "ZARCZX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/CZX", + "from_symbol": "ZAR", + "to_symbol": "CZX" + }, + { + "id": "ZARDKK=X", + "currency": "DKK", + "exchange": "CCY", + "name": "ZAR/DKK", + "from_symbol": "ZAR", + "to_symbol": "DKK" + }, + { + "id": "ZARDKX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/DKX", + "from_symbol": "ZAR", + "to_symbol": "DKX" + }, + { + "id": "ZAREGP=X", + "currency": "EGP", + "exchange": "CCY", + "name": "ZAR/EGP", + "from_symbol": "ZAR", + "to_symbol": "EGP" + }, + { + "id": "ZAREUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "ZAR/EUR", + "from_symbol": "ZAR", + "to_symbol": "EUR" + }, + { + "id": "ZARGBP=X", + "currency": "GBP", + "exchange": "CCY", + "name": "ZAR/GBP", + "from_symbol": "ZAR", + "to_symbol": "GBP" + }, + { + "id": "ZARGHS=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/GHS", + "from_symbol": "ZAR", + "to_symbol": "GHS" + }, + { + "id": "ZARHKD=X", + "currency": "HKD", + "exchange": "CCY", + "name": "ZAR/HKD", + "from_symbol": "ZAR", + "to_symbol": "HKD" + }, + { + "id": "ZARHUX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/HUX", + "from_symbol": "ZAR", + "to_symbol": "HUX" + }, + { + "id": "ZARIDR=X", + "currency": "IDR", + "exchange": "CCY", + "name": "ZAR/IDR", + "from_symbol": "ZAR", + "to_symbol": "IDR" + }, + { + "id": "ZARINR=X", + "currency": "INR", + "exchange": "CCY", + "name": "ZAR/INR", + "from_symbol": "ZAR", + "to_symbol": "INR" + }, + { + "id": "ZARJPY=X", + "currency": "JPY", + "exchange": "CCY", + "name": "ZAR/JPY", + "from_symbol": "ZAR", + "to_symbol": "JPY" + }, + { + "id": "ZARKES=X", + "currency": "KES", + "exchange": "CCY", + "name": "ZAR/KES", + "from_symbol": "ZAR", + "to_symbol": "KES" + }, + { + "id": "ZARKRW=X", + "currency": "KRW", + "exchange": "CCY", + "name": "ZAR/KRW", + "from_symbol": "ZAR", + "to_symbol": "KRW" + }, + { + "id": "ZARLKR=X", + "currency": "LKR", + "exchange": "CCY", + "name": "ZAR/LKR", + "from_symbol": "ZAR", + "to_symbol": "LKR" + }, + { + "id": "ZARLSL=X", + "currency": "LSL", + "exchange": "CCY", + "name": "ZAR/LSL", + "from_symbol": "ZAR", + "to_symbol": "LSL" + }, + { + "id": "ZARMWK=X", + "currency": "MWK", + "exchange": "CCY", + "name": "ZAR/MWK", + "from_symbol": "ZAR", + "to_symbol": "MWK" + }, + { + "id": "ZARMXN=X", + "currency": "MXN", + "exchange": "CCY", + "name": "ZAR/MXN", + "from_symbol": "ZAR", + "to_symbol": "MXN" + }, + { + "id": "ZARMYR=X", + "currency": "MYR", + "exchange": "CCY", + "name": "ZAR/MYR", + "from_symbol": "ZAR", + "to_symbol": "MYR" + }, + { + "id": "ZARNOK=X", + "currency": "NOK", + "exchange": "CCY", + "name": "ZAR/NOK", + "from_symbol": "ZAR", + "to_symbol": "NOK" + }, + { + "id": "ZARNZD=X", + "currency": "NZD", + "exchange": "CCY", + "name": "ZAR/NZD", + "from_symbol": "ZAR", + "to_symbol": "NZD" + }, + { + "id": "ZARPHP=X", + "currency": "PHP", + "exchange": "CCY", + "name": "ZAR/PHP", + "from_symbol": "ZAR", + "to_symbol": "PHP" + }, + { + "id": "ZARPKR=X", + "currency": "PKR", + "exchange": "CCY", + "name": "ZAR/PKR", + "from_symbol": "ZAR", + "to_symbol": "PKR" + }, + { + "id": "ZARPLN=X", + "currency": "PLN", + "exchange": "CCY", + "name": "ZAR/PLN", + "from_symbol": "ZAR", + "to_symbol": "PLN" + }, + { + "id": "ZARPLX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/PLX", + "from_symbol": "ZAR", + "to_symbol": "PLX" + }, + { + "id": "ZARRUB=X", + "currency": "RUB", + "exchange": "CCY", + "name": "ZAR/RUB", + "from_symbol": "ZAR", + "to_symbol": "RUB" + }, + { + "id": "ZARRUX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/RUX", + "from_symbol": "ZAR", + "to_symbol": "RUX" + }, + { + "id": "ZARSCR=X", + "currency": "SCR", + "exchange": "CCY", + "name": "ZAR/SCR", + "from_symbol": "ZAR", + "to_symbol": "SCR" + }, + { + "id": "ZARSEK=X", + "currency": "SEK", + "exchange": "CCY", + "name": "ZAR/SEK", + "from_symbol": "ZAR", + "to_symbol": "SEK" + }, + { + "id": "ZARSGD=X", + "currency": "SGD", + "exchange": "CCY", + "name": "ZAR/SGD", + "from_symbol": "ZAR", + "to_symbol": "SGD" + }, + { + "id": "ZARSZL=X", + "currency": "SZL", + "exchange": "CCY", + "name": "ZAR/SZL", + "from_symbol": "ZAR", + "to_symbol": "SZL" + }, + { + "id": "ZARTHB=X", + "currency": "THB", + "exchange": "CCY", + "name": "ZAR/THB", + "from_symbol": "ZAR", + "to_symbol": "THB" + }, + { + "id": "ZARTHX=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/THX", + "from_symbol": "ZAR", + "to_symbol": "THX" + }, + { + "id": "ZARTWD=X", + "currency": "TWD", + "exchange": "CCY", + "name": "ZAR/TWD", + "from_symbol": "ZAR", + "to_symbol": "TWD" + }, + { + "id": "ZARUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "ZAR/USD", + "from_symbol": "ZAR", + "to_symbol": "USD" + }, + { + "id": "ZARXDR=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/XDR", + "from_symbol": "ZAR", + "to_symbol": "XDR" + }, + { + "id": "ZARZMW=X", + "currency": null, + "exchange": "CCY", + "name": "ZAR/ZMW", + "from_symbol": "ZAR", + "to_symbol": "ZMW" + }, + { + "id": "ZMW=X", + "currency": "ZMW", + "exchange": "CCY", + "name": "USD/ZMW", + "from_symbol": "USD", + "to_symbol": "ZMW" + }, + { + "id": "ZMWBRX=X", + "currency": null, + "exchange": "CCY", + "name": "ZMW/BRX", + "from_symbol": "ZMW", + "to_symbol": "BRX" + }, + { + "id": "ZMWCAD=X", + "currency": "CAD", + "exchange": "CCY", + "name": "ZMW/CAD", + "from_symbol": "ZMW", + "to_symbol": "CAD" + }, + { + "id": "ZMWCHF=X", + "currency": "CHF", + "exchange": "CCY", + "name": "ZMW/CHF", + "from_symbol": "ZMW", + "to_symbol": "CHF" + }, + { + "id": "ZMWEUR=X", + "currency": "EUR", + "exchange": "CCY", + "name": "ZMW/EUR", + "from_symbol": "ZMW", + "to_symbol": "EUR" + }, + { + "id": "ZMWGBP=X", + "currency": "GBP", + "exchange": "CCY", + "name": "ZMW/GBP", + "from_symbol": "ZMW", + "to_symbol": "GBP" + }, + { + "id": "ZMWJPY=X", + "currency": "JPY", + "exchange": "CCY", + "name": "ZMW/JPY", + "from_symbol": "ZMW", + "to_symbol": "JPY" + }, + { + "id": "ZMWUSD=X", + "currency": "USD", + "exchange": "CCY", + "name": "ZMW/USD", + "from_symbol": "ZMW", + "to_symbol": "USD" + }, + { + "id": "ZMWZAR=X", + "currency": null, + "exchange": "CCY", + "name": "ZMW/ZAR", + "from_symbol": "ZMW", + "to_symbol": "ZAR" + } + ] diff --git a/gamestonk_terminal/forex/forex_api.py b/gamestonk_terminal/forex/forex_api.py index 8d477f35214..f2331a40688 100644 --- a/gamestonk_terminal/forex/forex_api.py +++ b/gamestonk_terminal/forex/forex_api.py @@ -6,8 +6,9 @@ from gamestonk_terminal.helper_classes import ModelsNamespace as _models # pylint: disable=unused-import # Context menus -from .av_view import display_candle as candle +from .forex_helper import display_candle as candle from .av_view import display_quote as quote +from .technical_analysis import ta_api as ta from .oanda import oanda_api as oanda # Models diff --git a/gamestonk_terminal/forex/forex_controller.py b/gamestonk_terminal/forex/forex_controller.py index 11838549069..0319f1f7067 100644 --- a/gamestonk_terminal/forex/forex_controller.py +++ b/gamestonk_terminal/forex/forex_controller.py @@ -12,11 +12,13 @@ from prompt_toolkit.completion import NestedCompleter from gamestonk_terminal import feature_flags as gtff from gamestonk_terminal.decorators import log_start_end -from gamestonk_terminal.forex import av_model, av_view +from gamestonk_terminal.forex import av_view, forex_helper +from gamestonk_terminal.forex.forex_helper import FOREX_SOURCES, SOURCES_INTERVALS from gamestonk_terminal.helper_funcs import parse_known_args_and_warn, valid_date from gamestonk_terminal.menu import session from gamestonk_terminal.parent_classes import BaseController from gamestonk_terminal.rich_config import console +from gamestonk_terminal.decorators import check_api_key # pylint: disable=R1710,import-outside-toplevel @@ -27,7 +29,7 @@ class ForexController(BaseController): """Forex Controller class.""" CHOICES_COMMANDS = ["to", "from", "load", "quote", "candle", "resources"] - CHOICES_MENUS = ["oanda"] + CHOICES_MENUS = ["ta", "oanda"] PATH = "/forex/" FILE_PATH = os.path.join(os.path.dirname(__file__), "README.md") @@ -37,12 +39,14 @@ class ForexController(BaseController): self.from_symbol = "USD" self.to_symbol = "" + self.source = "yf" self.data = pd.DataFrame() if session and gtff.USE_PROMPT_TOOLKIT: choices: dict = {c: {} for c in self.controller_choices} - choices["to"] = {c: None for c in av_model.CURRENCY_LIST} - choices["from"] = {c: None for c in av_model.CURRENCY_LIST} + choices["to"] = {c: None for c in forex_helper.YF_CURRENCY_LIST} + choices["from"] = {c: None for c in forex_helper.YF_CURRENCY_LIST} + choices["load"]["--source"] = {c: None for c in FOREX_SOURCES} self.completer = NestedCompleter.from_nested_dict(choices) def print_help(self): @@ -53,19 +57,29 @@ class ForexController(BaseController): from select the "from" currency in a forex pair to select the "to" currency in a forex pair[/cmds] -[param]From: [/param]{None or self.from_symbol} -[param]To: [/param]{None or self.to_symbol}[cmds] -{has_symbols_start} -[src][AlphaVantage][/src][cmds] - quote get last quote +[param]From: [/param]{None or self.from_symbol} +[param]To: [/param]{None or self.to_symbol} +[param]Source: [/param]{None or FOREX_SOURCES[self.source]}[cmds]{has_symbols_start} +[cmds] + quote get last quote [src][AlphaVantage][/src] load get historical data candle show candle plot for loaded data[/cmds] -{has_symbols_end} +[menu] +> ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv +[/menu]{has_symbols_end} [info]Forex brokerages:[/info][menu] > oanda Oanda menu[/menu][/cmds] """ console.print(text=help_text, menu="Forex") + def custom_reset(self): + """Class specific component of reset command""" + set_from_symbol = f"from {self.from_symbol}" if self.from_symbol else "" + set_to_symbol = f"to {self.to_symbol}" if self.to_symbol else "" + if set_from_symbol and set_to_symbol: + return ["forex", set_from_symbol, set_to_symbol] + return [] + @log_start_end(log=logger) def call_to(self, other_args: List[str]): """Process 'to' command.""" @@ -79,7 +93,7 @@ class ForexController(BaseController): "-n", "--name", help="To currency", - type=av_model.check_valid_forex_currency, + type=forex_helper.check_valid_yf_forex_currency, dest="to_symbol", ) @@ -96,7 +110,9 @@ class ForexController(BaseController): self.to_symbol = ns_parser.to_symbol console.print( - f"\nSelected pair\nFrom: {self.from_symbol}\nTo: {self.to_symbol}\n\n" + f"\nSelected pair\nFrom: {self.from_symbol}\n" + f"To: {self.to_symbol}\n" + f"Source: {FOREX_SOURCES[self.source]}\n\n" ) @log_start_end(log=logger) @@ -112,7 +128,7 @@ class ForexController(BaseController): "-n", "--name", help="From currency", - type=av_model.check_valid_forex_currency, + type=forex_helper.check_valid_yf_forex_currency, dest="from_symbol", ) @@ -128,7 +144,9 @@ class ForexController(BaseController): if ns_parser: self.from_symbol = ns_parser.from_symbol console.print( - f"\nSelected pair\nFrom: {self.from_symbol}\nTo: {self.to_symbol}\n\n" + f"\nSelected pair\nFrom: {self.from_symbol}\n" + f"To: {self.to_symbol}\n" + f"Source: {FOREX_SOURCES[self.source]}\n\n" ) @log_start_end(log=logger) @@ -138,8 +156,20 @@ class ForexController(BaseController): add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="load", - description="Load historical exchange rate data.", + description="Load historical exchange rate data." + "Available data sources are Alpha Advantage and YahooFinance" + "By default main source used for analysis is YahooFinance (yf). To change it use --source av", ) + + parser.add_argument( + "--source", + help="Source of historical data", + dest="source", + choices=("yf", "av"), + default="yf", + required=False, + ) + parser.add_argument( "-r", "--resolution", @@ -151,33 +181,54 @@ class ForexController(BaseController): parser.add_argument( "-i", "--interval", - choices=[1, 5, 15, 30, 60], - default="5", - help="Interval of intraday data. Can be 1, 5, 15, 30 or 60.", + choices=SOURCES_INTERVALS["yf"], + default="5min", + help="""Interval of intraday data. Options: + [YahooFinance] 1min, 2min, 5min, 15min, 30min, 60min, 90min, 1hour, 1day, 5day, 1week, 1month, 3month. + [AlphaAdvantage] 1min, 5min, 15min, 30min, 60min""", dest="interval", ) parser.add_argument( "-s", "--start_date", - default=(datetime.now() - timedelta(days=366)), + default=(datetime.now() - timedelta(days=59)), type=valid_date, help="Start date of data.", dest="start_date", ) - if other_args and "-t" not in other_args and "-h" not in other_args: - other_args.insert(0, "-t") + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + if self.to_symbol and self.from_symbol: - self.data = av_model.get_historical( + + self.data = forex_helper.load( to_symbol=self.to_symbol, from_symbol=self.from_symbol, resolution=ns_parser.resolution, interval=ns_parser.interval, start_date=ns_parser.start_date.strftime("%Y-%m-%d"), + source=ns_parser.source, ) + + if self.data.empty: + console.print( + "\n[red]" + + "No historical data loaded.\n" + + f"Make sure you have appropriate access for the '{ns_parser.source}' data source " + + f"and that '{ns_parser.source}' supports the requested range." + + "[/red]\n" + ) + else: + self.data.index.name = "date" + + self.source = ns_parser.source + console.print( - f"Loaded historic data from {self.from_symbol} to {self.to_symbol}" + f"\nSelected pair\nFrom: {self.from_symbol}\n" + f"To: {self.to_symbol}\n" + f"Source: {FOREX_SOURCES[self.source]}\n\n" ) else: logger.error( @@ -199,7 +250,7 @@ class ForexController(BaseController): ns_parser = parse_known_args_and_warn(parser, other_args) if ns_parser: if not self.data.empty: - av_view.display_candle(self.data, self.to_symbol, self.from_symbol) + forex_helper.display_candle(self.data, self.to_symbol, self.from_symbol) else: logger.error( "No forex historical data loaded. Load first using ." @@ -226,16 +277,46 @@ class ForexController(BaseController): "Make sure both a 'to' symbol and a 'from' symbol are selected." ) console.print( - '[red]Make sure both a "to" symbol and a "from" symbol are selected.[/red]\n' + "[red]Make sure both a 'to' symbol and a 'from' symbol are selected.[/red]\n" ) # MENUS @log_start_end(log=logger) + @check_api_key(["OANDA_ACCOUNT_TYPE", "OANDA_ACCOUNT", "OANDA_TOKEN"]) def call_oanda(self, _): """Enter Oanda menu.""" from gamestonk_terminal.forex.oanda.oanda_controller import OandaController - self.queue = self.load_class(OandaController, self.queue) + if self.to_symbol and self.from_symbol: + + self.queue = self.load_class( + OandaController, + queue=self.queue, + ) + else: + console.print("No currency pair data is loaded. Use 'load' to load data.\n") + + @log_start_end(log=logger) + def call_ta(self, _): + """Process ta command""" + from gamestonk_terminal.forex.technical_analysis.ta_controller import ( + TechnicalAnalysisController, + ) + + # TODO: Play with this to get correct usage + if self.to_symbol and self.from_symbol and not self.data.empty: + self.queue = self.load_class( + TechnicalAnalysisController, + ticker=f"{self.from_symbol}/{self.to_symbol}", + source=self.source, + data=self.data, + start=self.data.index[0], + interval="", + queue=self.queue, + ) + + else: + console.print("No currency pair data is loaded. Use 'load' to load data.\n") # HELP WANTED! # TODO: Add news and reddit commands back diff --git a/gamestonk_terminal/forex/forex_helper.py b/gamestonk_terminal/forex/forex_helper.py new file mode 100644 index 00000000000..33695412f7f --- /dev/null +++ b/gamestonk_terminal/forex/forex_helper.py @@ -0,0 +1,201 @@ +from datetime import datetime +from typing import List, Optional, Dict +import os +import argparse +import logging + +import yfinance as yf +import pandas as pd +import matplotlib.pyplot as plt +import mplfinance as mpf + +from gamestonk_terminal.forex import av_model +from gamestonk_terminal.rich_config import console +from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import plot_autoscale +from gamestonk_terminal.config_terminal import theme + + +FOREX_SOURCES: Dict = { + "yf": "YahooFinance", + "av": "AlphaAdvantage", +} + +SOURCES_INTERVALS: Dict = { + "yf": [ + "1min", + "2min", + "5min", + "15min", + "30min", + "60min", + "90min", + "1hour", + "1day", + "5day", + "1week", + "1month", + "3month", + ], + "av": ["1min", "5min", "15min", "30min", "60min"], +} + +INTERVAL_MAPS: Dict = { + "yf": { + "1min": "1m", + "2min": "2m", + "5min": "5m", + "15min": "15m", + "30min": "30m", + "60min": "60m", + "90min": "90m", + "1hour": "1h", + "1day": "1d", + "5day": "5d", + "1week": "1wk", + "1month": "1mo", + "3month": "3mo", + }, + "av": {"1min": 1, "5min": 5, "15min": 15, "30min": 30, "60min": 60}, +} + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def load( + to_symbol: str, + from_symbol: str, + resolution: str, + interval: str, + start_date: str, + source: str = "yf", +): + interval_map = INTERVAL_MAPS[source] + + if interval not in interval_map.keys(): + console.print( + f"Interval not supported by {FOREX_SOURCES[source]}. Need to be one of the following options", + interval_map.keys(), + ) + return pd.DataFrame() + + if source == "av": + + df = av_model.get_historical( + to_symbol=to_symbol, + from_symbol=from_symbol, + resolution=resolution, + interval=interval_map[interval], + start_date=start_date, + ) + + if source == "yf": + + df = yf.download( + f"{from_symbol}{to_symbol}=X", + end=datetime.now(), + start=datetime.strptime(start_date, "%Y-%m-%d"), + interval=interval_map[interval], + progress=False, + ) + + return df + + +@log_start_end(log=logger) +def get_yf_currency_list() -> List: + """Load YF list of forex pair a local file.""" + path = os.path.join(os.path.dirname(__file__), "data/yahoofinance_forex.json") + + return sorted(list(set(pd.read_json(path)["from_symbol"]))) + + +YF_CURRENCY_LIST = get_yf_currency_list() + + +@log_start_end(log=logger) +def check_valid_yf_forex_currency(fx_symbol: str) -> str: + """Check if given symbol is supported on Yahoo Finance + + Parameters + ---------- + fx_symbol : str + Symbol to check + + Returns + ------- + str + Currency symbol + + Raises + ------ + argparse.ArgumentTypeError + Symbol not valid on YahooFinance + """ + if fx_symbol.upper() in get_yf_currency_list(): + return fx_symbol.upper() + + raise argparse.ArgumentTypeError( + f"{fx_symbol.upper()} not found in YahooFinance supported currency codes. " + ) + + +@log_start_end(log=logger) +def display_candle( + data: pd.DataFrame, + to_symbol: str, + from_symbol: str, + external_axes: Optional[List[plt.Axes]] = None, +): + """Show candle plot for fx data. + + Parameters + ---------- + data : pd.DataFrame + Loaded fx historical data + to_symbol : str + To forex symbol + from_symbol : str + From forex symbol + external_axes: Optional[List[plt.Axes]] + External axes (1 axis are expected in the list), by default None + """ + candle_chart_kwargs = { + "type": "candle", + "style": theme.mpf_style, + "mav": (20, 50), + "volume": False, + "xrotation": theme.xticks_rotation, + "scale_padding": {"left": 0.3, "right": 1, "top": 0.8, "bottom": 0.8}, + "update_width_config": { + "candle_linewidth": 0.6, + "candle_width": 0.8, + "volume_linewidth": 0.8, + "volume_width": 0.8, + }, + "warn_too_much_data": 20000, + } + # This plot has 2 axes + if not external_axes: + candle_chart_kwargs["returnfig"] = True + candle_chart_kwargs["figratio"] = (10, 7) + candle_chart_kwargs["figscale"] = 1.10 + candle_chart_kwargs["figsize"] = plot_autoscale() + fig, ax = mpf.plot(data, **candle_chart_kwargs) + fig.suptitle( + f"{from_symbol}/{to_symbol}", + x=0.055, + y=0.965, + horizontalalignment="left", + ) + theme.visualize_output(force_tight_layout=False) + ax[0].legend() + else: + if len(external_axes) != 1: + logger.error("Expected list of 1 axis items.") + console.print("[red]Expected list of 1 axis items./n[/red]") + return + (ax1,) = external_axes + candle_chart_kwargs["ax"] = ax1 + mpf.plot(data, **candle_chart_kwargs) diff --git a/gamestonk_terminal/forex/oanda/oanda_controller.py b/gamestonk_terminal/forex/oanda/oanda_controller.py index 3c6cf6d51f8..16bfb8147ab 100644 --- a/gamestonk_terminal/forex/oanda/oanda_controller.py +++ b/gamestonk_terminal/forex/oanda/oanda_controller.py @@ -10,7 +10,8 @@ from prompt_toolkit.completion import NestedCompleter from gamestonk_terminal import config_terminal as cfg from gamestonk_terminal import feature_flags as gtff from gamestonk_terminal.decorators import log_start_end -from gamestonk_terminal.forex import av_model +from gamestonk_terminal.forex import av_model, forex_helper +from gamestonk_terminal.forex.forex_helper import FOREX_SOURCES from gamestonk_terminal.forex.oanda import oanda_view from gamestonk_terminal.helper_funcs import ( check_non_negative_float, @@ -55,6 +56,7 @@ class OandaController(BaseController): self.from_symbol = "" self.to_symbol = "" + self.source = "Oanda" self.instrument: Union[str, None] = None if session and gtff.USE_PROMPT_TOOLKIT: @@ -64,8 +66,8 @@ class OandaController(BaseController): # In Oanda they have their own list of available instruments. It would be # Great to fetch these lists and store them locally like it's done for # other currency codes (see ./av_forex_currencies.csv and how it's handled). - choices["to"] = {c: None for c in av_model.CURRENCY_LIST} - choices["from"] = {c: None for c in av_model.CURRENCY_LIST} + choices["to"] = {c: None for c in forex_helper.YF_CURRENCY_LIST} + choices["from"] = {c: None for c in forex_helper.YF_CURRENCY_LIST} self.completer = NestedCompleter.from_nested_dict(choices) def print_help(self): @@ -87,6 +89,9 @@ class OandaController(BaseController): to select the "to" currency in a forex pair[/cmds] [param]Loaded instrument: [/param]{self.instrument if self.instrument else ""}[cmds] +[param]From: [/param]{None or self.from_symbol} +[param]To: [/param]{None or self.to_symbol} +[param]Source: [/param]{None or FOREX_SOURCES[self.source]}[cmds] {has_instrument_start} candles show candles @@ -128,7 +133,9 @@ class OandaController(BaseController): self.instrument = f"{self.from_symbol}_{self.to_symbol}" console.print( - f"\nSelected pair\nFrom: {self.from_symbol}\nTo: {self.to_symbol}\n\n" + f"\nSelected pair\nFrom: {self.from_symbol}\n" + f"To: {self.to_symbol}\n" + f"Source: {FOREX_SOURCES[self.source]}\n\n" ) @log_start_end(log=logger) @@ -163,7 +170,9 @@ class OandaController(BaseController): self.instrument = f"{self.from_symbol}_{self.to_symbol}" console.print( - f"\nSelected pair\nFrom: {self.from_symbol}\nTo: {self.to_symbol}\n\n" + f"\nSelected pair\nFrom: {self.from_symbol}\n" + f"To: {self.to_symbol}\n" + f"Source: {FOREX_SOURCES[self.source]}\n\n" ) @log_start_end(log=logger) diff --git a/gamestonk_terminal/forex/technical_analysis/__init__.py b/gamestonk_terminal/forex/technical_analysis/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gamestonk_terminal/forex/technical_analysis/ta_api.py b/gamestonk_terminal/forex/technical_analysis/ta_api.py new file mode 100644 index 00000000000..b1bc7fc552a --- /dev/null +++ b/gamestonk_terminal/forex/technical_analysis/ta_api.py @@ -0,0 +1,43 @@ +"""TA context API.""" +import os +from gamestonk_terminal.helper_classes import ModelsNamespace as _models + +# flake8: noqa +# pylint: disable=unused-import + +# Menu commands +from gamestonk_terminal.common.technical_analysis.overlap_view import view_ma as ma +from gamestonk_terminal.common.technical_analysis.momentum_view import ( + display_cci as cci, +) +from gamestonk_terminal.common.technical_analysis.momentum_view import ( + display_macd as macd, +) +from gamestonk_terminal.common.technical_analysis.momentum_view import ( + display_rsi as rsi, +) +from gamestonk_terminal.common.technical_analysis.momentum_view import ( + display_stoch as stoch, +) +from gamestonk_terminal.common.technical_analysis.momentum_view import ( + display_fisher as fisher, +) +from gamestonk_terminal.common.technical_analysis.momentum_view import display_cg as cg +from gamestonk_terminal.common.technical_analysis.trend_indicators_view import ( + display_adx as adx, +) +from gamestonk_terminal.common.technical_analysis.trend_indicators_view import ( + display_aroon as aroon, +) +from gamestonk_terminal.common.technical_analysis.volatility_view import ( + display_bbands as bbands, +) +from gamestonk_terminal.common.technical_analysis.volatility_view import ( + display_donchian as donchian, +) +from gamestonk_terminal.common.technical_analysis.custom_indicators_view import ( + fibonacci_retracement as fib, +) + +# Models +models = _models(os.path.abspath(os.path.dirname(__file__))) diff --git a/gamestonk_terminal/forex/technical_analysis/ta_controller.py b/gamestonk_terminal/forex/technical_analysis/ta_controller.py new file mode 100644 index 00000000000..4d25ae2bc65 --- /dev/null +++ b/gamestonk_terminal/forex/technical_analysis/ta_controller.py @@ -0,0 +1,931 @@ +"""Crypto Technical Analysis Controller Module""" +__docformat__ = "numpy" +# pylint:disable=too-many-lines + +import argparse +import logging +from datetime import datetime +from typing import List + +import pandas as pd +from prompt_toolkit.completion import NestedCompleter + +from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.common.technical_analysis import ( + custom_indicators_view, + momentum_view, + overlap_view, + trend_indicators_view, + volatility_view, + volume_view, +) +from gamestonk_terminal.decorators import log_start_end +from gamestonk_terminal.helper_funcs import ( + EXPORT_BOTH_RAW_DATA_AND_FIGURES, + check_positive, + check_positive_list, + parse_known_args_and_warn, + valid_date, +) +from gamestonk_terminal.forex.forex_helper import FOREX_SOURCES +from gamestonk_terminal.menu import session +from gamestonk_terminal.parent_classes import StockBaseController +from gamestonk_terminal.rich_config import console + +logger = logging.getLogger(__name__) + + +class TechnicalAnalysisController(StockBaseController): + """Technical Analysis Controller class""" + + CHOICES_COMMANDS = [ + "ema", + "sma", + "zlma", + "cci", + "macd", + "rsi", + "stoch", + "fisher", + "cg", + "adx", + "aroon", + "bbands", + "donchian", + "fib", + ] + + PATH = "/forex/ta/" + + def __init__( + self, + ticker: str, + source: str, + start: datetime, + interval: str, + data: pd.DataFrame, + queue: List[str] = None, + ): + """Constructor""" + super().__init__(queue) + + self.ticker = ticker + self.source = source + self.start = start + self.interval = interval + self.data = data + self.data["Adj Close"] = data["Close"] + + if session and gtff.USE_PROMPT_TOOLKIT: + choices: dict = {c: {} for c in self.controller_choices} + self.completer = NestedCompleter.from_nested_dict(choices) + + def print_help(self): + """Print help""" + currency_str = f" {self.ticker} (from {self.start.strftime('%Y-%m-%d')})" + help_text = f"""[cmds] +[param]Currency Pair Loaded: [/param]{currency_str} +[param]Source: [/param]{FOREX_SOURCES[self.source]} + +[info]Overlap:[/info] + ema exponential moving average + sma simple moving average + zlma zero lag moving average +[info]Momentum:[/info] + cci commodity channel index + macd moving average convergence/divergence + rsi relative strength index + stoch stochastic oscillator + fisher fisher transform + cg centre of gravity +[info]Trend:[/info] + adx average directional movement index + aroon aroon indicator +[info]Volatility:[/info] + bbands bollinger bands + donchian donchian channels +[info]Custom:[/info] + fib fibonacci retracement[/cmds] +""" + console.print(text=help_text, menu="Forex - Technical Analysis") + + def custom_reset(self): + """Class specific component of reset command""" + if self.ticker: + from_symbol, to_symbol = self.ticker.split("/") + return ["forex", f"from {from_symbol}", f"to {to_symbol}", "load", "ta"] + return [] + + # TODO: Go through all models and make sure all needed columns are in dfs. + # This todo is a duplicated in crypto/ta_controller + + @log_start_end(log=logger) + def call_ema(self, other_args: List[str]): + """Process ema command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="ema", + description=""" + The Exponential Moving Average is a staple of technical + analysis and is used in countless technical indicators. In a Simple Moving + Average, each value in the time period carries equal weight, and values outside + of the time period are not included in the average. However, the Exponential + Moving Average is a cumulative calculation, including all data. Past values have + a diminishing contribution to the average, while more recent values have a greater + contribution. This method allows the moving average to be more responsive to changes + in the data. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive_list, + default=[20, 50], + help="Window lengths. Multiple values indicated as comma separated values.", + ) + + parser.add_argument( + "-o", + "--offset", + action="store", + dest="n_offset", + type=int, + default=0, + help="offset", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-l") + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + overlap_view.view_ma( + ma_type="EMA", + s_ticker=self.ticker, + series=self.data["Close"], + length=ns_parser.n_length, + offset=ns_parser.n_offset, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_sma(self, other_args: List[str]): + """Process sma command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="sma", + description=""" + Moving Averages are used to smooth the data in an array to + help eliminate noise and identify trends. The Simple Moving Average is literally + the simplest form of a moving average. Each output value is the average of the + previous n values. In a Simple Moving Average, each value in the time period carries + equal weight, and values outside of the time period are not included in the average. + This makes it less responsive to recent changes in the data, which can be useful for + filtering out those changes. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive_list, + default=[20, 50], + help="Window lengths. Multiple values indicated as comma separated values. ", + ) + + parser.add_argument( + "-o", + "--offset", + action="store", + dest="n_offset", + type=int, + default=0, + help="offset", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-l") + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + overlap_view.view_ma( + ma_type="SMA", + s_ticker=self.ticker, + series=self.data["Close"], + length=ns_parser.n_length, + offset=ns_parser.n_offset, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_zlma(self, other_args: List[str]): + """Process zlma command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="zlma", + description=""" + The zero lag exponential moving average (ZLEMA) indicator + was created by John Ehlers and Ric Way. The idea is do a + regular exponential moving average (EMA) calculation but + on a de-lagged data instead of doing it on the regular data. + Data is de-lagged by removing the data from "lag" days ago + thus removing (or attempting to) the cumulative effect of + the moving average. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive_list, + default=[20], + help="Window lengths. Multiple values indicated as comma separated values.", + ) + + parser.add_argument( + "-o", + "--offset", + action="store", + dest="n_offset", + type=int, + default=0, + help="offset", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-l") + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + overlap_view.view_ma( + ma_type="ZLMA", + s_ticker=self.ticker, + series=self.data["Close"], + length=ns_parser.n_length, + offset=ns_parser.n_offset, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_cci(self, other_args: List[str]): + """Process cci command""" + + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="cci", + description=""" + The CCI is designed to detect beginning and ending market trends. + The range of 100 to -100 is the normal trading range. CCI values outside of this + range indicate overbought or oversold conditions. You can also look for price + divergence in the CCI. If the price is making new highs, and the CCI is not, + then a price correction is likely. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=14, + help="length", + ) + parser.add_argument( + "-s", + "--scalar", + action="store", + dest="n_scalar", + type=check_positive, + default=0.015, + help="scalar", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + momentum_view.display_cci( + s_ticker=self.ticker, + ohlc=self.data, + length=ns_parser.n_length, + scalar=ns_parser.n_scalar, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_macd(self, other_args: List[str]): + """Process macd command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="macd", + description=""" + The Moving Average Convergence Divergence (MACD) is the difference + between two Exponential Moving Averages. The Signal line is an Exponential Moving + Average of the MACD. \n \n The MACD signals trend changes and indicates the start + of new trend direction. High values indicate overbought conditions, low values + indicate oversold conditions. Divergence with the price indicates an end to the + current trend, especially if the MACD is at extreme high or low values. When the MACD + line crosses above the signal line a buy signal is generated. When the MACD crosses + below the signal line a sell signal is generated. To confirm the signal, the MACD + should be above zero for a buy, and below zero for a sell. + """, + ) + parser.add_argument( + "-f", + "--fast", + action="store", + dest="n_fast", + type=check_positive, + default=12, + help="The short period.", + ) + parser.add_argument( + "-s", + "--slow", + action="store", + dest="n_slow", + type=check_positive, + default=26, + help="The long period.", + ) + parser.add_argument( + "--signal", + action="store", + dest="n_signal", + type=check_positive, + default=9, + help="The signal period.", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + momentum_view.display_macd( + s_ticker=self.ticker, + series=self.data["Adj Close"], + n_fast=ns_parser.n_fast, + n_slow=ns_parser.n_slow, + n_signal=ns_parser.n_signal, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_rsi(self, other_args: List[str]): + """Process rsi command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="rsi", + description=""" + The Relative Strength Index (RSI) calculates a ratio of the + recent upward price movements to the absolute price movement. The RSI ranges + from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when + the value is over 70/below 30. You can also look for divergence with price. If + the price is making new highs/lows, and the RSI is not, it indicates a reversal. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=14, + help="length", + ) + parser.add_argument( + "-s", + "--scalar", + action="store", + dest="n_scalar", + type=check_positive, + default=100, + help="scalar", + ) + parser.add_argument( + "-d", + "--drift", + action="store", + dest="n_drift", + type=check_positive, + default=1, + help="drift", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-l") + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + momentum_view.display_rsi( + s_ticker=self.ticker, + series=self.data["Adj Close"], + length=ns_parser.n_length, + scalar=ns_parser.n_scalar, + drift=ns_parser.n_drift, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_stoch(self, other_args: List[str]): + """Process stoch command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="stoch", + description=""" + The Stochastic Oscillator measures where the close is in relation + to the recent trading range. The values range from zero to 100. %D values over 75 + indicate an overbought condition; values under 25 indicate an oversold condition. + When the Fast %D crosses above the Slow %D, it is a buy signal; when it crosses + below, it is a sell signal. The Raw %K is generally considered too erratic to use + for crossover signals. + """, + ) + parser.add_argument( + "-k", + "--fastkperiod", + action="store", + dest="n_fastkperiod", + type=check_positive, + default=14, + help="The time period of the fastk moving average", + ) + parser.add_argument( + "-d", + "--slowdperiod", + action="store", + dest="n_slowdperiod", + type=check_positive, + default=3, + help="The time period of the slowd moving average", + ) + parser.add_argument( + "--slowkperiod", + action="store", + dest="n_slowkperiod", + type=check_positive, + default=3, + help="The time period of the slowk moving average", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + momentum_view.display_stoch( + s_ticker=self.ticker, + ohlc=self.data, + fastkperiod=ns_parser.n_fastkperiod, + slowdperiod=ns_parser.n_slowdperiod, + slowkperiod=ns_parser.n_slowkperiod, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_fisher(self, other_args: List[str]): + """Process fisher command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="fisher", + description=""" + The Fisher Transform is a technical indicator created by John F. Ehlers + that converts prices into a Gaussian normal distribution.1 The indicator + highlights when prices have moved to an extreme, based on recent prices. + This may help in spotting turning points in the price of an asset. It also + helps show the trend and isolate the price waves within a trend. + """, + ) + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=14, + help="length", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-l") + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + momentum_view.display_fisher( + s_ticker=self.ticker, + ohlc=self.data, + length=ns_parser.n_length, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_cg(self, other_args: List[str]): + """Process cg command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="cg", + description=""" + The Center of Gravity indicator, in short, is used to anticipate future price movements + and to trade on price reversals as soon as they happen. However, just like other oscillators, + the COG indicator returns the best results in range-bound markets and should be avoided when + the price is trending. Traders who use it will be able to closely speculate the upcoming + price change of the asset. + """, + ) + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=14, + help="length", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-l") + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + momentum_view.display_cg( + s_ticker=self.ticker, + series=self.data["Adj Close"], + length=ns_parser.n_length, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_adx(self, other_args: List[str]): + """Process adx command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="adx", + description=""" + The ADX is a Welles Wilder style moving average of the Directional Movement Index (DX). + The values range from 0 to 100, but rarely get above 60. To interpret the ADX, consider + a high number to be a strong trend, and a low number, a weak trend. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=14, + help="length", + ) + + parser.add_argument( + "-s", + "--scalar", + action="store", + dest="n_scalar", + type=check_positive, + default=100, + help="scalar", + ) + + parser.add_argument( + "-d", + "--drift", + action="store", + dest="n_drift", + type=check_positive, + default=1, + help="drift", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + trend_indicators_view.display_adx( + s_ticker=self.ticker, + ohlc=self.data, + length=ns_parser.n_length, + scalar=ns_parser.n_scalar, + drift=ns_parser.n_drift, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_aroon(self, other_args: List[str]): + """Process aroon command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="aroon", + description=""" + The word aroon is Sanskrit for "dawn's early light." The Aroon + indicator attempts to show when a new trend is dawning. The indicator consists + of two lines (Up and Down) that measure how long it has been since the highest + high/lowest low has occurred within an n period range. \n \n When the Aroon Up is + staying between 70 and 100 then it indicates an upward trend. When the Aroon Down + is staying between 70 and 100 then it indicates an downward trend. A strong upward + trend is indicated when the Aroon Up is above 70 while the Aroon Down is below 30. + Likewise, a strong downward trend is indicated when the Aroon Down is above 70 while + the Aroon Up is below 30. Also look for crossovers. When the Aroon Down crosses above + the Aroon Up, it indicates a weakening of the upward trend (and vice versa). + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=25, + help="length", + ) + + parser.add_argument( + "-s", + "--scalar", + action="store", + dest="n_scalar", + type=check_positive, + default=100, + help="scalar", + ) + + parser.add_argument( + "-o", + "--offset", + action="store", + dest="n_offset", + type=check_positive, + default=0, + help="offset", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + trend_indicators_view.display_aroon( + s_ticker=self.ticker, + ohlc=self.data, + length=ns_parser.n_length, + scalar=ns_parser.n_scalar, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_bbands(self, other_args: List[str]): + """Process bbands command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="bbands", + description=""" + Bollinger Bands consist of three lines. The middle band is a simple + moving average (generally 20 periods) of the typical price (TP). The upper and lower + bands are F standard deviations (generally 2) above and below the middle band. + The bands widen and narrow when the volatility of the price is higher or lower, + respectively. \n \nBollinger Bands do not, in themselves, generate buy or sell signals; + they are an indicator of overbought or oversold conditions. When the price is near the + upper or lower band it indicates that a reversal may be imminent. The middle band + becomes a support or resistance level. The upper and lower bands can also be + interpreted as price targets. When the price bounces off of the lower band and crosses + the middle band, then the upper band becomes the price target. + """, + ) + + parser.add_argument( + "-l", + "--length", + action="store", + dest="n_length", + type=check_positive, + default=5, + help="length", + ) + + parser.add_argument( + "-s", + "--std", + action="store", + dest="n_std", + type=check_positive, + default=2, + help="std", + ) + + parser.add_argument( + "-m", + "--mamode", + action="store", + dest="s_mamode", + default="sma", + help="mamode", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + volatility_view.display_bbands( + ticker=self.ticker, + ohlc=self.data, + length=ns_parser.n_length, + n_std=ns_parser.n_std, + mamode=ns_parser.s_mamode, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_donchian(self, other_args: List[str]): + """Process donchian command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="donchian", + description=""" + Donchian Channels are three lines generated by moving average + calculations that comprise an indicator formed by upper and lower + bands around a midrange or median band. The upper band marks the + highest price of a security over N periods while the lower band + marks the lowest price of a security over N periods. The area + between the upper and lower bands represents the Donchian Channel. + """, + ) + + parser.add_argument( + "-u", + "--length_upper", + action="store", + dest="n_length_upper", + type=check_positive, + default=20, + help="length", + ) + + parser.add_argument( + "-l", + "--length_lower", + action="store", + dest="n_length_lower", + type=check_positive, + default=20, + help="length", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + volatility_view.display_donchian( + ticker=self.ticker, + ohlc=self.data, + upper_length=ns_parser.n_length_upper, + lower_length=ns_parser.n_length_lower, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_ad(self, other_args: List[str]): + """Process ad command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="ad", + description=""" + The Accumulation/Distribution Line is similar to the On Balance + Volume (OBV), which sums the volume times +1/-1 based on whether the close is + higher than the previous close. The Accumulation/Distribution indicator, however + multiplies the volume by the close location value (CLV). The CLV is based on the + movement of the issue within a single bar and can be +1, -1 or zero. \n \n + The Accumulation/Distribution Line is interpreted by looking for a divergence in + the direction of the indicator relative to price. If the Accumulation/Distribution + Line is trending upward it indicates that the price may follow. Also, if the + Accumulation/Distribution Line becomes flat while the price is still rising (or falling) + then it signals an impending flattening of the price. + """, + ) + + parser.add_argument( + "--open", + action="store_true", + default=False, + dest="b_use_open", + help="uses open value of data", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + volume_view.display_ad( + s_ticker=self.ticker, + ohlc=self.data, + use_open=ns_parser.b_use_open, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_obv(self, other_args: List[str]): + """Process obv command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="obv", + description=""" + The On Balance Volume (OBV) is a cumulative total of the up and + down volume. When the close is higher than the previous close, the volume is added + to the running total, and when the close is lower than the previous close, the volume + is subtracted from the running total. \n \n To interpret the OBV, look for the OBV + to move with the price or precede price moves. If the price moves before the OBV, + then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the + OBV indicates a strong trend. If the OBV is flat, then the market is not trending. + """, + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + volume_view.display_obv( + s_ticker=self.ticker, + ohlc=self.data, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_fib(self, other_args: List[str]): + """Process fib command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="fib", + description="Calculates the fibonacci retracement levels", + ) + + parser.add_argument( + "-p", + "--period", + dest="period", + type=int, + help="Days to lookback for retracement", + default=120, + ) + + parser.add_argument( + "--start", + dest="start", + type=valid_date, + help="Starting date to select", + required="--end" in other_args, + ) + + parser.add_argument( + "--end", + dest="end", + type=valid_date, + help="Ending date to select", + required="--start" in other_args, + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + ) + if ns_parser: + custom_indicators_view.fibonacci_retracement( + s_ticker=self.ticker, + ohlc=self.data, + period=ns_parser.period, + start_date=ns_parser.start, + end_date=ns_parser.end, + export=ns_parser.export, + ) diff --git a/gamestonk_terminal/jupyter/dashboards/crypto.ipynb b/gamestonk_terminal/jupyter/dashboards/crypto.ipynb new file mode 100644 index 00000000000..830e666a7db --- /dev/null +++ b/gamestonk_terminal/jupyter/dashboards/crypto.ipynb @@ -0,0 +1,160 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6e198e40-0c33-45b3-a79b-2d9ee9eff7b9", + "metadata": { + "tags": [] + }, + "source": [ + "# Cryptocurrency exchange rates to USD ($)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "import ipywidgets as ipw\n", + "from IPython.display import display\n", + "import pandas as pd\n", + "\n", + "from gamestonk_terminal import api as gst\n", + "\n", + "ipw.HTML(f\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcbbf867-af50-4d06-ae5c-7c09647aa55d", + "metadata": {}, + "outputs": [], + "source": [ + "COIN_LIST = (\n", + " \"BTC,ETH,USDT,BNB,USDC,XRP,LUNA,ADA,\"\n", + " + \"SOL,AVAX,DOT,BUSD,DOGE,UST,SHIB,WBTC,\"\n", + " + \"MATIC,CRO,DAI,LTC,ATOM,NEAR,LINK,BCH,\"\n", + " + \"UNI,TRX,FTT,ETC,LEO,ALGO,XLM,MANA,\"\n", + " + \"BTCB,HBAR,EGLD,ICP,SAND,XMR,WAVES,VET,\"\n", + " + \"APE,FIL,FTM,AXS,THETA,KLAY,XTZ,RUNE\"\n", + ").split(\",\")\n", + "exchange_rates = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deeeb80a-3675-44fb-9a1b-2325d8da4e7d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_exchange_rate(coin: str, days: int = 2):\n", + " \"\"\"Get exchange rate for a cryptocurrency agains USD.\"\"\"\n", + " (_, _, _, _, current_df, _,) = gst.crypto.load(\n", + " coin=coin,\n", + " source=\"cp\",\n", + " days=days,\n", + " vs=\"usd\",\n", + " interval=\"1day\",\n", + " should_load_ta_data=True,\n", + " )\n", + " price_color = \"neutral_color\"\n", + " if current_df[\"Close\"].iloc[-1] > current_df[\"Close\"].iloc[-2]:\n", + " price_color = \"up_color\"\n", + " elif current_df[\"Close\"].iloc[-1] < current_df[\"Close\"].iloc[-2]:\n", + " price_color = \"down_color\"\n", + " price = str(current_df[\"Close\"].iloc[-1])[:7]\n", + "\n", + " return price, price_color" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18acf5cb-6cbf-484d-8042-107c030098de", + "metadata": {}, + "outputs": [], + "source": [ + "def show_rates(btn):\n", + " \"\"\"Show exchange rates in grid box widget.\"\"\"\n", + " grid.children = ()\n", + " for coin in COIN_LIST:\n", + " if coin not in exchange_rates.keys():\n", + " exchange_rates[coin] = {\"price\": None, \"color\": None}\n", + " try:\n", + " price, price_color = get_exchange_rate(coin=coin)\n", + " except:\n", + " price, price_color = \"-------\", \"neutral_color\"\n", + " widgets = list(grid.children)\n", + " widgets.append(\n", + " ipw.HTML(\n", + " gst.widgets.price_card(\n", + " ticker=coin,\n", + " price=price,\n", + " price_color=price_color,\n", + " )\n", + " )\n", + " )\n", + " grid.children = tuple(widgets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf2021e9-de8a-466e-b341-fcd6abda64b5", + "metadata": {}, + "outputs": [], + "source": [ + "update_rates = ipw.Button(description=\"Update rates\")\n", + "update_rates.on_click(show_rates)\n", + "header = ipw.HBox([update_rates])\n", + "\n", + "layout = ipw.Layout(grid_template_columns=\"1fr \" * 8)\n", + "grid = ipw.GridBox(\n", + " [],\n", + " layout=layout,\n", + ")\n", + "\n", + "display(header, grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19dfcbfe-eaec-406e-bb3c-72d1b3538a6e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "voila": { + "theme": "dark" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gamestonk_terminal/jupyter/dashboards/dashboards_controller.py b/gamestonk_terminal/jupyter/dashboards/dashboards_controller.py index 510d8e0cf35..c19ce5af98d 100644 --- a/gamestonk_terminal/jupyter/dashboards/dashboards_controller.py +++ b/gamestonk_terminal/jupyter/dashboards/dashboards_controller.py @@ -31,9 +31,9 @@ class DashboardsController(BaseController): "vsurf", "chains", "shortdata", - "currencies", + "crypto", ] - PATH = "/jupyter/dashboard/" + PATH = "/jupyter/dashboards/" def __init__(self, queue: List[str] = None): """Constructor""" @@ -51,7 +51,7 @@ class DashboardsController(BaseController): vsurf options volatility surface chains options chain analysis shortdata finra shortdata analysis - currencies forex currency exchange dashboard[/cmds] + crypto cryptocurrency exchange rates against USD[/cmds] """ console.print(text=help_text, menu="Jupyter - Dashboards") @@ -81,9 +81,9 @@ class DashboardsController(BaseController): create_call(other_args, "shortdata", "") @log_start_end(log=logger) - def call_currencies(self, other_args: List[str]): - """Process currencies command""" - create_call(other_args, "currencies", "") + def call_crypto(self, other_args: List[str]): + """Process crypto command""" + create_call(other_args, "crypto", "") def create_call(other_args: List[str], name: str, filename: str = None) -> None: diff --git a/gamestonk_terminal/jupyter/reports/crypto_market.ipynb b/gamestonk_terminal/jupyter/reports/crypto_market.ipynb index 5f68c0e594b..a4a1878a1e8 100644 --- a/gamestonk_terminal/jupyter/reports/crypto_market.ipynb +++ b/gamestonk_terminal/jupyter/reports/crypto_market.ipynb @@ -265,7 +265,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/gamestonk_terminal/jupyter/reports/due_diligence.ipynb b/gamestonk_terminal/jupyter/reports/due_diligence.ipynb deleted file mode 100644 index 0aab454c946..00000000000 --- a/gamestonk_terminal/jupyter/reports/due_diligence.ipynb +++ /dev/null @@ -1,299 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "phantom-facility", - "metadata": {}, - "outputs": [], - "source": [ - "# Import most generic modules\n", - "import importlib\n", - "import pathlib\n", - "import os\n", - "import sys\n", - "from datetime import datetime, timedelta\n", - "import pandas as pd\n", - "from IPython.display import display, Markdown\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "module_path = os.path.abspath(os.path.join(\"../..\"))\n", - "\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "compact-count", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# Parameters that will be replaced when calling this notebook\n", - "ticker = \"AAPL\"\n", - "\n", - "report_name = (\n", - " f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_due_diligence_{ticker.upper()}\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "possible-constraint", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " Markdown(\n", - " f\"# {ticker.upper()} - Due Diligence - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a473d25", - "metadata": {}, - "outputs": [], - "source": [ - "base_path = os.path.abspath(os.path.join(\".\"))\n", - "report_cache_dir = pathlib.Path(base_path, \"stored\")\n", - "\n", - "if not os.path.isdir(report_cache_dir):\n", - " print(\"Reports data directory not found.\")\n", - " print(f\"Creating {report_cache_dir}\")\n", - " os.mkdir(report_cache_dir)\n", - "else:\n", - " print(f\"Found reports directory {report_cache_dir}\")" - ] - }, - { - "cell_type": "markdown", - "id": "temporal-clerk", - "metadata": {}, - "source": [ - "## Candle View" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d783ed59-1fb2-44f2-b4b0-bfdfee9afcb6", - "metadata": {}, - "outputs": [], - "source": [ - "from gamestonk_terminal.stocks import stocks_helper\n", - "\n", - "df_stock_cache = pathlib.Path(report_cache_dir, f\"{ticker}_stock_data.pkl\")\n", - "if os.path.isfile(df_stock_cache):\n", - " print(f\"Found a cache file. Loading {df_stock_cache}\")\n", - " df_stock = pd.read_pickle(df_stock_cache)\n", - "else:\n", - " print(\"Cache file not found. Getting data.\")\n", - " df_stock = stocks_helper.load_ticker(\n", - " ticker, (datetime.now() - timedelta(days=180)).strftime(\"%Y-%m-%d\")\n", - " )\n", - " df_stock = stocks_helper.find_trendline(df_stock, \"OC_High\", \"high\")\n", - " df_stock = stocks_helper.find_trendline(df_stock, \"OC_Low\", \"low\")\n", - " print(f\"Saving cache file {ticker}_stock_data.pkl\")\n", - " df_stock.to_pickle(df_stock_cache)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "transsexual-trance", - "metadata": {}, - "outputs": [], - "source": [ - "import mplfinance as mpf\n", - "\n", - "mc = mpf.make_marketcolors(\n", - " up=\"green\", down=\"red\", edge=\"black\", wick=\"black\", volume=\"in\", ohlc=\"i\"\n", - ")\n", - "s = mpf.make_mpf_style(marketcolors=mc, gridstyle=\":\", y_on_right=True)\n", - "\n", - "ap0 = []\n", - "\n", - "if \"OC_High_trend\" in df_stock.columns:\n", - " ap0.append(\n", - " mpf.make_addplot(df_stock[\"OC_High_trend\"], color=\"g\"),\n", - " )\n", - "\n", - "if \"OC_Low_trend\" in df_stock.columns:\n", - " ap0.append(\n", - " mpf.make_addplot(df_stock[\"OC_Low_trend\"], color=\"b\"),\n", - " )\n", - "\n", - "mpf.plot(\n", - " df_stock,\n", - " type=\"candle\",\n", - " mav=(20, 50, 200),\n", - " volume=True,\n", - " addplot=ap0,\n", - " xrotation=0,\n", - " style=s,\n", - " figratio=(10, 7),\n", - " figscale=2.00,\n", - " update_width_config=dict(\n", - " candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "political-intake", - "metadata": {}, - "source": [ - "## Analyst Targets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "numerous-begin", - "metadata": {}, - "outputs": [], - "source": [ - "from gamestonk_terminal.stocks.due_diligence import finviz_view\n", - "\n", - "try:\n", - " finviz_view.analyst(ticker=ticker, export=\"\")\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "3cfd302c", - "metadata": {}, - "source": [ - "## Fundamentals" - ] - }, - { - "cell_type": "markdown", - "id": "written-lease", - "metadata": {}, - "source": [ - "### Annual Income Statement" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "played-vancouver", - "metadata": {}, - "outputs": [], - "source": [ - "from gamestonk_terminal.stocks.fundamental_analysis import av_view\n", - "\n", - "av_view.display_income_statement(ticker, limit=1)" - ] - }, - { - "cell_type": "markdown", - "id": "smooth-forge", - "metadata": {}, - "source": [ - "### Quarterly Income Statement" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "juvenile-throw", - "metadata": {}, - "outputs": [], - "source": [ - "from gamestonk_terminal.stocks.fundamental_analysis import av_view\n", - "\n", - "av_view.display_income_statement(ticker, limit=1, quarterly=True)" - ] - }, - { - "cell_type": "markdown", - "id": "passive-nerve", - "metadata": {}, - "source": [ - "### Annual Balance Sheet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "nominated-empire", - "metadata": {}, - "outputs": [], - "source": [ - "from gamestonk_terminal.stocks.fundamental_analysis import av_view\n", - "\n", - "av_view.display_balance_sheet(ticker, limit=1)" - ] - }, - { - "cell_type": "markdown", - "id": "special-bargain", - "metadata": {}, - "source": [ - "### Annual Cash Flow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "focused-thanks", - "metadata": {}, - "outputs": [], - "source": [ - "from gamestonk_terminal.stocks.fundamental_analysis import av_view\n", - "\n", - "av_view.display_cash_flow(ticker, limit=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sought-phase", - "metadata": {}, - "outputs": [], - "source": [ - "!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "interpreter": { - "hash": "bdefb52be37b6ab26a82d292d220aaeebafdd00af58c431a8f759eb373ff9ec1" - }, - "kernelspec": { - "display_name": "Python 3.8.12 ('gst')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/gamestonk_terminal/jupyter/reports/due_dilligence.ipynb b/gamestonk_terminal/jupyter/reports/due_dilligence.ipynb new file mode 100644 index 00000000000..cf0e8ef4952 --- /dev/null +++ b/gamestonk_terminal/jupyter/reports/due_dilligence.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70ba2c8a", + "metadata": {}, + "source": [ + "## Notebook setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "driven-billion", + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import warnings\n", + "from datetime import datetime, timedelta\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib_inline.backend_inline\n", + "\n", + "from gamestonk_terminal import api as gst\n", + "from gamestonk_terminal.helper_classes import TerminalStyle\n", + "\n", + "%matplotlib inline\n", + "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", + "except:\n", + " pass\n", + "stylesheet = gst.widgets.html_report_stylesheet()" + ] + }, + { + "cell_type": "markdown", + "id": "807fda4e", + "metadata": {}, + "source": [ + "## Select Ticker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "intended-sherman", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters that will be replaced when calling this notebook\n", + "ticker = \"AMC\"\n", + "report_name = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", + "metadata": {}, + "outputs": [], + "source": [ + "ticker_data = gst.stocks.load(ticker, start=datetime.now() - timedelta(days=365))\n", + "ticker_data = gst.stocks.process_candle(ticker_data)\n", + "\n", + "report_title = (f\"{ticker.upper()} Due Diligence report ({datetime.now().strftime('%Y-%m-%d %H:%M:%S')})\")\n", + "report_title" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb", + "metadata": {}, + "outputs": [], + "source": [ + "overview = gst.stocks.fa.models.yahoo_finance.get_info(ticker=ticker).transpose()[\n", + " \"Long business summary\"\n", + "][0]\n", + "overview" + ] + }, + { + "cell_type": "markdown", + "id": "f8a0d747", + "metadata": {}, + "source": [ + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aca4b6ef", + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " df_year_estimates,\n", + " df_quarter_earnings,\n", + " df_quarter_revenues,\n", + ") = gst.stocks.dd.models.business_insider.get_estimates(ticker)" + ] + }, + { + "cell_type": "markdown", + "id": "b9c8feff", + "metadata": { + "tags": [] + }, + "source": [ + "### 1. Yearly Estimates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0f0a1ca", + "metadata": {}, + "outputs": [], + "source": [ + "display_year = sorted(df_year_estimates.columns.tolist())[:3]\n", + "df_year_estimates = df_year_estimates[display_year].head(5)\n", + "df_year_estimates" + ] + }, + { + "cell_type": "markdown", + "id": "64d3d490", + "metadata": { + "tags": [] + }, + "source": [ + "### 2. Quarterly Earnings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fde46353", + "metadata": {}, + "outputs": [], + "source": [ + "df_quarter_earnings" + ] + }, + { + "cell_type": "markdown", + "id": "d13a1e5e", + "metadata": { + "tags": [] + }, + "source": [ + "### 3. Quarterly Revenues" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51dfd9d2", + "metadata": {}, + "outputs": [], + "source": [ + "df_quarter_revenues" + ] + }, + { + "cell_type": "markdown", + "id": "6303e520", + "metadata": { + "tags": [] + }, + "source": [ + "### 4. SEC Filings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3022130e", + "metadata": {}, + "outputs": [], + "source": [ + "df_sec_filings = gst.stocks.dd.models.marketwatch.get_sec_filings(ticker)[\n", + " [\"Type\", \"Category\", \"Link\"]\n", + "].head(5)\n", + "df_sec_filings" + ] + }, + { + "cell_type": "markdown", + "id": "1e201003", + "metadata": { + "tags": [] + }, + "source": [ + "### 5. Analyst Ratings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4662b1ac", + "metadata": {}, + "outputs": [], + "source": [ + "df_analyst = gst.stocks.dd.models.finviz.get_analyst_data(ticker)\n", + "df_analyst[\"target_to\"] = df_analyst[\"target_to\"].combine_first(df_analyst[\"target\"])\n", + "df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target_to\"]].rename(\n", + " columns={\n", + " \"category\": \"Category\",\n", + " \"analyst\": \"Analyst\",\n", + " \"rating\": \"Rating\",\n", + " \"target_to\": \"Price Target\",\n", + " }\n", + ")\n", + "df_analyst" + ] + }, + { + "cell_type": "markdown", + "id": "ade589d6-d80f-4651-acb7-929943114ef0", + "metadata": {}, + "source": [ + "## Plots" + ] + }, + { + "cell_type": "markdown", + "id": "621ed931-57e9-4ca6-afe8-8ef278122c77", + "metadata": {}, + "source": [ + "### 1. Price history" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "394668e1-1158-4a2b-8a38-04c72a6abdd6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (candles, volume) = plt.subplots(nrows=2, ncols=1, figsize=(5, 3), dpi=150)\n", + "gst.stocks.candle(\n", + " s_ticker=ticker,\n", + " df_stock=ticker_data,\n", + " use_matplotlib=True,\n", + " external_axes=[candles, volume],\n", + ")\n", + "candles.set_xticklabels(\"\")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "bf07a909", + "metadata": {}, + "source": [ + "### 2. Price Target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "900686a3", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n", + "gst.stocks.dd.pt(\n", + " ticker=ticker,\n", + " start=\"2021-10-25\",\n", + " interval=\"1440min\",\n", + " stock=ticker_data,\n", + " num=10,\n", + " raw=False,\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_target_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "63c0538c", + "metadata": {}, + "source": [ + "### 3. Ratings over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3733ff2", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n", + "gst.stocks.dd.rot(\n", + " ticker=ticker,\n", + " num=10,\n", + " raw=False,\n", + " export=\"\",\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "ratings_over_time_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac501a32-124d-4d21-aae1-a8d147bb1071", + "metadata": {}, + "source": [ + "## Render the report template to a file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mounted-phase", + "metadata": {}, + "outputs": [], + "source": [ + "body = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12", + "metadata": {}, + "outputs": [], + "source": [ + "# Title\n", + "body += gst.widgets.h(1, report_title)\n", + "\n", + "body += gst.widgets.h(2, \"Overview\")\n", + "body += gst.widgets.row([gst.widgets.p(overview)])\n", + "\n", + "\n", + "# Analysts ratings\n", + "body += gst.widgets.h(2, \"Analyst assessments\")\n", + "body += gst.widgets.row([price_target_chart])\n", + "body += gst.widgets.row([df_analyst.to_html()])\n", + "body += gst.widgets.row([ratings_over_time_chart])\n", + "\n", + "# Price history and yearly estimates\n", + "body += gst.widgets.row(\n", + " [\n", + " gst.widgets.h(3, \"Price history\") + price_chart,\n", + " gst.widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html(),\n", + " ]\n", + ")\n", + "\n", + "# Earnings and revenues\n", + "body += gst.widgets.h(2, \"Earnings and revenues\")\n", + "body += gst.widgets.row([df_quarter_earnings.head().to_html()])\n", + "body += gst.widgets.row([df_quarter_revenues.head().to_html()])\n", + "\n", + "# Sec filings and insider trading\n", + "body += gst.widgets.h(2, \"SEC filings\")\n", + "body += gst.widgets.row([df_sec_filings.to_html()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb26492f-d13f-4302-b36d-ea8ee0020f82", + "metadata": {}, + "outputs": [], + "source": [ + "report = gst.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee07c30c-9b38-4035-b128-20b7c9989951", + "metadata": {}, + "outputs": [], + "source": [ + "# to save the results\n", + "with open(report_name + \".html\", \"w\") as fh:\n", + " fh.write(report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34991096-bc3a-4134-b519-aa3a490abe98", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "interpreter": { + "hash": "31090e8428bc3cbe850895ffb75aca3a30f914a21038b56ab61f04d2ddd7301b" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gamestonk_terminal/jupyter/reports/reports_controller.py b/gamestonk_terminal/jupyter/reports/reports_controller.py index 3c4edb365c4..f84e61f6e63 100644 --- a/gamestonk_terminal/jupyter/reports/reports_controller.py +++ b/gamestonk_terminal/jupyter/reports/reports_controller.py @@ -133,7 +133,7 @@ Select one of the following reports:[/info][cmds] (known_args, other_args) = self.parser.parse_known_args(an_input.split()) - if not other_args and an_input in ("1", "3", "4"): + if not other_args and an_input in ("2", "3", "5"): console.print("[red]Error: No ticker provided\n[/red]") logger.error("Exception: No ticker provided") return self.queue diff --git a/gamestonk_terminal/jupyter/widget_helpers.py b/gamestonk_terminal/jupyter/widget_helpers.py new file mode 100644 index 00000000000..090a5ce87ae --- /dev/null +++ b/gamestonk_terminal/jupyter/widget_helpers.py @@ -0,0 +1,97 @@ +"""Widgets Helper Library. + +A library of `ipywidgets` wrappers for notebook based reports and voila dashboards. +The library includes both python code and html/css/js elements that can be found in the +`./widgets` folder. +""" +import os +from typing import List +from jinja2 import Template + + +def price_card_stylesheet(): + """Load a default CSS stylesheet from file.""" + with open( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "widgets", "style.css") + ) as f: + style = f.read() + return style + + +def price_card(ticker: str, price: str, price_color: str = "neutral_color") -> str: + """Prepare a styled HTML element of a 128 by 128 price card. + + Parameters + ---------- + ticker : str + Instrument ticker for the price card + price : str + Instrument price as a string + price_color : str, optional + The color of the price. Accepts "up_color", "down_color" and default "neutral_color" + + Returns + ------- + str + HTML code as string + """ + with open( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "widgets", "card.j2") + ) as f: + template = Template(f.read()) + card = template.render(ticker=ticker, price=price, price_color=price_color) + return card + + +def html_report_stylesheet(): + """Load a default CSS stylesheet from file.""" + with open( + os.path.join( + os.path.dirname(os.path.abspath(__file__)), "widgets", "report.css" + ) + ) as f: + style = f.read() + return style + + +def html_report(title: str = "", stylesheet: str = "", body: str = "") -> str: + """Prepare a styled HTML page element. + + Parameters + ---------- + title : str, optional + Contents of the title tag, by default "" + stylesheet : str, optional + Contents of the stylesheet tag, by default "" + body : str, optional + Contents of the body tag, by default "" + + Returns + ------- + str + HTML code as string + """ + with open( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "widgets", "report.j2") + ) as f: + template = Template(f.read()) + card = template.render(title=title, stylesheet=stylesheet, body=body) + return card + + +def h(level: int, text: str) -> str: + """Wrap text into an HTML `h` tag.""" + return f"{text}" + + +def p(text: str) -> str: + """Wrap text into an HTML `p` tag.""" + return f"

{text}

" + + +def row(elements: List) -> str: + with open( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "widgets", "row.j2") + ) as f: + template = Template(f.read()) + return template.render(elements=elements) diff --git a/gamestonk_terminal/jupyter/widgets/card.j2 b/gamestonk_terminal/jupyter/widgets/card.j2 new file mode 100644 index 00000000000..09e630ad46b --- /dev/null +++ b/gamestonk_terminal/jupyter/widgets/card.j2 @@ -0,0 +1,8 @@ +
+
+
+

{{ ticker }}

+

{{ price }}

+
+
+
diff --git a/gamestonk_terminal/jupyter/widgets/report.css b/gamestonk_terminal/jupyter/widgets/report.css new file mode 100644 index 00000000000..07a9fcadab3 --- /dev/null +++ b/gamestonk_terminal/jupyter/widgets/report.css @@ -0,0 +1,224 @@ +.body { + background-color: #ffffff; + color: #000000; + font-weight: 400; + text-decoration: none; + vertical-align: baseline; + font-size: 12pt; + font-family: 'Consolas', Arial, Helvetica, sans-serif; + font-style: normal; + margin-top: 30px; + margin-bottom: 30px; + margin-left: 50px; + margin-left: 50px; + padding: 0 +} + +.title { + padding-top: 0pt; + color: #000000; + font-size: 42pt; + font-weight: 800; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + text-align: center +} + +.subtitle { + padding-top: 0pt; + color: #666666; + font-size: 33pt; + padding-bottom: 10pt; + line-height: 1.15; + page-break-after: avoid; + font-style: italic; + text-align: center +} + +li { + color: #000000; + font-size: 13pt; +} + +p { + margin: 0; + color: #000000; + font-size: 13pt; +} + +h1 { + padding-top: 10pt; + color: #000000; + font-size: 28pt; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + text-align: left +} + +h2 { + padding-top: 10pt; + color: #000000; + font-weight: 700; + font-size: 21pt; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + text-align: left +} + +h3 { + padding-top: 8pt; + color: #666666; + font-weight: 700; + font-size: 14pt; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + text-align: left +} + +h4 { + padding-top: 8pt; + -webkit-text-decoration-skip: none; + color: #666666; + text-decoration: underline; + font-size: 13pt; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + text-decoration-skip-ink: none; + text-align: left +} + +h5 { + padding-top: 8pt; + color: #666666; + font-size: 13pt; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + text-align: left +} + +h6 { + padding-top: 8pt; + color: #666666; + font-size: 13pt; + padding-bottom: 0pt; + line-height: 1.15; + page-break-after: avoid; + font-style: italic; + text-align: left +} + +table { + border-collapse: collapse; + border-spacing: 0; + border-width: 0; + border-top-color: #ffffff; + border-bottom-color: #ffffff; + border-left-color: #ffffff; + border-right-color: #ffffff; + color: black; + table-layout: fixed; + margin-left: 0; + margin-right: 0; + margin-top: 15pt; + margin-bottom: 30pt; + padding: 3pt; +} + +table thead { + color: black; + text-align: center; +} + +table thead tr th { + border-top: 0; + border-bottom: 0; + border-left: 1; + border-left-color: #000000; + border-right: 1; + border-right-color: #000000; + padding-right: 5pt; + padding-left: 5pt; + font-weight: 600; +} + +table thead tr th:first-child { + border-left: 0; +} + +table thead tr th:last-child { + border-right: 0; +} + +table tbody { + color: black; +} + +table tbody tr td { + border: 1; + border-color: #000000; + padding-left: 7pt; + padding-right: 7pt; +} + +table tbody tr th { + border: 1; + border-color: #000000; + font-weight: 200; + text-align: right; + padding-left: 7pt; + padding-right: 7pt; +} + +table tbody tr th:first-child { + border-left: 0; +} + +table tbody tr th:last-child { + border-right: 0; +} + +table tbody tr:last-child th { + border-bottom: 0; +} + +table tbody tr td:first-child { + border-left: 0; +} + +table tbody tr td:last-child { + border-right: 0; +} + +table tbody tr:last-child td { + border-bottom: 0; +} + +tbody tr:nth-child(even) { + background-color: rgb(216, 216, 216); +} + +@media print { + table { + -webkit-print-color-adjust: exact; + } +} + +.hcontainer { + margin: 10pt; + width: 1000px; +} + +.item { + display: inline-block; + *display: inline; + zoom: 1; + padding-left: 10pt; + padding-right: 10pt; + vertical-align: top; +} diff --git a/gamestonk_terminal/jupyter/widgets/report.j2 b/gamestonk_terminal/jupyter/widgets/report.j2 new file mode 100644 index 00000000000..718424f8136 --- /dev/null +++ b/gamestonk_terminal/jupyter/widgets/report.j2 @@ -0,0 +1,19 @@ + + + + + {% if title %} + {{ title }} + {% else %} + Gamestonk Terminal + {% endif %} + {% if stylesheet %} + + {% endif %} + + + {{ body }} + + diff --git a/gamestonk_terminal/jupyter/widgets/row.j2 b/gamestonk_terminal/jupyter/widgets/row.j2 new file mode 100644 index 00000000000..5e8a5a6bb4d --- /dev/null +++ b/gamestonk_terminal/jupyter/widgets/row.j2 @@ -0,0 +1,5 @@ +
+ {% for item in elements %} +
{{ item }}
+ {% endfor %} +
diff --git a/gamestonk_terminal/jupyter/widgets/style.css b/gamestonk_terminal/jupyter/widgets/style.css new file mode 100644 index 00000000000..7584138ab99 --- /dev/null +++ b/gamestonk_terminal/jupyter/widgets/style.css @@ -0,0 +1,54 @@ +.container { + position: relative; +} + +.container .card { + font-family: Consolas, sans-serif; + position: relative; + width: 128px; + height: 128px; + background: #000000; + border-radius: 5px; + border-color: rgb(142, 142, 142); + border-style: solid; + border-width: 0.5px; + overflow: hidden; +} + +.container .card .contentBx { + position: absolute; + top: 28; + bottom: 32; + width: 100%; + text-align: center; + z-index: 10; +} + +.container .card .contentBx h1 { + position: relative; + font-size: 24px; + font-weight: 600; + letter-spacing: 1px; + color: #fff; + margin-top: 28px; + margin-bottom: 10px; +} + +.container .card .contentBx h2 { + position: relative; + font-size: 24px; + font-weight: 600; + letter-spacing: 1px; +} + +.up_color { + color: #00ACFF; +} + +.down_color { + color: #e4003a; +} + +.neutral_color { + color: #ffed00; +} diff --git a/gamestonk_terminal/log/__init__.py b/gamestonk_terminal/log/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gamestonk_terminal/log/collection/__init__.py b/gamestonk_terminal/log/collection/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gamestonk_terminal/log/collection/log_sender.py b/gamestonk_terminal/log/collection/log_sender.py new file mode 100644 index 00000000000..cb2b2a8f7e7 --- /dev/null +++ b/gamestonk_terminal/log/collection/log_sender.py @@ -0,0 +1,100 @@ +# IMPORTATION STANDARD +from copy import deepcopy +from threading import Thread +from queue import SimpleQueue +from pathlib import Path + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL +from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.log.constants import ( + ARCHIVES_FOLDER_NAME, + S3_FOLDER_SUFFIX, + TMP_FOLDER_NAME, +) +from gamestonk_terminal.log.collection.s3_sender import send_to_s3 +from gamestonk_terminal.log.generation.settings import AppSettings + +# DO NOT USE THE FILE LOGGER IN THIS MODULE + + +class QueueItem: + @property + def path(self) -> Path: + return self.__path + + @property + def last(self) -> bool: + return self.__last + + def __init__(self, path: Path, last: bool = False): + self.__path = path + self.__last = last + + def __str__(self): + return "{" + f'"last": {self.last}, "path": {self.path}' + "}" + + +class LogSender(Thread): + @property + def queue(self) -> SimpleQueue: + return self.__queue + + @property + def app_settings(self) -> AppSettings: + return deepcopy(self.__app_settings) + + # OVERRIDE + def run(self): + queue = self.__queue + app_settings = self.__app_settings + + app_name = app_settings.name + identifier = app_settings.identifier + + while True: + item: QueueItem = queue.get() + file = item.path + last = item.last + + if gtff.LOG_COLLECTION: + archives_file = file.parent / ARCHIVES_FOLDER_NAME / f"{file.stem}.log" + object_key = ( + f"{app_name}{S3_FOLDER_SUFFIX}/logs/{identifier}/{file.stem}.log" + ) + tmp_file = file.parent / TMP_FOLDER_NAME / f"{file.stem}.log" + + try: + send_to_s3( + archives_file=archives_file, + file=file, + object_key=object_key, + tmp_file=tmp_file, + ) + except Exception: + pass + finally: + pass + + if last: + break + + # OVERRIDE + def __init__( + self, + app_settings: AppSettings, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.__app_settings = app_settings + + self.__queue: SimpleQueue = SimpleQueue() + + def send_path(self, path: Path, last: bool = False): + """Only closed files should be sent.""" + + queue = self.__queue + queue.put(QueueItem(path=path, last=last)) diff --git a/gamestonk_terminal/log/collection/logging_clock.py b/gamestonk_terminal/log/collection/logging_clock.py new file mode 100644 index 00000000000..d9219320024 --- /dev/null +++ b/gamestonk_terminal/log/collection/logging_clock.py @@ -0,0 +1,135 @@ +import logging +import time +from datetime import datetime, timedelta +from enum import Enum +from math import ceil +from threading import Thread +from typing import Callable, Optional + + +class Precision(Enum): + hour = 3600 + minute = 60 + + +class LoggingClock(Thread): + """ + Like a Talking Clock but for logs. + + Usage example : + import logging + from gamestonk_terminal.log_collection.logging_clock import LoggingClock, Precision + + logging.basicConfig() + logger = logging.getLogger(__name__) + logger.setLevel(level=logging.INFO) + + logging_clock = LoggingClock( + logger=logger, + precision=Precision.minute, + ) + logging_clock.start() + logging_clock.join() + """ + + @classmethod + def calculate_next_sharp( + cls, + current_time: datetime, + precision: Precision, + ) -> datetime: + if precision is Precision.hour: + sharp_time = cls.calculate_next_sharp_hour(current_time=current_time) + elif precision is Precision.minute: + sharp_time = cls.calculate_next_sharp_minute(current_time=current_time) + else: + raise AttributeError(f"Unknown precision {precision}") + + return sharp_time + + @staticmethod + def calculate_next_sharp_hour(current_time: datetime) -> datetime: + current_truncated_time = datetime( + current_time.year, + current_time.month, + current_time.day, + current_time.hour, + ) + next_sharp_hour = current_truncated_time + timedelta(minutes=1) + + return next_sharp_hour + + @staticmethod + def calculate_next_sharp_minute(current_time: datetime) -> datetime: + current_truncated_time = datetime( + current_time.year, + current_time.month, + current_time.day, + current_time.hour, + current_time.minute, + ) + next_sharp_minute = current_truncated_time + timedelta(minutes=1) + + return next_sharp_minute + + @classmethod + def do_action_every_sharp( + cls, + action: Callable, + precision: Precision = Precision.hour, + ): + next_hour = cls.calculate_next_sharp( + current_time=datetime.now(), + precision=precision, + ) + + while True: + current_time = datetime.now() + delta = current_time - next_hour + delta_seconds = delta.total_seconds() + + if delta_seconds > 0: + action() + next_hour = cls.calculate_next_sharp( + current_time=current_time, + precision=precision, + ) + else: + sleep_duration = ceil(abs(delta_seconds)) + time.sleep(sleep_duration) + + # OVERRIDE + def run(self): + action = self.__action + precision = self.__precision + + self.do_action_every_sharp( + action=action, + precision=precision, + ) + + # OVERRIDE + def __init__( + self, + *args, + action: Optional[Callable] = None, + level: int = logging.INFO, + logger: Optional[logging.Logger] = None, + msg: str = "Logging Clock : %s", + precision: Precision = Precision.hour, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.__action = action or self.default_action + self.__level = level + self.__logger = logger or logging.getLogger(self.__module__) + self.__msg = msg + self.__precision = precision + + def default_action(self): + level = self.__level + logger = self.__logger + msg = self.__msg % datetime.now() + + logger.log(level=level, msg=msg) diff --git a/gamestonk_terminal/log/collection/s3_sender.py b/gamestonk_terminal/log/collection/s3_sender.py new file mode 100644 index 00000000000..167d265e260 --- /dev/null +++ b/gamestonk_terminal/log/collection/s3_sender.py @@ -0,0 +1,100 @@ +# IMPORTATION STANDARD +from pathlib import Path +from typing import Any, Dict + +# IMPORTATION THIRDPARTY +import requests + +try: + import boto3 +except ImportError: + WITH_BOTO3 = True +finally: + WITH_BOTO3 = False + +# IMPORTATION INTERNAL +from gamestonk_terminal import config_terminal as cfg +from gamestonk_terminal.log.constants import DEFAULT_API_URL, DEFAULT_BUCKET + +# DO NOT USE THE FILE LOGGER IN THIS MODULE + + +def send_to_s3_directly( + aws_access_key: str, + aws_access_key_id: str, + bucket: str, + file: Path, + object_key: str, +): + if not WITH_BOTO3: + raise ModuleNotFoundError("Library `boto3` is required to directly access S3.") + + s3_client = boto3.client( + service_name="s3", + aws_access_key_id=aws_access_key, + aws_secret_access_key=aws_access_key_id, + ) + s3_client.upload_file(str(file), bucket, object_key) + + +def fetch_presigned_url(api_url: str, object_key: str) -> Dict[str, Any]: + raw_response = requests.put(url=api_url, json={"object_key": object_key}) + raw_response.raise_for_status() + + response = raw_response.json() + + return response + + +def send_to_s3_using_presigned_url( + api_url: str, + file: Path, + object_key: str, +): + presigned_info = fetch_presigned_url(api_url=api_url, object_key=object_key) + + with open(file, "rb") as f: + files = {"file": f} + + raw_response = requests.post( + presigned_info["url"], + data=presigned_info["fields"], + files=files, + ) + + raw_response.raise_for_status() + + if raw_response.status_code == 204: + pass + + +def send_to_s3(archives_file: Path, file: Path, object_key: str, tmp_file: Path): + api_url = DEFAULT_API_URL + aws_access_key = cfg.AWS_ACCESS_KEY + aws_access_key_id = cfg.AWS_ACCESS_KEY_ID + bucket = DEFAULT_BUCKET + + if file.stat().st_size <= 0: + file.unlink(missing_ok=True) + raise AttributeError(f"File is empty : {file}") + + tmp_file.parent.mkdir(exist_ok=True) + file = file.rename(tmp_file) + + if aws_access_key != "REPLACE_ME" and aws_access_key_id != "REPLACE_ME": + send_to_s3_directly( + aws_access_key=aws_access_key_id, + aws_access_key_id=aws_access_key_id, + bucket=bucket, + file=file, + object_key=object_key, + ) + else: + send_to_s3_using_presigned_url( + api_url=api_url, + file=file, + object_key=object_key, + ) + + archives_file.parent.mkdir(exist_ok=True) + file.rename(archives_file) diff --git a/gamestonk_terminal/log/constants.py b/gamestonk_terminal/log/constants.py new file mode 100644 index 00000000000..5a73ca28f29 --- /dev/null +++ b/gamestonk_terminal/log/constants.py @@ -0,0 +1,5 @@ +ARCHIVES_FOLDER_NAME = "archives" +DEFAULT_BUCKET = "gst-restrictions" +DEFAULT_API_URL = "https://knaqi3sud7.execute-api.eu-west-3.amazonaws.com/log_api/logs" +S3_FOLDER_SUFFIX = "-app" +TMP_FOLDER_NAME = "tmp" diff --git a/gamestonk_terminal/log/generation/__init__.py b/gamestonk_terminal/log/generation/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gamestonk_terminal/log/generation/directories.py b/gamestonk_terminal/log/generation/directories.py new file mode 100644 index 00000000000..d03b18672a2 --- /dev/null +++ b/gamestonk_terminal/log/generation/directories.py @@ -0,0 +1,44 @@ +# IMPORTATION STANDARD +import os +import uuid +from pathlib import Path + +# IMPORTATION THIRDPARTY + + +# IMPORTATION INTERNAL + + +def get_log_dir() -> Path: + """Retrieve application's log directory.""" + + file_path = Path(__file__) + log_dir = file_path.parent.parent.parent.parent.absolute().joinpath("logs") + + if not os.path.isdir(log_dir.absolute()): + os.mkdir(log_dir.absolute()) + + log_id = log_dir.absolute().joinpath(".logid") + + if not os.path.isfile(log_id.absolute()): + logging_id = f"{uuid.uuid4()}" + with open(log_id.absolute(), "a") as a_file: + a_file.write(f"{logging_id}\n") + else: + with open(log_id.absolute()) as a_file: + logging_id = a_file.readline().rstrip() + + uuid_log_dir = log_dir.absolute().joinpath(logging_id) + + if not os.path.isdir(uuid_log_dir.absolute()): + os.mkdir(uuid_log_dir.absolute()) + + return uuid_log_dir + + +def get_log_sub_dir(name: str): + log_dir = get_log_dir() + + sub_dir = log_dir / name + + return sub_dir diff --git a/gamestonk_terminal/log/generation/expired_files.py b/gamestonk_terminal/log/generation/expired_files.py new file mode 100644 index 00000000000..7da9870a055 --- /dev/null +++ b/gamestonk_terminal/log/generation/expired_files.py @@ -0,0 +1,29 @@ +# IMPORTATION STANDARD +from datetime import datetime +from pathlib import Path +from typing import List + + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL + + +def get_timestamp_from_x_days(x: int): + timestamp_from_x_days = datetime.now().timestamp() - x * 86400 + return timestamp_from_x_days + + +def get_expired_file_list(directory: Path, before_timestamp: float): + expired_files = list() + if directory.exists and directory.is_dir(): + for file in directory.iterdir(): + if file.lstat().st_mtime < before_timestamp: + expired_files.append(file) + + return expired_files + + +def remove_file_list(file_list: List[Path]): + for file in file_list: + file.unlink(missing_ok=True) diff --git a/gamestonk_terminal/log/generation/formatter_with_exceptions.py b/gamestonk_terminal/log/generation/formatter_with_exceptions.py new file mode 100644 index 00000000000..95547c0845a --- /dev/null +++ b/gamestonk_terminal/log/generation/formatter_with_exceptions.py @@ -0,0 +1,176 @@ +# IMPORTATION STANDARD +import logging +import os +import re + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL +from gamestonk_terminal.log.generation.settings import AppSettings + + +class FormatterWithExceptions(logging.Formatter): + """Logging Formatter that includes formatting of Exceptions""" + + DATEFORMAT = "%Y-%m-%dT%H:%M:%S%z" + LOGFORMAT = "%(asctime)s|%(name)s|%(funcName)s|%(lineno)s|%(message)s" + LOGPREFIXFORMAT = ( + "%(levelname)s|%(appName)s|%(commitHash)s|%(appId)s|%(sessionId)s|%(userId)s|" + ) + + @staticmethod + def calculate_level_name(record: logging.LogRecord): + if record.exc_text: + level_name = "X" + elif record.levelname: + level_name = record.levelname[0] + else: + level_name = "U" + + return level_name + + @staticmethod + def extract_log_extra(record: logging.LogRecord): + log_extra = dict() + + if hasattr(record, "func_name_override"): + record.funcName = record.func_name_override # type: ignore + record.lineno = 0 + + log_extra["userId"] = record.user_id if hasattr(record, "user_id") else "NA" # type: ignore + + if hasattr(record, "session_id"): + log_extra["sessionId"] = record.session_id # type: ignore + + return log_extra + + @staticmethod + def filter_piis(text: str) -> str: + ip_regex = r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}" + s_list = [] + ip_reg = re.compile(ip_regex) + for word in text.split(): + + if ( + ip_reg.search(word) + # and int(max(word.split("."))) < 256 + # and int(min(word.split("."))) >= 0 + ): + s_list.append("suspected_ip") + elif "@" in word and "." in word: + s_list.append("suspected_email") + elif os.sep in word and f"GamestonkTerminal{os.sep}" in word: + s_list.append( + word.split(f"GamestonkTerminal{os.sep}")[1] + .replace('"', "") + .replace("'", "") + ) + elif os.sep in word and os.sep != word: + s_list.append( + (f"cut{os.sep}file{os.sep}path{os.sep}" + word.split(os.sep)[-1]) + .replace('"', "") + .replace("'", "") + ) + else: + s_list.append(word) + + text = " ".join(s_list) + + return text + + @staticmethod + def filter_special_characters(text: str): + filtered_text = ( + text.replace("\\n", " - ") + .replace("\n", " - ") + .replace("\t", " ") + .replace("\r", "") + .replace("'Traceback", "Traceback") + ) + return filtered_text + + @staticmethod + def detect_terminal_message(text: str): + if "The command " in text and "doesn't exist on the" in text and os.sep == "/": + return True + return False + + @classmethod + def filter_log_line(cls, text: str): + text = cls.filter_special_characters(text=text) + contains_terminal_menu = cls.detect_terminal_message(text) + if contains_terminal_menu: + first_message, second_message = text.split("menu. - Traceback") + text = ( + first_message + + "menu. - Traceback" + + cls.filter_piis(text=second_message) + ) + elif "CMD: {" not in text and "QUEUE: {" not in text: + cls.filter_piis(text=text) + + return text + + # OVERRIDE + def __init__( + self, + app_settings: AppSettings, + style="%", + validate=True, + ) -> None: + super().__init__( + fmt=self.LOGFORMAT, + datefmt=self.DATEFORMAT, + style=style, + validate=validate, + ) + self.__log_settings = app_settings + + # OVERRIDE + def formatException(self, ei) -> str: + """Exception formatting handler + Parameters + ---------- + ei : logging._SysExcInfoType + Exception to be logged + Returns + ------- + str + Formatted exception + """ + + result = super().formatException(ei) + return repr(result) + + # OVERRIDE + def format(self, record: logging.LogRecord) -> str: + """Log formatter + Parameters + ---------- + record : logging.LogRecord + Logging record + Returns + ------- + str + Formatted_log message + """ + + app_settings = self.__log_settings + + level_name = self.calculate_level_name(record=record) + log_prefix_content = { + "appName": app_settings.name, + "levelname": level_name, + "appId": app_settings.identifier, + "sessionId": app_settings.session_id, + "commitHash": app_settings.commit_hash, + } + log_extra = self.extract_log_extra(record=record) + log_prefix_content = {**log_prefix_content, **log_extra} + log_prefix = self.LOGPREFIXFORMAT % log_prefix_content + + log_line = super().format(record) + log_line = self.filter_log_line(text=log_line) + log_line_full = log_prefix + log_line + + return log_line_full diff --git a/gamestonk_terminal/log/generation/path_tracking_file_handler.py b/gamestonk_terminal/log/generation/path_tracking_file_handler.py new file mode 100644 index 00000000000..a3014102290 --- /dev/null +++ b/gamestonk_terminal/log/generation/path_tracking_file_handler.py @@ -0,0 +1,180 @@ +# IMPORTATION STANDARD +from copy import deepcopy +from logging.handlers import TimedRotatingFileHandler +from pathlib import Path +from typing import Callable + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL +from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.log.constants import ARCHIVES_FOLDER_NAME, TMP_FOLDER_NAME +from gamestonk_terminal.log.collection.log_sender import LogSender +from gamestonk_terminal.log.collection.logging_clock import LoggingClock, Precision +from gamestonk_terminal.log.generation.directories import get_log_dir, get_log_sub_dir +from gamestonk_terminal.log.generation.expired_files import ( + get_expired_file_list, + get_timestamp_from_x_days, + remove_file_list, +) + +from gamestonk_terminal.log.generation.settings import Settings + + +class PathTrackingFileHandler(TimedRotatingFileHandler): + @staticmethod + def build_log_file_path(settings: Settings) -> Path: + app_settings = settings.app_settings + log_settings = settings.log_settings + app_name = app_settings.name + directory = log_settings.directory + session_id = app_settings.session_id + + path = directory.absolute().joinpath(f"{app_name}_{session_id}") + return path + + @staticmethod + def build_log_sender(settings: Settings, start: bool) -> LogSender: + log_sender = LogSender(app_settings=settings.app_settings, daemon=True) + + if start: + log_sender.start() + + return log_sender + + @staticmethod + def clean_expired_files(before_timestamp: float): + """Deleting old files inside : archives and tmp folders. + + Only files inside the following folders are considered : + - {LOG_FOLDER_PATH}/{ARCHIVES_FOLDER_NAME} + - {LOG_FOLDER_PATH}/{TMP_FOLDER_NAME} + + Args: + before_timestamp (float): Timestamp before which files are considered expired. + """ + + archives_directory = get_log_sub_dir(name=ARCHIVES_FOLDER_NAME) + tmp_directory = get_log_sub_dir(name=TMP_FOLDER_NAME) + expired_archives_file_list = get_expired_file_list( + directory=archives_directory, + before_timestamp=before_timestamp, + ) + expired_tmp_file_list = get_expired_file_list( + directory=tmp_directory, + before_timestamp=before_timestamp, + ) + + remove_file_list(file_list=expired_archives_file_list) + remove_file_list(file_list=expired_tmp_file_list) + + @staticmethod + def build_rolling_clock( + action: Callable, + frequency: str, + start: bool, + ) -> LoggingClock: + frequency = frequency.upper() + + if frequency == "H": + precision = Precision.hour + elif frequency == "M": + precision = Precision.minute + else: + raise AttributeError("Unsupported `logging_clock.Precision`.") + + rolling_clock = LoggingClock( + action=action, + daemon=True, + precision=precision, + ) + + if start: + rolling_clock.start() + + return rolling_clock + + def send_expired_files(self, before_timestamp: float): + """Try to send files older than before_timestamp days. + + Only files inside the following folder are considered : + - {LOG_FOLDER_PATH}/ + + Args: + before_timestamp (float): Timestamp before which files are considered expired. + """ + + log_sender = self.__log_sender + log_directory = get_log_dir() + + expired_log_file_list = get_expired_file_list( + directory=log_directory, + before_timestamp=before_timestamp, + ) + + for file in expired_log_file_list: + log_sender.send_path(last=False, path=file) + + @property + def log_sender(self) -> LogSender: + return self.__log_sender + + @property + def rolling_clock(self) -> LoggingClock: + return self.__rolling_clock + + @property + def settings(self) -> Settings: + return deepcopy(self.__settings) + + # OVERRIDE + def __init__( + self, + settings: Settings, + *args, + rolling_clock: bool = False, + **kwargs, + ) -> None: + filename = str(self.build_log_file_path(settings=settings)) + frequency = settings.log_settings.frequency + kwargs["when"] = frequency + super().__init__(filename, *args, **kwargs) + + self.suffix += ".log" + self.__settings = settings + + self.clean_expired_files(before_timestamp=get_timestamp_from_x_days(x=5)) + + if gtff.LOG_COLLECTION: + self.__log_sender = self.build_log_sender(settings=settings, start=True) + self.__rolling_clock = self.build_rolling_clock( + action=self.doRollover, + frequency=frequency, + start=rolling_clock, + ) + self.send_expired_files(before_timestamp=get_timestamp_from_x_days(x=3)) + + # OVERRIDE + def doRollover(self) -> None: + super().doRollover() + + if gtff.LOG_COLLECTION: + log_sender = self.__log_sender + to_delete_path_list = self.getFilesToDelete() + for path in to_delete_path_list: + log_sender.send_path(path=Path(path)) + + # OVERRIDE + def close(self): + """Do not use the file logger in this function.""" + + super().close() + + if gtff.LOG_COLLECTION: + log_sender = self.__log_sender + closed_log_path = self.baseFilename + log_sender.send_path(path=Path(closed_log_path), last=True) + try: + log_sender.join() + except Exception: + pass diff --git a/gamestonk_terminal/log/generation/settings.py b/gamestonk_terminal/log/generation/settings.py new file mode 100644 index 00000000000..0185202afc5 --- /dev/null +++ b/gamestonk_terminal/log/generation/settings.py @@ -0,0 +1,111 @@ +# IMPORTATION STANDARD +from copy import deepcopy +from pathlib import Path + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL + + +class AppSettings: + @property + def name(self) -> str: + return self.__name + + @property + def commit_hash(self) -> str: + return self.__commit_hash + + @property + def identifier(self) -> str: + return self.__identifier + + @property + def session_id(self) -> str: + return self.__session_id + + def __init__( + self, + name: str, + commit_hash: str, + session_id: str, + identifier: str, + ): + """ + Args: + name (str): Name of the application. + commit_hash (str): Commit hash of the current running code. + identifier (str): Unique key identifying a particular installation. + session_id (str): Key identifying a particular running session. + """ + + self.__name = name + self.__commit_hash = commit_hash + self.__identifier = identifier + self.__session_id = session_id + + +class LogSettings: + @property + def directory(self) -> Path: + return self.__directory + + @property + def frequency(self) -> str: + return self.__frequency + + @property + def handler_list(self) -> str: + return self.__handler_list + + @property + def verbosity(self) -> int: + return self.__verbosity + + def __init__( + self, + directory: Path, + frequency: str, + handler_list: str, + verbosity: int, + ): + """ + Args: + directory (Path): Directory used to store log files. + frequency (str): Frequency of the log files rotation. + handler_list (str) : Comma separated list of handlers : stdout,stderr,noop,file. + verbosity (str): Verbosity level as defined in Python `logging` module. + """ + + self.__directory = directory + self.__frequency = frequency + self.__handler_list = handler_list + self.__verbosity = verbosity + + +class Settings: + @property + def app_settings(self) -> AppSettings: + return deepcopy(self.__app_settings) + + @property + def log_settings(self) -> LogSettings: + return deepcopy(self.__log_settings) + + def __init__( + self, + app_settings: AppSettings, + log_settings: LogSettings, + ): + """This model regroups all configurations used by these classes instance : + - gamestonk_terminal.log.collection.log_sender.LogSender + - gamestonk_terminal.log.generation.formatter_with_exceptions.FormatterWithExceptions + - gamestonk_terminal.log.generation.path_tracking_file_handler.PathTrackingFileHandler + + Args: + app_name (str): Instance of AppSettings. + log_settings (str): Instance of LogSettings. + """ + + self.__app_settings = app_settings + self.__log_settings = log_settings diff --git a/gamestonk_terminal/loggers.py b/gamestonk_terminal/loggers.py index f43347fb22b..c05503e0130 100644 --- a/gamestonk_terminal/loggers.py +++ b/gamestonk_terminal/loggers.py @@ -1,197 +1,49 @@ """Logging Configuration""" __docformat__ = "numpy" + +# IMPORTATION STANDARD import logging -from logging.handlers import TimedRotatingFileHandler import os -import re -from pathlib import Path import sys import time -import uuid -from math import floor, ceil -import threading -import requests +from pathlib import Path +from typing import Optional +# IMPORTATION THIRDPARTY import git -import boto3 -from botocore.exceptions import ClientError +# IMPORTATION INTERNAL import gamestonk_terminal.config_terminal as cfg -from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.log.generation.settings import ( + AppSettings, + LogSettings, + Settings, +) +from gamestonk_terminal.log.generation.path_tracking_file_handler import ( + PathTrackingFileHandler, +) +from gamestonk_terminal.log.generation.formatter_with_exceptions import ( + FormatterWithExceptions, +) +from gamestonk_terminal.log.generation.directories import get_log_dir + +logging.getLogger("requests").setLevel(cfg.LOGGING_VERBOSITY) +logging.getLogger("urllib3").setLevel(cfg.LOGGING_VERBOSITY) logger = logging.getLogger(__name__) -LOGFORMAT = "%(asctime)s|%(name)s|%(funcName)s|%(lineno)s|%(message)s" -LOGPREFIXFORMAT = "%(levelname)s|%(appName)s|%(version)s|%(loggingId)s|%(sessionId)s|" -DATEFORMAT = "%Y-%m-%dT%H:%M:%S%z" -BUCKET = "gst-restrictions" -FOLDER_NAME = "gst-app/logs" -URL = "https://knaqi3sud7.execute-api.eu-west-3.amazonaws.com/log_api/logs" +START_TIMESTAMP = int(time.time()) -def library_loggers(verbosity: int = 0) -> None: - """Setup library logging - Parameters - ---------- - verbosity : int, optional - Log level verbosity, by default 0 - """ +def get_app_id() -> str: + """UUID of the current installation.""" - logging.getLogger("requests").setLevel(verbosity) - logging.getLogger("urllib3").setLevel(verbosity) + app_id = get_log_dir().stem + + return app_id -def get_log_dir() -> Path: - file_path = Path(__file__) - logger.debug("Parent dir: %s", file_path.parent.parent.absolute()) - log_dir = file_path.parent.parent.absolute().joinpath("logs") - logger.debug("Future logdir: %s", log_dir) - - if not os.path.isdir(log_dir.absolute()): - logger.debug("Logdir does not exist. Creating.") - os.mkdir(log_dir.absolute()) - - log_id = log_dir.absolute().joinpath(".logid") - - if not os.path.isfile(log_id.absolute()): - logger.debug("Log ID does not exist: %s", log_id.absolute()) - cfg.LOGGING_ID = f"{uuid.uuid4()}" - with open(log_id.absolute(), "a") as a_file: - a_file.write(f"{cfg.LOGGING_ID}\n") - else: - logger.debug("Log ID exists: %s", log_id.absolute()) - with open(log_id.absolute()) as a_file: - cfg.LOGGING_ID = a_file.readline().rstrip() - - logger.debug("Log id: %s", cfg.LOGGING_ID) - - uuid_log_dir = log_dir.absolute().joinpath(cfg.LOGGING_ID) - - logger.debug("Current_log dir: %s", uuid_log_dir) - - if not os.path.isdir(uuid_log_dir.absolute()): - logger.debug( - "UUID_log dir does not exist: %s. Creating.", uuid_log_dir.absolute() - ) - os.mkdir(uuid_log_dir.absolute()) - return uuid_log_dir - - -def setup_file_logger(app_name: str, session_id: str) -> None: - """Setup File Logger""" - - uuid_log_dir = get_log_dir() - - upload_archive_logs_s3(directory_str=uuid_log_dir, log_filter=r"gst_") - - start_time = int(time.time()) - cfg.LOGGING_FILE = uuid_log_dir.absolute().joinpath(f"gst_{start_time}") # type: ignore - - logger.debug("Current_log file: %s", cfg.LOGGING_FILE) - - handler = TimedRotatingFileHandlerWithUpload(cfg.LOGGING_FILE) - handler.suffix += ".log" - formatter = CustomFormatterWithExceptions( - app_name, uuid_log_dir.stem, session_id, fmt=LOGFORMAT, datefmt=DATEFORMAT - ) - handler.setFormatter(formatter) - logging.getLogger().addHandler(handler) - - -class TimedRotatingFileHandlerWithUpload(TimedRotatingFileHandler): - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.upload_thread_lock = threading.RLock() - - def doRollover(self) -> None: - super().doRollover() - t = threading.Thread(target=self.upload_logs_as_thread, args=()) - t.start() - - def upload_logs_as_thread(self) -> None: - with self.upload_thread_lock: - upload_archive_logs_s3((os.sep).join(self.baseFilename.split(os.sep)[:-1])) - - -class CustomFormatterWithExceptions(logging.Formatter): - """Custom Logging Formatter that includes formatting of Exceptions""" - - def __init__( - self, - app_name: str, - logging_id: str, - session_id: str, - fmt=None, - datefmt=None, - style="%", - validate=True, - ) -> None: - super().__init__(fmt=fmt, datefmt=datefmt, style=style, validate=validate) - self.logPrefixDict = { - "loggingId": logging_id, - "sessionId": session_id, - "version": cfg.LOGGING_VERSION, - "appName": app_name, - } - - def formatException(self, ei) -> str: - """Exception formatting handler - Parameters - ---------- - ei : logging._SysExcInfoType - Exception to be logged - Returns - ------- - str - Formatted exception - """ - result = super().formatException(ei) - return repr(result) - - def format(self, record: logging.LogRecord) -> str: - """Log formatter - Parameters - ---------- - record : logging.LogRecord - Logging record - Returns - ------- - str - Formatted_log message - """ - if hasattr(record, "func_name_override"): - record.funcName = record.func_name_override # type: ignore - record.lineno = 0 - - if hasattr(record, "user_id"): - self.logPrefixDict["loggingId"] = record.user_id # type: ignore - - if hasattr(record, "session_id"): - self.logPrefixDict["sessionId"] = record.session_id # type: ignore - - s = super().format(record) - if record.levelname: - self.logPrefixDict["levelname"] = record.levelname[0] - else: - self.logPrefixDict["levelname"] = "U" - - if record.exc_text: - self.logPrefixDict["levelname"] = "X" - logPrefix = LOGPREFIXFORMAT % self.logPrefixDict - s = ( - s.replace("\n", " - ") - .replace("\t", " ") - .replace("\r", "") - .replace("'", "`") - .replace('"', "`") - ) - - else: - logPrefix = LOGPREFIXFORMAT % self.logPrefixDict - return f"{logPrefix}{s}" - - -def get_commit_hash() -> None: +def get_commit_hash() -> str: """Get Commit Short Hash""" file_path = Path(__file__) @@ -201,225 +53,110 @@ def get_commit_hash() -> None: repo = git.Repo(search_parent_directories=True) sha = repo.head.object.hexsha short_sha = repo.git.rev_parse(sha, short=8) - cfg.LOGGING_VERSION = f"sha:{short_sha}" + commit_hash = f"sha:{short_sha}" + else: + commit_hash = "unknown-commit" + + return commit_hash -def setup_logging(app_name: str) -> None: - """Setup Logging""" +def add_stdout_handler(settings: Settings): + app_settings = settings.app_settings + handler = logging.StreamHandler(sys.stdout) + formatter = FormatterWithExceptions(app_settings=app_settings) + handler.setFormatter(formatter) + logging.getLogger().addHandler(handler) - START_TIME = int(time.time()) - LOGGING_ID = cfg.LOGGING_ID if cfg.LOGGING_ID else "" - verbosity_terminal = floor(cfg.LOGGING_VERBOSITY / 10) * 10 - verbosity_libraries = ceil(cfg.LOGGING_VERBOSITY / 10) * 10 +def add_stderr_handler(settings: Settings): + app_settings = settings.app_settings + handler = logging.StreamHandler(sys.stderr) + formatter = FormatterWithExceptions(app_settings=app_settings) + handler.setFormatter(formatter) + logging.getLogger().addHandler(handler) + + +def add_noop_handler(settings: Settings): + app_settings = settings.app_settings + handler = logging.NullHandler() + formatter = FormatterWithExceptions(app_settings=app_settings) + handler.setFormatter(formatter) + logging.getLogger().addHandler(handler) + + +def add_file_handler(settings: Settings): + app_settings = settings.app_settings + handler = PathTrackingFileHandler(settings=settings, rolling_clock=False) + formatter = FormatterWithExceptions(app_settings=app_settings) + handler.setFormatter(formatter) + logging.getLogger().addHandler(handler) + + +def setup_handlers(settings: Settings): + log_settings = settings.log_settings + handler_list = log_settings.handler_list + verbosity = log_settings.verbosity + logging.basicConfig( - level=verbosity_terminal, format=LOGFORMAT, datefmt=DATEFORMAT, handlers=[] + level=verbosity, + format=FormatterWithExceptions.LOGFORMAT, + datefmt=FormatterWithExceptions.DATEFORMAT, + handlers=[], ) - get_commit_hash() - - for a_handler in cfg.LOGGING_HANDLERS.split(","): - if a_handler == "stdout": - handler = logging.StreamHandler(sys.stdout) - formatter = CustomFormatterWithExceptions( - app_name, LOGGING_ID, str(START_TIME), fmt=LOGFORMAT, datefmt=DATEFORMAT - ) - handler.setFormatter(formatter) - logging.getLogger().addHandler(handler) - elif a_handler == "stderr": - handler = logging.StreamHandler(sys.stderr) - formatter = CustomFormatterWithExceptions( - app_name, LOGGING_ID, str(START_TIME), fmt=LOGFORMAT, datefmt=DATEFORMAT - ) - handler.setFormatter(formatter) - logging.getLogger().addHandler(handler) - elif a_handler == "noop": - handler = logging.NullHandler() # type: ignore - formatter = CustomFormatterWithExceptions( - app_name, LOGGING_ID, str(START_TIME), fmt=LOGFORMAT, datefmt=DATEFORMAT - ) - handler.setFormatter(formatter) - logging.getLogger().addHandler(handler) - elif a_handler == "file": - setup_file_logger(app_name, str(START_TIME)) + for handler_type in handler_list.split(","): + if handler_type == "stdout": + add_stdout_handler(settings=settings) + elif handler_type == "stderr": + add_stderr_handler(settings=settings) + elif handler_type == "noop": + add_noop_handler(settings=settings) + elif handler_type == "file": + add_file_handler(settings=settings) else: - logger.debug("Unknown loghandler") - - library_loggers(verbosity_libraries) + logger.debug("Unknown log handler.") logger.info("Logging configuration finished") - logger.info("Logging set to %s", cfg.LOGGING_HANDLERS) - logger.info("Verbosity set to %s", verbosity_libraries) + logger.info("Logging set to %s", handler_list) + logger.info("Verbosity set to %s", verbosity) logger.info( - "FORMAT: %s%s", LOGPREFIXFORMAT.replace("|", "-"), LOGFORMAT.replace("|", "-") + "LOGFORMAT: %s%s", + FormatterWithExceptions.LOGPREFIXFORMAT.replace("|", "-"), + FormatterWithExceptions.LOGFORMAT.replace("|", "-"), ) -def upload_file_to_s3( - file: Path, - bucket: str = None, - object_name: str = None, - folder_name: str = None, - only_send_file_size=gtff.ONLY_SEND_FILE_SIZE, -) -> bool: - success = False - - # If S3 object_name was not specified, use file_name - if object_name is None: - object_name = file.name - - # If folder_name was specified, upload in the folder - if folder_name is not None: - object_name = f"{folder_name}/{object_name}" - - # Upload the file - if ( - bucket is not None - and cfg.AWS_ACCESS_KEY != "REPLACE_ME" - and cfg.AWS_ACCESS_KEY_ID != "REPLACE_ME" - ): - try: - s3_client = boto3.client( - service_name="s3", - aws_access_key_id=cfg.AWS_ACCESS_KEY_ID, - aws_secret_access_key=cfg.AWS_ACCESS_KEY, - ) - s3_client.upload_file(str(file), bucket, object_name) - except ClientError as e: - logger.exception(str(e)) - success = True - else: - files = None - if only_send_file_size: - files = {"file": bytes(log_past_logsize(str(file)), "utf-8")} - else: - try: - with open(file, "rb") as f: - files = {"file": f.read()} - except Exception as e: - logger.exception("Could not open file: %s", str(e)) - - json = requests.put(url=URL, json={"object_key": object_name}).json() - r = requests.post(json["url"], data=json["fields"], files=files) - - if r.status_code in [403, 401, 400]: - logger.error("%s could not be uploaded", file.name) - elif r.status_code == 204: - logger.info("Log uploaded") - success = True - else: - logger.error( - "Unexpected status_code: %s when uploading: %s", - str(r.status_code), - file.name, - ) - return success - - -def contains_goodbye(file: Path) -> bool: - with open(file, "rb") as f: - try: - f.seek(-2, os.SEEK_END) - while f.read(1) != b"\n": - f.seek(-2, os.SEEK_CUR) - except OSError: - f.seek(0) - logger.exception("Could not find last line") - return False - last_line = f.readline().decode() - return "print_goodbye" in last_line - - -def upload_archive_logs_s3( - directory_str=None, - log_filter=r"gst_\d{10}\.20[2-3][0-9]-[0-2][0-9]-[0-3][0-9]_[0-2][0-9]\.log", - bucket=None, - folder_name=FOLDER_NAME, +def setup_logging( + app_name: Optional[str] = None, + frequency: Optional[str] = None, + session_id: Optional[str] = None, + verbosity: Optional[int] = None, ) -> None: - if gtff.LOG_COLLECTION: - if directory_str is None: - directory = get_log_dir() - else: - directory = Path(directory_str) - archive = directory / "archive" - tmp = directory / "tmp" + """Setup Logging""" - if not archive.exists(): - # Create a new directory because it does not exist - archive.mkdir() - logger.debug("The new archive directory is created!") - if not tmp.exists(): - # Create a new directory because it does not exist - tmp.mkdir() - logger.debug("The new uploading directory is created!") + name = app_name or cfg.LOGGING_APP_NAME + commit_hash = get_commit_hash() + identifier = get_app_id() + session_id = session_id or str(START_TIMESTAMP) - log_files = {} + frequency = frequency or cfg.LOGGING_FREQUENCY + verbosity = verbosity or cfg.LOGGING_VERBOSITY + directory = get_log_dir() + handler_list = cfg.LOGGING_HANDLERS - for file in directory.iterdir(): - if not file.is_file(): - continue - unused_file = False - suffix = "" - unused_file = ( - file.name[4:14].isdigit() - and (int(time.time()) - int(file.name[4:14])) / 86400 - > 1 # 86400 seconds in one day - ) - if file.suffix == ".log": - unused_file = True - else: - suffix += ".log" + settings = Settings( + app_settings=AppSettings( + name=name, + commit_hash=commit_hash, + identifier=identifier, + session_id=session_id, + ), + log_settings=LogSettings( + directory=directory, + frequency=frequency, + handler_list=handler_list, + verbosity=verbosity, + ), + ) - if not unused_file and contains_goodbye(file): - for handler in logging.getLogger().handlers: - handler.close() - logging.getLogger().removeHandler(handler) - new_handler = logging.FileHandler( - archive / f"uploader_for_{file.name}.log" - ) - logging.getLogger().addHandler(new_handler) - unused_file = True - - regexp = re.compile(log_filter) - if regexp.search(str(file)) and unused_file: - suffix = ".log" if file.suffix != ".log" else "" - try: - file.rename(tmp / (file.name + suffix)) - except Exception as e: - logger.exception("Cannot rename file: %s", str(e)) - - log_files[str(tmp / (file.name + suffix))] = tmp / ( - file.name + suffix - ), (archive / (file.name + suffix)) - - for log_file, archived_file in log_files.values(): - logger.info("Uploading logs") - success = upload_file_to_s3( - file=log_file, - bucket=bucket, - folder_name=f"{folder_name}/{cfg.LOGGING_ID}", - ) - if success: - try: - log_file.rename(archived_file) - except Exception as e: - logger.exception("Cannot archive file: %s", str(e)) - else: - logger.info("Logs not allowed to be collected") - - -def log_past_logsize(file: str) -> str: - with open(file, "rb") as f: - line_count = sum(1 for line in f) - try: - f.seek(-2, os.SEEK_END) - while f.read(1) != b"\n": - f.seek(-2, os.SEEK_CUR) - except OSError: - f.seek(0) - logger.exception("Could not find last line") - return "" - last_line = f.readline().decode() - send_up = "|".join(last_line.split("|")[:-1]) - send_up += f"|Size: {os.path.getsize(file)}|Lines: {line_count}" - return send_up + setup_handlers(settings=settings) diff --git a/gamestonk_terminal/parent_classes.py b/gamestonk_terminal/parent_classes.py index 75927637b11..ffb1c5b451f 100644 --- a/gamestonk_terminal/parent_classes.py +++ b/gamestonk_terminal/parent_classes.py @@ -134,7 +134,12 @@ class BaseController(metaclass=ABCMeta): def log_queue(self, message: str) -> None: if self.queue: - logger.info("%s: %s", message, "/".join(self.queue)) + logger.info( + "%s: {'path': '%s', 'queue': '%s'}", + message, + self.PATH, + "/".join(self.queue), + ) @log_start_end(log=logger) def switch(self, an_input: str) -> List[str]: @@ -178,7 +183,13 @@ class BaseController(metaclass=ABCMeta): known_args.cmd = "reset" set_command_location(f"{self.PATH}{known_args.cmd}") - logger.info("CMD: %s", an_input) + logger.info( + "CMD: {'path': '%s', 'known_cmd': '%s', 'other_args': '%s', 'input': '%s'}", + self.PATH, + known_args.cmd, + ";".join(other_args), + an_input, + ) self.log_queue("QUEUE") # This is what mutes portfolio issue @@ -422,11 +433,6 @@ class StockBaseController(BaseController, metaclass=ABCMeta): "--file", default=None, help="Path to load custom file.", - choices=[ - x - for x in os.listdir(os.path.join("custom_imports", "stocks")) - if x.endswith(".csv") - ], dest="filepath", type=str, ) @@ -478,6 +484,21 @@ class StockBaseController(BaseController, metaclass=ABCMeta): monthly=ns_parser.monthly, ) else: + # This seems to block the .exe since the folder needs to be manually created + # This block basically makes sure that we only look for the file if the -f flag is used + # If we add files in the argparse choices, it will fail for the .exe even if no -f is used + try: + if ns_parser.filepath not in os.listdir( + os.path.join("custom_imports", "stocks") + ): + console.print( + f"[red]{ns_parser.filepath} not found in custom_imports/stocks/ folder[/red].\n" + ) + return + except Exception as e: + console.print(e) + return + df_stock_candidate = stocks_helper.load_custom( os.path.join( os.path.join("custom_imports", "stocks"), ns_parser.filepath diff --git a/gamestonk_terminal/portfolio/brokers/degiro/degiro_controller.py b/gamestonk_terminal/portfolio/brokers/degiro/degiro_controller.py index 6e7e82507e1..67772524539 100644 --- a/gamestonk_terminal/portfolio/brokers/degiro/degiro_controller.py +++ b/gamestonk_terminal/portfolio/brokers/degiro/degiro_controller.py @@ -1,10 +1,12 @@ +# IMPORTATION STANDARD import argparse import logging from typing import List +# IMPORTATION THIRDPARTY from prompt_toolkit.completion import NestedCompleter -import gamestonk_terminal.config_terminal as config +# IMPORTATION INTERNAL from gamestonk_terminal import feature_flags as gtff from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import parse_known_args_and_warn @@ -205,37 +207,10 @@ class DegiroController(BaseController): add_help=False, prog="login", ) - parser.add_argument( - "-u", - "--username", - type=str, - default=config.DG_USERNAME, - help="Username in Degiro's account.", - ) - parser.add_argument( - "-p", - "--password", - type=str, - default=config.DG_PASSWORD, - help="Password in Degiro's account.", - ) - parser.add_argument( - "-o", - "--otp", - type=int, - default=None, - help="One time password (2FA).", - ) - parser.add_argument( - "-s", - "--topt-secret", - type=str, - default=None, - help="TOTP SECRET (2FA).", - ) ns_parser = parse_known_args_and_warn(parser, other_args) - self.__degiro_view.login(ns_parser=ns_parser) + if ns_parser: + self.__degiro_view.login() @log_start_end(log=logger) def call_logout(self, other_args: List[str]): @@ -248,7 +223,8 @@ class DegiroController(BaseController): ) ns_parser = parse_known_args_and_warn(parser, other_args) - self.__degiro_view.logout(ns_parser=ns_parser) + if ns_parser: + self.__degiro_view.logout() @log_start_end(log=logger) def call_lookup(self, other_args: List[str]): diff --git a/gamestonk_terminal/portfolio/brokers/degiro/degiro_model.py b/gamestonk_terminal/portfolio/brokers/degiro/degiro_model.py index 1ab23c30a0a..eb0cdbed0da 100644 --- a/gamestonk_terminal/portfolio/brokers/degiro/degiro_model.py +++ b/gamestonk_terminal/portfolio/brokers/degiro/degiro_model.py @@ -265,8 +265,7 @@ class DegiroModel: return news @log_start_end(log=logger) - def login(self, credentials: Credentials): - self.__trading_api.credentials.CopyFrom(credentials) + def login(self): self.__trading_api.connect() self.__setup_extra_credentials() diff --git a/gamestonk_terminal/portfolio/brokers/degiro/degiro_view.py b/gamestonk_terminal/portfolio/brokers/degiro/degiro_view.py index 43946f511f9..831453497a9 100644 --- a/gamestonk_terminal/portfolio/brokers/degiro/degiro_view.py +++ b/gamestonk_terminal/portfolio/brokers/degiro/degiro_view.py @@ -2,6 +2,7 @@ import logging from argparse import Namespace +# IMPORTATION THIRDPARTY import pandas as pd from degiro_connector.core.helpers import pb_handler from degiro_connector.trading.models.trading_pb2 import ( @@ -306,22 +307,14 @@ class DegiroView: @log_start_end(log=logger) @check_api_key(["DG_USERNAME", "DG_PASSWORD"]) - def login(self, ns_parser: Namespace): + def login(self): # GET ATTRIBUTES degiro_model = self.__degiro_model default_credentials = degiro_model.login_default_credentials() credentials = Credentials() credentials.CopyFrom(default_credentials) - credentials.username = ns_parser.username - credentials.password = ns_parser.password - - if ns_parser.otp is not None: - credentials.one_time_password = ns_parser.otp - if ns_parser.topt_secret is not None: - credentials.totp_secret_key = ns_parser.topt_secret - - degiro_model.login(credentials=credentials) + degiro_model.login() DegiroView.__login_display_success() @@ -331,9 +324,7 @@ class DegiroView: console.print("You are now logged in !") @log_start_end(log=logger) - def logout(self, ns_parser: Namespace): - _ = ns_parser - + def logout(self): # GET ATTRIBUTES degiro_model = self.__degiro_model diff --git a/gamestonk_terminal/stocks/comparison_analysis/ca_controller.py b/gamestonk_terminal/stocks/comparison_analysis/ca_controller.py index aec8ebe8326..b49bea69a2f 100644 --- a/gamestonk_terminal/stocks/comparison_analysis/ca_controller.py +++ b/gamestonk_terminal/stocks/comparison_analysis/ca_controller.py @@ -114,7 +114,6 @@ class ComparisonAnalysisController(BaseController): getfinnhub get similar stocks from finnhub API getfinviz get similar stocks from finviz API{has_ticker_end} - set reset and set similar companies add add more similar companies rmv remove similar companies individually or all[/cmds] @@ -185,7 +184,7 @@ class ComparisonAnalysisController(BaseController): ) else: self.ticker = ns_parser.ticker.upper() - console.print("") + console.print() @log_start_end(log=logger) def call_tsne(self, other_args: List[str]): @@ -474,7 +473,7 @@ class ComparisonAnalysisController(BaseController): else: self.similar = [] - console.print("") + console.print() self.user = "Custom" @log_start_end(log=logger) @@ -525,11 +524,11 @@ class ComparisonAnalysisController(BaseController): ) parser.add_argument( "-n", - "--no-scale", + "--normalize", action="store_true", - dest="no_scale", + dest="normalize", default=False, - help="Flag to not put all prices on same 0-1 scale", + help="Flag to normalize all prices on same 0-1 scale", ) parser.add_argument( "-s", @@ -550,7 +549,7 @@ class ComparisonAnalysisController(BaseController): similar_tickers=self.similar, start=ns_parser.start.strftime("%Y-%m-%d"), candle_type=ns_parser.type_candle, - normalize=not ns_parser.no_scale, + normalize=ns_parser.normalize, export=ns_parser.export, ) diff --git a/gamestonk_terminal/stocks/due_diligence/finnhub_view.py b/gamestonk_terminal/stocks/due_diligence/finnhub_view.py index fa08223af46..b593125dbc9 100644 --- a/gamestonk_terminal/stocks/due_diligence/finnhub_view.py +++ b/gamestonk_terminal/stocks/due_diligence/finnhub_view.py @@ -77,7 +77,13 @@ def plot_rating_over_time( @log_start_end(log=logger) @check_api_key(["API_FINNHUB_KEY"]) -def rating_over_time(ticker: str, num: int, raw: bool, export: str): +def rating_over_time( + ticker: str, + num: int, + raw: bool, + export: str, + external_axes: Optional[List[plt.Axes]] = None, +): """Rating over time (monthly). [Source: Finnhub] Parameters @@ -116,9 +122,7 @@ def rating_over_time(ticker: str, num: int, raw: bool, export: str): title="Monthly Rating", ) else: - plot_rating_over_time(df_rot.head(num), ticker) - - console.print("") + plot_rating_over_time(df_rot.head(num), ticker, external_axes) export_data( export, diff --git a/gamestonk_terminal/stocks/fundamental_analysis/dcf_model.py b/gamestonk_terminal/stocks/fundamental_analysis/dcf_model.py index f2e45c75086..efe90f71580 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/dcf_model.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/dcf_model.py @@ -23,6 +23,121 @@ from gamestonk_terminal.stocks.fundamental_analysis import dcf_static logger = logging.getLogger(__name__) +CURRENCIES = [ + "ALL", + "AFN", + "ARS", + "AWG", + "AUD", + "AZN", + "BSD", + "BBD", + "BYN", + "BZD", + "BMD", + "BOB", + "BAM", + "BWP", + "BGN", + "BRL", + "BND", + "KHR", + "CAD", + "KYD", + "CLP", + "CNY", + "COP", + "CRC", + "HRK", + "CUP", + "CZK", + "DKK", + "DOP", + "XCD", + "EGP", + "SVC", + "EUR", + "FKP", + "FJD", + "GHS", + "GIP", + "GTQ", + "GGP", + "GYD", + "HNL", + "HKD", + "HUF", + "ISK", + "INR", + "IDR", + "IRR", + "IMP", + "ILS", + "JMD", + "JPY", + "JEP", + "KZT", + "KPW", + "KRW", + "KGS", + "LAK", + "LBP", + "LRD", + "MKD", + "MYR", + "MUR", + "MXN", + "MNT", + "MNT", + "MZN", + "NAD", + "NPR", + "ANG", + "NZD", + "NIO", + "NGN", + "NOK", + "OMR", + "PKR", + "PAB", + "PYG", + "PEN", + "PHP", + "PLN", + "QAR", + "RON", + "RUB", + "SHP", + "SAR", + "RSD", + "SCR", + "SGD", + "SBD", + "SOS", + "KRW", + "ZAR", + "LKR", + "SEK", + "CHF", + "SRD", + "SYP", + "TWD", + "THB", + "TTD", + "TRY", + "TVD", + "UAH", + "AED", + "GBP", + "USD", + "UYU", + "UZS", + "VEF", + "VND", + "YER", + "ZWD", +] + @log_start_end(log=logger) def string_float(string: str) -> float: @@ -273,6 +388,8 @@ def create_dataframe(ticker: str, statement: str, period: str = "annual"): The financial statement requested rounding : int The amount of rounding to use + statement_currency: str + The currency the financial statements are reported in """ if statement not in ["BS", "CF", "IS"]: raise ValueError("statement variable must be 'BS','CF', or 'IS'") @@ -289,18 +406,18 @@ def create_dataframe(ticker: str, statement: str, period: str = "annual"): r = requests.get(URL, headers=dcf_static.headers) if "404 - Page Not Found" in r.text: - return pd.DataFrame(), None + return pd.DataFrame(), None, None soup = BeautifulSoup(r.content, "html.parser") table = soup.find("table", attrs={"class": re.compile("fintbl")}) if table is None: - return pd.DataFrame(), None + return pd.DataFrame(), None, None head = table.find("thead") if head is None: - return pd.DataFrame(), None + return pd.DataFrame(), None, None columns = head.find_all("th") if columns is None: - return pd.DataFrame(), None + return pd.DataFrame(), None, None years = [x.get_text().strip() for x in columns] len_data = len(years) - 1 @@ -315,7 +432,12 @@ def create_dataframe(ticker: str, statement: str, period: str = "annual"): elif "billions" in phrase: rounding = 1_000_000_000 else: - return pd.DataFrame(), None + return pd.DataFrame(), None, None + + for currency in CURRENCIES: + if currency.lower() in phrase: + statement_currency = currency + break body = table.find("tbody") rows = body.find_all("tr") @@ -351,11 +473,11 @@ def create_dataframe(ticker: str, statement: str, period: str = "annual"): if vals[0] in df.index: blank_list = ["0" for _ in df.loc[vals[0]].to_list()] else: - return pd.DataFrame(), None + return pd.DataFrame(), None, None for i, _ in enumerate(vals[1][1:]): df = insert_row(vals[1][i + 1], vals[1][i], df, blank_list) - return df, rounding + return df, rounding, statement_currency @log_start_end(log=logger) diff --git a/gamestonk_terminal/stocks/fundamental_analysis/dcf_view.py b/gamestonk_terminal/stocks/fundamental_analysis/dcf_view.py index 95fd960fa46..2acba9e0f2c 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/dcf_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/dcf_view.py @@ -119,7 +119,7 @@ class CreateExcelFA: @log_start_end(log=logger) def get_data(self, statement: str, row: int, header: bool) -> pd.DataFrame: - df, rounding = dcf_model.create_dataframe(self.info["ticker"], statement) + df, rounding, _ = dcf_model.create_dataframe(self.info["ticker"], statement) if df.empty: raise ValueError("Could generate a dataframe for the ticker") self.info["rounding"] = rounding diff --git a/gamestonk_terminal/stocks/options/barchart_view.py b/gamestonk_terminal/stocks/options/barchart_view.py index ff2fc851dc6..17fad7c791e 100644 --- a/gamestonk_terminal/stocks/options/barchart_view.py +++ b/gamestonk_terminal/stocks/options/barchart_view.py @@ -27,7 +27,7 @@ def print_options_data(ticker: str, export: str): data = barchart_model.get_options_info(ticker) print_rich_table( - data, show_index=False, headers=list(data.columns), title="Options Information" + data, show_index=False, headers=["Info", "Value"], title="Options Information" ) - console.print("") + console.print() export_data(export, os.path.dirname(os.path.abspath(__file__)), "info", data) diff --git a/gamestonk_terminal/stocks/options/chartexchange_view.py b/gamestonk_terminal/stocks/options/chartexchange_view.py index 6510160d137..9a0b52b1fee 100644 --- a/gamestonk_terminal/stocks/options/chartexchange_view.py +++ b/gamestonk_terminal/stocks/options/chartexchange_view.py @@ -3,18 +3,32 @@ __docformat__ = "numpy" import logging import os +from typing import Optional, List +import pandas as pd + +import mplfinance as mpf +import matplotlib.pyplot as plt from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.helper_funcs import export_data, print_rich_table from gamestonk_terminal.rich_config import console from gamestonk_terminal.stocks.options import chartexchange_model +from gamestonk_terminal.config_terminal import theme + +from gamestonk_terminal.helper_funcs import plot_autoscale logger = logging.getLogger(__name__) @log_start_end(log=logger) def display_raw( - ticker: str, date: str, call: bool, price: str, num: int = 20, export: str = "" + ticker: str, + date: str, + call: bool, + price: str, + num: int = 5, + export: str = "", + external_axes: Optional[List[plt.Axes]] = None, ) -> None: """Return raw stock data[chartexchange] @@ -35,6 +49,48 @@ def display_raw( """ df = chartexchange_model.get_option_history(ticker, date, call, price) + df["Date"] = pd.to_datetime(df["Date"]) + df = df.set_index("Date") + + candle_chart_kwargs = { + "type": "candle", + "style": theme.mpf_style, + "volume": True, + "xrotation": theme.xticks_rotation, + "scale_padding": {"left": 0.3, "right": 1, "top": 0.8, "bottom": 0.8}, + "update_width_config": { + "candle_linewidth": 0.6, + "candle_width": 0.8, + "volume_linewidth": 0.8, + "volume_width": 0.8, + }, + "warn_too_much_data": 10000, + } + # This plot has 2 axes + option_type = "call" if call else "put" + + if not external_axes: + candle_chart_kwargs["returnfig"] = True + candle_chart_kwargs["figratio"] = (10, 7) + candle_chart_kwargs["figscale"] = 1.10 + candle_chart_kwargs["figsize"] = plot_autoscale() + fig, ax = mpf.plot(df, **candle_chart_kwargs) + fig.suptitle( + f"Historical quotes for {ticker} {option_type}", + x=0.055, + y=0.965, + horizontalalignment="left", + ) + theme.visualize_output(force_tight_layout=False) + ax[0].legend() + else: + if len(external_axes) != 1: + logger.error("Expected list of 1 axis items.") + console.print("[red]Expected list of 1 axis items./n[/red]") + return + (ax1,) = external_axes + candle_chart_kwargs["ax"] = ax1 + mpf.plot(df, **candle_chart_kwargs) export_data( export, @@ -42,7 +98,6 @@ def display_raw( "hist", df, ) - print_rich_table( df.head(num), headers=list(df.columns), diff --git a/gamestonk_terminal/stocks/options/options_controller.py b/gamestonk_terminal/stocks/options/options_controller.py index 73bb581e71f..9dcee89ce99 100644 --- a/gamestonk_terminal/stocks/options/options_controller.py +++ b/gamestonk_terminal/stocks/options/options_controller.py @@ -633,7 +633,9 @@ Expiry: [/param]{self.selected_date or None} "--strike", dest="strike", type=float, - required="--chain" not in other_args and "-c" not in other_args, + required="--chain" not in other_args + and "-c" not in other_args + and "-h" not in other_args, help="Strike price to look at", ) parser.add_argument( @@ -668,6 +670,7 @@ Expiry: [/param]{self.selected_date or None} "--limit", dest="limit", type=int, + default=10, help="Limit of data rows to display", ) if other_args and "-" not in other_args[0][0]: diff --git a/gamestonk_terminal/stocks/options/tradier_view.py b/gamestonk_terminal/stocks/options/tradier_view.py index 4e370818f17..9d5dfa16561 100644 --- a/gamestonk_terminal/stocks/options/tradier_view.py +++ b/gamestonk_terminal/stocks/options/tradier_view.py @@ -258,29 +258,17 @@ def plot_oi( (ax,) = external_axes if not calls_only: - put_oi.plot( - x="strike", - y="open_interest", - label="Puts", - ax=ax, - marker="o", - ls="-", - ) + ax.plot(put_oi.index, put_oi.values, "-o", label="Puts") + if not puts_only: - call_oi.plot( - x="strike", - y="open_interest", - label="Calls", - ax=ax, - marker="o", - ls="-", - ) + ax.plot(call_oi.index, call_oi.values, "-o", label="Calls") + + ax.legend(loc=0) ax.axvline(current_price, lw=2, ls="--", label="Current Price", alpha=0.7) ax.axvline(max_pain, lw=3, label=f"Max Pain: {max_pain}", alpha=0.7) ax.set_xlabel("Strike Price") ax.set_ylabel("Open Interest (1k) ") ax.set_xlim(min_strike, max_strike) - ax.set_title(f"Open Interest for {ticker.upper()} expiring {expiry}") theme.style_primary_axis(ax) diff --git a/gamestonk_terminal/stocks/sector_industry_analysis/financedatabase_view.py b/gamestonk_terminal/stocks/sector_industry_analysis/financedatabase_view.py index a94b959d411..ebededfee03 100644 --- a/gamestonk_terminal/stocks/sector_industry_analysis/financedatabase_view.py +++ b/gamestonk_terminal/stocks/sector_industry_analysis/financedatabase_view.py @@ -166,7 +166,7 @@ def display_bars_financials( company_metric = company_metric * 100 else: - unit = " KMBTP"[magnitude] + unit = " KMBTP"[magnitude] if magnitude != 0 else "" colors = iter(theme.get_colors()) for name, metric, ticker in zip( @@ -174,12 +174,7 @@ def display_bars_financials( ): if len(name.split(" ")) > 6 and len(name) > 40: name = f'{" ".join(name.split(" ")[:4])}\n{" ".join(name.split(" ")[4:])}' - ax.barh(name, metric, label=ticker, color=next(colors)) - - handles, _ = plt.gca().get_legend_handles_labels() - ax.legend( - reversed(handles), reversed(company_ticker[::-1]), loc="lower right" - ) + ax.barh(f"{name} ({ticker})", metric, label=ticker, color=next(colors)) metric_title = ( "".join( @@ -193,21 +188,16 @@ def display_bars_financials( benchmark = np.median(company_metric) ax.axvline(x=benchmark, lw=3, ls="--", c="grey") - if unit != " ": - units = f" [{unit}] " - else: - units = " " - - title = f"{metric_title.capitalize()}{units}with benchmark of {benchmark:.2f} {unit}\n" + title = f"The {metric_title.title()} (benchmark: {benchmark:.2f}{unit}) of " title += marketcap + " cap companies " if marketcap else "Companies " if industry: - title += f"in {industry} industry\n" + title += f"in {industry} industry " elif sector: - title += f"in {sector} sector\n" + title += f"in {sector} sector " if country: title += f"in {country}" - title += " " if (industry or sector) else "\n" + title += " " if (industry or sector) else "" title += ( "(excl. data from international exchanges)" @@ -215,7 +205,11 @@ def display_bars_financials( else "(incl. data from international exchanges)" ) - ax.set_title(title) + ax.set_title(title, wrap=True, fontsize=11) + + labels = ax.get_xticks().tolist() + ax.set_xticks(labels) + ax.set_xticklabels([f"{label}{unit}" for label in labels]) theme.style_primary_axis(ax) diff --git a/gamestonk_terminal/stocks/sector_industry_analysis/sia_controller.py b/gamestonk_terminal/stocks/sector_industry_analysis/sia_controller.py index a47c6a6f863..9e0342e9797 100644 --- a/gamestonk_terminal/stocks/sector_industry_analysis/sia_controller.py +++ b/gamestonk_terminal/stocks/sector_industry_analysis/sia_controller.py @@ -181,6 +181,7 @@ class SectorIndustryAnalysisController(BaseController): self.stocks_data: dict = {} self.tickers: List = list() + self.currency: str = "" if ticker: data = yf.utils.get_json(f"https://finance.yahoo.com/quote/{ticker}") @@ -872,14 +873,6 @@ class SectorIndustryAnalysisController(BaseController): help="Metric to visualize", choices=self.vis_choices, ) - parser.add_argument( - "-l", - "--limit", - dest="limit", - default=10, - help="Limit number of companies to display", - type=check_positive, - ) parser.add_argument( "-p", @@ -889,21 +882,24 @@ class SectorIndustryAnalysisController(BaseController): help="Limit number of periods to display", type=check_positive, ) + parser.add_argument( - "-r", - "--raw", - action="store_true", - dest="raw", - default=False, - help="Output all raw data", + "-cc", + "--convert_currency", + dest="convert_currency", + help="Convert the currency of the chosen country to a specified currency. By default, this is set " + "to USD (US Dollars).", + default="USD", ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-m") ns_parser = parse_known_args_and_warn( - parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES + parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES, limit=10, raw=True ) if ns_parser: + if ns_parser.convert_currency != self.currency: + self.stocks_data = {} ( self.stocks_data, self.tickers, @@ -916,6 +912,7 @@ class SectorIndustryAnalysisController(BaseController): period=self.period, period_length=ns_parser.period, marketcap=self.mktcap, + convert_currency=ns_parser.convert_currency, exclude_exchanges=self.exclude_exchanges, limit=ns_parser.limit, export=ns_parser.export, @@ -923,6 +920,8 @@ class SectorIndustryAnalysisController(BaseController): already_loaded_stocks_data=self.stocks_data, ) + self.currency = ns_parser.convert_currency + @log_start_end(log=logger) def call_cps(self, other_args: List[str]): """Process cps command""" diff --git a/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_model.py b/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_model.py index e3905402af8..e85259645cc 100644 --- a/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_model.py +++ b/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_model.py @@ -8,6 +8,7 @@ from typing import Dict, Any import numpy as np import pandas as pd from tqdm import tqdm +import yfinance as yf from gamestonk_terminal.decorators import log_start_end from gamestonk_terminal.rich_config import console @@ -81,7 +82,12 @@ sa_keys = { @log_start_end(log=logger) def get_stocks_data( - stocks: list, finance_key: str, sa_dict: dict, stocks_data: dict, period: str + stocks: list, + finance_key: str, + sa_dict: dict, + stocks_data: dict, + period: str, + convert_currency: str = "USD", ): """Get stocks data based on a list of stocks and the finance key. The function searches for the correct financial statement automatically. [Source: StockAnalysis] @@ -101,6 +107,8 @@ def get_stocks_data( for the first time. period : str Whether you want annually, quarterly or trailing financial statements. + convert_currency : str + Choose in what currency you wish to convert each company's financial statement. Default is USD (US Dollars). Returns ------- @@ -114,16 +122,36 @@ def get_stocks_data( if statement not in stocks_data: stocks_data[statement] = {} used_statement = statement - symbol_statement = create_dataframe(symbol, statement, period.lower()) + symbol_statement, rounding, currency = create_dataframe( + symbol, statement, period.lower() + ) - if symbol_statement[0].empty: + if symbol_statement.empty: no_data.append(symbol) continue - stocks_data[statement][symbol] = ( - change_type_dataframes(symbol_statement[0]) * symbol_statement[1] + symbol_statement_rounded = ( + change_type_dataframes(symbol_statement) * rounding ) + if convert_currency and convert_currency != currency: + currency_data = yf.download( + f"{currency}{convert_currency}=X", + start=f"{symbol_statement_rounded.columns[0]}-01-01", + end=f"{symbol_statement_rounded.columns[-1]}-12-31", + progress=False, + )["Adj Close"] + + for year in symbol_statement_rounded: + # Since fiscal year can differ, I take the median of the currency and not the last value + # of the year + symbol_statement_rounded[year] = ( + symbol_statement_rounded[year] + * currency_data.loc[year].median() + ) + + stocks_data[statement][symbol] = symbol_statement_rounded + if period in ["Quarterly", "Trailing"]: for symbol in stocks_data[used_statement]: stocks_data[used_statement][symbol].columns = ( diff --git a/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_view.py b/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_view.py index 3853f7157b2..a008f1fca31 100644 --- a/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_view.py +++ b/gamestonk_terminal/stocks/sector_industry_analysis/stockanalysis_view.py @@ -39,6 +39,7 @@ def display_plots_financials( period_length: int, marketcap: str = "", exclude_exchanges: bool = True, + convert_currency: str = "USD", limit: int = 10, export: str = "", external_axes: Optional[List[plt.Axes]] = None, @@ -67,6 +68,8 @@ def display_plots_financials( Select stocks based on the market cap. exclude_exchanges: bool When you wish to include different exchanges use this boolean. + convert_currency : str + Choose in what currency you wish to convert each company's financial statement. Default is USD (US Dollars). limit: int Limit amount of companies displayed (default is 10) export: str @@ -104,10 +107,23 @@ def display_plots_financials( return dict(), list() stocks_data = stockanalysis_model.get_stocks_data( - company_tickers, finance_key, sa_dict, already_loaded_stocks_data, period + company_tickers, + finance_key, + sa_dict, + already_loaded_stocks_data, + period, + convert_currency, ) stocks_data_statement = copy.deepcopy(stocks_data[used_statement]) + + if not stocks_data_statement: + console.print( + "It appears the entire dataset is empty. This could be due to the source being unavailable. " + "Please check whether https://stockanalysis.com/ is accessible. \n" + ) + return dict(), list() + company_tickers = list(stocks_data[used_statement].keys()) if len(stocks_data_statement[company_tickers[0]].columns) > period_length: @@ -137,17 +153,25 @@ def display_plots_financials( maximum_value = df.max().max() + if convert_currency: + denomination = f"[{convert_currency} " + else: + denomination = "[" + if maximum_value > 1_000_000_000: df = df / 1_000_000_000 - denomination = "[$ Billions]" - elif 1_000_000_000 > maximum_value: + denomination += "Billions]" + elif maximum_value > 1_000_000: df = df / 1_000_000 - denomination = "[$ Millions]" - elif 1_000_000 > maximum_value: + denomination += "Millions]" + elif maximum_value > 1_000: df = df / 1_000 - denomination = "[$ Thousands]" + denomination += "Thousands]" else: - denomination = "" + if convert_currency: + denomination = f"[{convert_currency}]" + else: + denomination = "" if raw: print_rich_table( diff --git a/gamestonk_terminal/stocks/stocks_helper.py b/gamestonk_terminal/stocks/stocks_helper.py index 352e3defea9..e1d5c69c920 100644 --- a/gamestonk_terminal/stocks/stocks_helper.py +++ b/gamestonk_terminal/stocks/stocks_helper.py @@ -401,12 +401,13 @@ def display_candle( theme.visualize_output(force_tight_layout=False) else: - if len(external_axes) != 1: + if len(external_axes) != 2: logger.error("Expected list of one axis item.") - console.print("[red]Expected list of 1 axis items./n[/red]") + console.print("[red]Expected list of 2 axis items./n[/red]") return - (ax1,) = external_axes + ax1, ax2 = external_axes candle_chart_kwargs["ax"] = ax1 + candle_chart_kwargs["volume"] = ax2 mpf.plot(df_stock, **candle_chart_kwargs) else: @@ -713,12 +714,12 @@ def load_ticker( return df_data -def process_candle(df_data: pd.DataFrame) -> pd.DataFrame: +def process_candle(df: pd.DataFrame) -> pd.DataFrame: """Process DataFrame into candle style plot Parameters ---------- - df_data : DataFrame + df : DataFrame Stock dataframe. Returns @@ -726,6 +727,7 @@ def process_candle(df_data: pd.DataFrame) -> pd.DataFrame: DataFrame A Panda's data frame with columns Open, High, Low, Close, Adj Close, Volume, date_id, OC-High, OC-Low. """ + df_data = df.copy() df_data["date_id"] = (df_data.index.date - df_data.index.date.min()).astype( "timedelta64[D]" ) diff --git a/poetry.lock b/poetry.lock index b2904a64fb5..9eab1fa5d7d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -359,15 +359,15 @@ webencodings = "*" [[package]] name = "boto3" -version = "1.21.19" +version = "1.21.25" description = "The AWS SDK for Python" category = "main" -optional = false +optional = true python-versions = ">= 3.6" [package.dependencies] -botocore = ">=1.24.19,<1.25.0" -jmespath = ">=0.7.1,<1.0.0" +botocore = ">=1.24.25,<1.25.0" +jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.5.0,<0.6.0" [package.extras] @@ -375,19 +375,19 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.24.19" +version = "1.24.25" description = "Low-level, data-driven core of boto 3." category = "main" -optional = false +optional = true python-versions = ">= 3.6" [package.dependencies] -jmespath = ">=0.7.1,<1.0.0" +jmespath = ">=0.7.1,<2.0.0" python-dateutil = ">=2.1,<3.0.0" urllib3 = ">=1.25.4,<1.27" [package.extras] -crt = ["awscrt (==0.12.5)"] +crt = ["awscrt (==0.13.5)"] [[package]] name = "bs4" @@ -535,7 +535,7 @@ toml = ["tomli"] [[package]] name = "cryptography" -version = "36.0.1" +version = "36.0.2" description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." category = "main" optional = false @@ -593,7 +593,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" [[package]] name = "dateparser" -version = "1.1.0" +version = "1.1.1" description = "Date parsing library designed to parse dates from HTML pages" category = "main" optional = false @@ -602,7 +602,7 @@ python-versions = ">=3.5" [package.dependencies] python-dateutil = "*" pytz = "*" -regex = "<2019.02.19 || >2019.02.19,<2021.8.27 || >2021.8.27" +regex = "<2019.02.19 || >2019.02.19,<2021.8.27 || >2021.8.27,<2022.3.15" tzlocal = "*" [package.extras] @@ -681,22 +681,6 @@ category = "main" optional = false python-versions = ">=3.6" -[[package]] -name = "df2img" -version = "0.2.4" -description = "Save a Pandas DataFrame as image" -category = "main" -optional = true -python-versions = ">=3.8,<4.0" - -[package.dependencies] -kaleido = "0.2.1" -pandas = ">=1.0.0,<2.0.0" -plotly = ">=5.3.1,<6.0.0" - -[package.extras] -docs = ["ipykernel (>=6.6.0,<7.0.0)", "nbformat (>=5.1.3,<6.0.0)", "nbsphinx (>=0.8.7,<0.9.0)", "numpydoc (>=1.1.0,<2.0.0)", "pandoc (>=1.1.0,<2.0.0)", "sphinx (>=4.3.0,<5.0.0)", "sphinx-copybutton (>=0.4.0,<0.5.0)", "sphinx-rtd-theme (>=1.0.0,<2.0.0)"] - [[package]] name = "disnake" version = "2.4.0" @@ -738,6 +722,14 @@ idna = ["idna (>=2.1,<4.0)"] trio = ["trio (>=0.14,<0.20)"] wmi = ["wmi (>=1.5.1,<2.0.0)"] +[[package]] +name = "docstring-to-markdown" +version = "0.10" +description = "On the fly conversion of Python docstrings to markdown" +category = "main" +optional = true +python-versions = ">=3.6" + [[package]] name = "docutils" version = "0.16" @@ -948,7 +940,7 @@ python-versions = "*" [[package]] name = "fonttools" -version = "4.30.0" +version = "4.31.2" description = "Tools to manipulate font files" category = "main" optional = false @@ -969,7 +961,7 @@ woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] [[package]] name = "formulaic" -version = "0.3.1" +version = "0.3.2" description = "An implementation of Wilkinson formulas." category = "main" optional = false @@ -1075,7 +1067,7 @@ gitdb = ">=4.0.1,<5" [[package]] name = "google-auth" -version = "2.6.0" +version = "2.6.2" description = "Google Authentication Library" category = "main" optional = true @@ -1120,7 +1112,7 @@ six = "*" [[package]] name = "grpcio" -version = "1.44.0" +version = "1.45.0" description = "HTTP/2-based RPC framework" category = "main" optional = false @@ -1130,7 +1122,7 @@ python-versions = ">=3.6" six = ">=1.5.2" [package.extras] -protobuf = ["grpcio-tools (>=1.44.0)"] +protobuf = ["grpcio-tools (>=1.45.0)"] [[package]] name = "h11" @@ -1175,7 +1167,7 @@ python-dateutil = "*" [[package]] name = "identify" -version = "2.4.11" +version = "2.4.12" description = "File identification library for Python" category = "dev" optional = false @@ -1234,7 +1226,7 @@ python-versions = "*" [[package]] name = "interface-meta" -version = "1.2.4" +version = "1.2.5" description = "InterfaceMeta" category = "main" optional = false @@ -1417,13 +1409,27 @@ parso = ">=0.8.0,<0.9.0" qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"] +[[package]] +name = "jedi-language-server" +version = "0.35.1" +description = "A language server for Jedi!" +category = "main" +optional = true +python-versions = ">=3.6,<4.0" + +[package.dependencies] +docstring-to-markdown = "<1.0.0" +jedi = ">=0.18.0,<0.19.0" +pydantic = ">=1.7,<2.0" +pygls = ">=0.11.1,<0.12.0" + [[package]] name = "jinja2" -version = "3.0.3" +version = "3.1.0" description = "A very fast and expressive template engine." category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] MarkupSafe = ">=2.0" @@ -1433,11 +1439,11 @@ i18n = ["Babel (>=2.7)"] [[package]] name = "jmespath" -version = "0.10.0" +version = "1.0.0" description = "JSON Matching Expressions" category = "main" -optional = false -python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +optional = true +python-versions = ">=3.7" [[package]] name = "joblib" @@ -1542,9 +1548,21 @@ python-versions = ">=3.6" pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""} traitlets = "*" +[[package]] +name = "jupyter-lsp" +version = "1.5.1" +description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +entrypoints = "*" +jupyter-server = ">=1.1.2" + [[package]] name = "jupyter-server" -version = "1.15.4" +version = "1.15.6" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." category = "main" optional = false @@ -1593,6 +1611,29 @@ tornado = ">=6.1.0" test = ["coverage", "pytest (>=6.0)", "pytest-cov", "pytest-console-scripts", "pytest-check-links (>=0.5)", "jupyterlab-server[test] (>=2.2,<3.0)", "requests", "requests-cache", "virtualenv", "check-manifest"] ui-tests = ["build"] +[[package]] +name = "jupyterlab-code-formatter" +version = "1.4.10" +description = "Code formatter for JupyterLab" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +jupyterlab = ">=3.0,<4.0" + +[[package]] +name = "jupyterlab-lsp" +version = "3.10.1" +description = "Coding assistance for JupyterLab with Language Server Protocol" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +jupyter-lsp = ">=1.4.0" +jupyterlab = ">=3.1.0,<4.0.0a0" + [[package]] name = "jupyterlab-pygments" version = "0.1.2" @@ -1606,28 +1647,28 @@ pygments = ">=2.4.1,<3" [[package]] name = "jupyterlab-server" -version = "2.10.3" +version = "2.11.2" description = "A set of server components for JupyterLab and JupyterLab like applications ." category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] babel = "*" entrypoints = ">=0.2.2" -jinja2 = ">=2.10" +jinja2 = ">=3.0.3" json5 = "*" jsonschema = ">=3.0.1" -jupyter-server = ">=1.4,<2.0" +jupyter-server = ">=1.8,<2.0" packaging = "*" requests = "*" [package.extras] -test = ["codecov", "ipykernel", "pytest (>=5.3.2)", "pytest-cov", "jupyter-server", "openapi-core (>=0.14.0,<0.15.0)", "pytest-console-scripts", "strict-rfc3339", "ruamel.yaml", "wheel"] +test = ["codecov", "ipykernel", "pytest (>=5.3.2)", "pytest-cov", "jupyter-server", "openapi-core (>=0.14.2)", "pytest-console-scripts", "strict-rfc3339", "ruamel.yaml", "wheel", "openapi-spec-validator (<0.5)"] [[package]] name = "jupyterlab-widgets" -version = "1.0.2" +version = "1.1.0" description = "A JupyterLab extension." category = "main" optional = false @@ -1635,7 +1676,7 @@ python-versions = ">=3.6" [[package]] name = "kaleido" -version = "0.2.1" +version = "0.2.1.post1" description = "Static image export for web-based visualization libraries with zero dependencies" category = "main" optional = true @@ -1954,16 +1995,28 @@ linkify = ["linkify-it-py (>=1.0,<2.0)"] rtd = ["ipython", "sphinx-book-theme (>=0.1.0,<0.2.0)", "sphinx-panels (>=0.5.2,<0.6.0)", "sphinxcontrib-bibtex (>=2.1,<3.0)", "sphinxext-rediraffe (>=0.2,<1.0)", "sphinxcontrib.mermaid (>=0.6.3,<0.7.0)", "sphinxext-opengraph (>=0.4.2,<0.5.0)"] testing = ["beautifulsoup4", "coverage", "docutils (>=0.17.0,<0.18.0)", "pytest (>=3.6,<4)", "pytest-cov", "pytest-regressions"] +[[package]] +name = "natsort" +version = "8.1.0" +description = "Simple yet flexible natural sorting in Python." +category = "main" +optional = true +python-versions = ">=3.6" + +[package.extras] +fast = ["fastnumbers (>=2.0.0)"] +icu = ["PyICU (>=1.0.0)"] + [[package]] name = "nbclassic" -version = "0.3.6" +version = "0.3.7" description = "Jupyter Notebook as a Jupyter Server extension." category = "main" optional = false python-versions = ">=3.7" [package.dependencies] -jupyter-server = ">=1.8,<2.0" +jupyter-server = ">=1.8" notebook = "<7" notebook-shim = ">=0.1.0" @@ -2634,7 +2687,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "pydantic" -version = "1.9.0" +version = "1.8.2" description = "Data validation and settings management using python 3.6 type hinting" category = "main" optional = true @@ -2680,6 +2733,24 @@ category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +[[package]] +name = "pygls" +version = "0.11.3" +description = "a pythonic generic language server (pronounced like \"pie glass\")." +category = "main" +optional = true +python-versions = "*" + +[package.dependencies] +pydantic = ">=1.7,<1.9" +typeguard = ">=2.10.0,<3" + +[package.extras] +dev = ["bandit (==1.6.0)", "flake8 (==3.7.7)", "mypy (==0.812)"] +docs = ["sphinx (==2.0.1)", "sphinx-rtd-theme (==0.4.3)"] +test = ["mock (==3.0.5)", "pytest (==4.5.0)", "pytest-asyncio (==0.10.0)"] +ws = ["websockets (>=9.0.0,<10.0.0)"] + [[package]] name = "pygments" version = "2.11.2" @@ -2863,7 +2934,7 @@ diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "pyportfolioopt" -version = "1.5.1" +version = "1.5.2" description = "Financial portfolio optimization in python" category = "main" optional = false @@ -3042,7 +3113,7 @@ requests = ">=2.0" [[package]] name = "pytz" -version = "2021.3" +version = "2022.1" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -3292,7 +3363,7 @@ name = "s3transfer" version = "0.5.2" description = "An Amazon S3 Transfer Manager" category = "main" -optional = false +optional = true python-versions = ">= 3.6" [package.dependencies] @@ -3435,14 +3506,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" [[package]] name = "slack-bolt" -version = "1.11.6" +version = "1.13.0" description = "The Bolt Framework for Python" category = "main" optional = true python-versions = ">=3.6" [package.dependencies] -slack-sdk = ">=3.9.0,<4" +slack-sdk = ">=3.15.2,<4" [package.extras] adapter = ["boto3 (<=2)", "bottle (>=0.12,<1)", "chalice (>=1.26.5,<2)", "click (>=7,<8)", "CherryPy (>=18,<19)", "Django (>=3,<5)", "falcon (>=2,<4)", "fastapi (>=0.70.0,<1)", "Flask (>=1,<3)", "Werkzeug (>=2,<3)", "pyramid (>=1,<3)", "sanic (>=21,<22)", "starlette (>=0.14,<1)", "tornado (>=6,<7)", "uvicorn (<1)", "gunicorn (>=20,<21)", "websocket-client (>=1.2.3,<2)"] @@ -3942,7 +4013,7 @@ python-versions = ">= 3.5" [[package]] name = "tqdm" -version = "4.63.0" +version = "4.63.1" description = "Fast, Extensible Progress Meter" category = "main" optional = false @@ -4008,9 +4079,21 @@ async-generator = ">=1.10" trio = ">=0.11" wsproto = ">=0.14" +[[package]] +name = "typeguard" +version = "2.13.3" +description = "Run-time type checker for Python" +category = "main" +optional = true +python-versions = ">=3.5.3" + +[package.extras] +doc = ["sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"] +test = ["pytest", "typing-extensions", "mypy"] + [[package]] name = "types-python-dateutil" -version = "2.8.9" +version = "2.8.10" description = "Typing stubs for python-dateutil" category = "dev" optional = false @@ -4018,7 +4101,7 @@ python-versions = "*" [[package]] name = "types-pytz" -version = "2021.3.5" +version = "2021.3.6" description = "Typing stubs for pytz" category = "dev" optional = false @@ -4026,7 +4109,7 @@ python-versions = "*" [[package]] name = "types-pyyaml" -version = "6.0.4" +version = "6.0.5" description = "Typing stubs for PyYAML" category = "dev" optional = false @@ -4034,7 +4117,7 @@ python-versions = "*" [[package]] name = "types-requests" -version = "2.27.12" +version = "2.27.14" description = "Typing stubs for requests" category = "dev" optional = false @@ -4045,7 +4128,7 @@ types-urllib3 = "<1.27" [[package]] name = "types-setuptools" -version = "57.4.10" +version = "57.4.11" description = "Typing stubs for setuptools" category = "dev" optional = false @@ -4069,7 +4152,7 @@ python-versions = ">=3.6" [[package]] name = "tzdata" -version = "2021.5" +version = "2022.1" description = "Provider of IANA time zone data" category = "main" optional = false @@ -4102,7 +4185,7 @@ python-versions = ">=3.7" [[package]] name = "undetected-chromedriver" -version = "3.1.5.post2" +version = "3.1.5.post4" description = "('Selenium.webdriver.Chrome replacement with compatiblity for Brave, and other Chromium based browsers.', 'Not triggered by CloudFlare/Imperva/hCaptcha and such.', 'NOTE: results may vary due to many factors. No guarantees are given, except for ongoing efforts in understanding detection algorithms.')" category = "main" optional = true @@ -4139,7 +4222,7 @@ test = ["pytest (>=2.7.3)"] [[package]] name = "urllib3" -version = "1.26.8" +version = "1.26.9" description = "HTTP library with thread-safe connection pooling, file post, and more." category = "main" optional = false @@ -4153,7 +4236,7 @@ pyOpenSSL = {version = ">=0.14", optional = true, markers = "extra == \"secure\" PySocks = {version = ">=1.5.6,<1.5.7 || >1.5.7,<2.0", optional = true, markers = "extra == \"socks\""} [package.extras] -brotli = ["brotlipy (>=0.6.0)"] +brotli = ["brotlicffi (>=0.8.0)", "brotli (>=1.0.9)", "brotlipy (>=0.6.0)"] secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"] socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] @@ -4219,7 +4302,7 @@ yarl = {version = "*", markers = "python_version >= \"3.6\""} [[package]] name = "virtualenv" -version = "20.13.3" +version = "20.13.4" description = "Virtual Python Environment builder" category = "dev" optional = false @@ -4237,7 +4320,7 @@ testing = ["coverage (>=4)", "coverage-enable-subprocess (>=1)", "flaky (>=3)", [[package]] name = "voila" -version = "0.3.3" +version = "0.3.4" description = "Voilà turns Jupyter notebooks into standalone web applications" category = "main" optional = false @@ -4388,14 +4471,15 @@ test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [extras] -discord = ["pyimgur", "df2img", "fastapi", "uvicorn", "disnake", "PyNaCl", "pyTelegramBotAPI", "slack-bolt", "undetected-chromedriver"] -installer = ["pyinstaller"] prediction = ["tensorflow"] +bots = ["boto3", "pyimgur", "fastapi", "uvicorn", "disnake", "PyNaCl", "pyTelegramBotAPI", "slack-bolt", "undetected-chromedriver", "natsort", "kaleido"] +jupyterlab = ["jupyterlab-code-formatter", "jupyterlab-lsp", "jedi-language-server"] +installer = ["pyinstaller"] 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"sha256:8831f689379ac9e2a62668157381379ed74b3702980e08e71f8673c179c4e3c7"}, + {file = "types-pytz-2021.3.6.tar.gz", hash = "sha256:74547fd90d8d8ab4f1eedf3a344a7d186d97486973895f81221a712e1e2cd993"}, + {file = "types_pytz-2021.3.6-py3-none-any.whl", hash = "sha256:6805c72d51118923c5bf98633c39593d5b464d2ab49a803440e2d7ab6b8920df"}, ] types-pyyaml = [ - {file = "types-PyYAML-6.0.4.tar.gz", hash = "sha256:6252f62d785e730e454dfa0c9f0fb99d8dae254c5c3c686903cf878ea27c04b7"}, - {file = "types_PyYAML-6.0.4-py3-none-any.whl", hash = "sha256:693b01c713464a6851f36ff41077f8adbc6e355eda929addfb4a97208aea9b4b"}, + {file = "types-PyYAML-6.0.5.tar.gz", hash = "sha256:464e050914f3d1d83a8c038e1cf46da5cb96b7cd02eaa096bcaa03675edd8a2e"}, + {file = "types_PyYAML-6.0.5-py3-none-any.whl", hash = "sha256:2fd21310870addfd51db621ad9f3b373f33ee3cbb81681d70ef578760bd22d35"}, ] types-requests = [ - {file = "types-requests-2.27.12.tar.gz", hash = "sha256:fd1382fa2e28eac848faedb0332840204f06f0cb517008e3c7b8282ca53e56d2"}, - {file = "types_requests-2.27.12-py3-none-any.whl", hash = "sha256:120c949953b618e334bbe78de38e65aa261e1f48df021a05f0be833a848e4ba7"}, + {file = "types-requests-2.27.14.tar.gz", hash = "sha256:04579ee164f7c2659be46950e3c2f8d51a081ad252ef1b01d4b12faba5c3810b"}, + {file = "types_requests-2.27.14-py3-none-any.whl", hash = "sha256:c01838abfe3e8a83ba68346cd373afff97594c19c15c922ddee4a0e80ba7e329"}, ] types-setuptools = [ - {file = "types-setuptools-57.4.10.tar.gz", hash = "sha256:9a13513679c640f6616e2d9ab50d431c99ca8ae9848a97243f887c80fd5cf294"}, - {file = "types_setuptools-57.4.10-py3-none-any.whl", hash = "sha256:ddc98da82c12e1208012d65276641a132d3aadc78ecfff68fd3e17d85933a3c1"}, + {file = "types-setuptools-57.4.11.tar.gz", hash = "sha256:262f7406e0c7d705ad6bb4526b5b761fa500bf99eab74de85ac3592187d62935"}, + {file = "types_setuptools-57.4.11-py3-none-any.whl", hash = "sha256:fbb0647569d6fb2f6bc472402265250c0ffa53e60180d2cbfee9e84f085921f0"}, ] types-urllib3 = [ {file = "types-urllib3-1.26.11.tar.gz", hash = "sha256:24d64e441168851eb05f1d022de18ae31558f5649c8f1117e384c2e85e31315b"}, @@ -7154,8 +7255,8 @@ typing-extensions = [ {file = "typing_extensions-4.1.1.tar.gz", hash = "sha256:1a9462dcc3347a79b1f1c0271fbe79e844580bb598bafa1ed208b94da3cdcd42"}, ] tzdata = [ - {file = "tzdata-2021.5-py2.py3-none-any.whl", hash = "sha256:3eee491e22ebfe1e5cfcc97a4137cd70f092ce59144d81f8924a844de05ba8f5"}, - {file = "tzdata-2021.5.tar.gz", hash = "sha256:68dbe41afd01b867894bbdfd54fa03f468cfa4f0086bfb4adcd8de8f24f3ee21"}, + {file = "tzdata-2022.1-py2.py3-none-any.whl", hash = "sha256:238e70234214138ed7b4e8a0fab0e5e13872edab3be586ab8198c407620e2ab9"}, + {file = "tzdata-2022.1.tar.gz", hash = "sha256:8b536a8ec63dc0751342b3984193a3118f8fca2afe25752bb9b7fffd398552d3"}, ] tzlocal = [ {file = "tzlocal-4.1-py3-none-any.whl", hash = "sha256:28ba8d9fcb6c9a782d6e0078b4f6627af1ea26aeaa32b4eab5324abc7df4149f"}, @@ -7214,8 +7315,8 @@ ujson = [ {file = "ujson-5.1.0.tar.gz", hash = "sha256:a88944d2f99db71a3ca0c63d81f37e55b660edde0b07216fb65a3e46403ef004"}, ] undetected-chromedriver = [ - {file = "undetected-chromedriver-3.1.5.post2.tar.gz", hash = "sha256:050d5b1395a0a3284561438e93680d193dcd2ced449f4c308996cb4d1f86cc85"}, - {file = "undetected_chromedriver-3.1.5.post2-py3.10.egg", hash = "sha256:ba3760eef52a07057c7b3ee0a0e37db116ea56f6b9f36829fd38c8096d74843f"}, + {file = "undetected-chromedriver-3.1.5.post4.tar.gz", hash = "sha256:cfd77c38d86825f2f2b5ab296bf7814ef8f7199c3d9f46f24e57b93ea1662223"}, + {file = "undetected_chromedriver-3.1.5.post4-py3.10.egg", hash = "sha256:0b01b09a1a514bf279114e0d0d884704e09fe3f9a081240ae77ee4c23c23a9de"}, ] unidecode = [ {file = "Unidecode-1.3.4-py3-none-any.whl", hash = "sha256:afa04efcdd818a93237574791be9b2817d7077c25a068b00f8cff7baa4e59257"}, @@ -7226,8 +7327,8 @@ update-checker = [ {file = "update_checker-0.18.0.tar.gz", hash = "sha256:6a2d45bb4ac585884a6b03f9eade9161cedd9e8111545141e9aa9058932acb13"}, ] urllib3 = [ - {file = "urllib3-1.26.8-py2.py3-none-any.whl", hash = "sha256:000ca7f471a233c2251c6c7023ee85305721bfdf18621ebff4fd17a8653427ed"}, - {file = "urllib3-1.26.8.tar.gz", hash = "sha256:0e7c33d9a63e7ddfcb86780aac87befc2fbddf46c58dbb487e0855f7ceec283c"}, + {file = "urllib3-1.26.9-py2.py3-none-any.whl", hash = "sha256:44ece4d53fb1706f667c9bd1c648f5469a2ec925fcf3a776667042d645472c14"}, + {file = "urllib3-1.26.9.tar.gz", hash = "sha256:aabaf16477806a5e1dd19aa41f8c2b7950dd3c746362d7e3223dbe6de6ac448e"}, ] user-agent = [ {file = "user_agent-0.1.10.tar.gz", hash = "sha256:b86537cb2a9d3bda0e2afcc654ec15b383502836877a67520654acadf73f1723"}, @@ -7249,12 +7350,12 @@ vcrpy = [ {file = "vcrpy-4.1.1.tar.gz", hash = "sha256:57095bf22fc0a2d99ee9674cdafebed0f3ba763018582450706f7d3a74fff599"}, ] virtualenv = [ - {file = "virtualenv-20.13.3-py2.py3-none-any.whl", hash = "sha256:dd448d1ded9f14d1a4bfa6bfc0c5b96ae3be3f2d6c6c159b23ddcfd701baa021"}, - {file = "virtualenv-20.13.3.tar.gz", hash = "sha256:e9dd1a1359d70137559034c0f5433b34caf504af2dc756367be86a5a32967134"}, + {file = "virtualenv-20.13.4-py2.py3-none-any.whl", hash = "sha256:c3e01300fb8495bc00ed70741f5271fc95fed067eb7106297be73d30879af60c"}, + {file = "virtualenv-20.13.4.tar.gz", hash = "sha256:ce8901d3bbf3b90393498187f2d56797a8a452fb2d0d7efc6fd837554d6f679c"}, ] voila = [ - {file = "voila-0.3.3-py3-none-any.whl", hash = "sha256:39c63ac00410a1c15ea37010416cfb713fa153f6bcb1f191444408bdb1c24b25"}, - {file = "voila-0.3.3.tar.gz", hash = "sha256:de2d3cbc619c91df69bbeba312c481b196db9bc26949254b27c22fb2f16b943e"}, + {file = "voila-0.3.4-py3-none-any.whl", hash = "sha256:da859eba8b396b28cefe3b2271ef2fd0513d06461cba6bb3a53d9d5baae74220"}, + {file = "voila-0.3.4.tar.gz", hash = "sha256:2e18062445a501fc24c4ae59bd53b2e81ba2d20a7d52516db5b13c3c934f22c4"}, ] wcwidth = [ {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"}, diff --git a/pyproject.toml b/pyproject.toml index d1ac69f3d4b..e1a7893fdc3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -79,7 +79,6 @@ python-dotenv = "^0.19.2" Pygments = "2.11.2" libclang = {version = "<12.0.0", optional = true} tensorflow = {version = "^2.8.0", optional = true} -df2img = {version = "^0.2.4", optional = true} fastapi = {version = "^0.73.0", optional = true} uvicorn = {version = "^0.17.1", optional = true} disnake = {version = "^2.3.0", optional = true} @@ -91,11 +90,15 @@ squarify = "^0.4.3" finnhub-python = "^2.4.10" linearmodels = "^4.25" black = "22.1" -boto3 = "^1.21.18" undetected-chromedriver = {version = "^3.1.5", optional = true} ipywidgets = "^7.7.0" -ipyflex = "^0.2.2" pyinstaller = {version = "^4.10", optional = true} +boto3 = {version = "^1.21.14", optional = true} +natsort = {version = "^8.1.0", optional = true} +kaleido = {version = "^0.2.1", optional = true} +jupyterlab-code-formatter = {version = "^1.4.10", optional = true} +jupyterlab-lsp = {version = "^3.10.1", optional = true} +jedi-language-server = {version = "^0.35.1", optional = true} [tool.poetry.dev-dependencies] @@ -122,11 +125,11 @@ types-setuptools = "^57.4.7" pre-commit = "^2.16.0" pandas-market-calendars = "~3.2" pytest-cov = "^3.0.0" -boto3= "^1.21.14" [tool.poetry.extras] prediction = ["tensorflow"] -discord = ["pyimgur", "df2img", "fastapi", "uvicorn", "disnake", "PyNaCl", "pyTelegramBotAPI", "slack_bolt", "undetected_chromedriver"] +bots = ["boto3", "pyimgur", "fastapi", "uvicorn", "disnake", "PyNaCl", "pyTelegramBotAPI", "slack-bolt", "undetected-chromedriver", "natsort", "kaleido"] +jupyterlab = ["jupyterlab-code-formatter", "jupyterlab-lsp", "jedi-language-server"] installer = ["pyinstaller"] [build-system] diff --git a/requirements-full.txt b/requirements-full.txt index 646de595069..445affb84b5 100644 --- a/requirements-full.txt +++ b/requirements-full.txt @@ -25,8 +25,6 @@ beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_f beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.7" black==22.1.0; python_full_version >= "3.6.2" bleach==4.1.0; python_version >= "3.7" -boto3==1.21.19; python_version >= "3.6" -botocore==1.24.19; python_version >= "3.6" bs4==0.0.1 bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") cachetools==5.0.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") @@ -40,12 +38,12 @@ colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0 commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" convertdate==2.4.0; python_version >= "3.7" and python_version < "4" coverage==6.3.2; python_version >= "3.7" -cryptography==36.0.1; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" +cryptography==36.0.2; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" cssselect==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" cvxpy==1.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" cycler==0.11.0; python_version >= "3.7" cython==0.29.28; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "darwin" or python_full_version >= "3.3.0" and sys_platform == "darwin" and python_version >= "3.7" -dateparser==1.1.0; python_version >= "3.5" +dateparser==1.1.1; python_version >= "3.5" datetime==4.4; python_version >= "3.5" debugpy==1.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" decorator==5.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") @@ -70,8 +68,8 @@ finviz==1.4.4 finvizfinance==0.12.2; python_version >= "3.5" flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") flatbuffers==2.0 -fonttools==4.30.0; python_version >= "3.7" -formulaic==0.3.1; python_version >= "3.7" +fonttools==4.31.2; python_version >= "3.7" +formulaic==0.3.2; python_version >= "3.7" fred==3.1 fredapi==0.4.3 frozendict==2.3.0; python_version >= "3.6" @@ -82,42 +80,41 @@ gast==0.5.3; python_version >= "2.7" and python_full_version < "3.0.0" or python gitdb==4.0.9; python_version >= "3.7" gitpython==3.1.27; python_version >= "3.7" google-auth-oauthlib==0.4.6; python_version >= "3.6" -google-auth==2.6.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" +google-auth==2.6.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" google-pasta==0.2.0 -grpcio==1.44.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6" +grpcio==1.45.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6" h11==0.13.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" h5py==3.6.0; python_version >= "3.7" hijri-converter==2.2.3; python_version >= "3.6" holidays==0.11.3.1; python_version >= "3.6" -identify==2.4.11; python_version >= "3.7" and python_full_version >= "3.6.1" +identify==2.4.12; python_version >= "3.7" and python_full_version >= "3.6.1" idna==3.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") imagesize==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" importlib-metadata==4.11.3; python_version < "3.10" and python_version >= "3.7" inflection==0.5.1; python_version >= "3.6" iniconfig==1.1.1; python_version >= "3.7" -interface-meta==1.2.4; python_version >= "3.7" +interface-meta==1.2.5; python_version >= "3.7" investpy==1.0.8; python_version >= "3.7" ipykernel==6.9.2; python_version >= "3.7" ipympl==0.8.8 ipython-genutils==0.2.0; python_version >= "3.6" ipython==8.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" -ipywidgets==7.6.5 +ipywidgets==7.7.0 iso8601==0.1.16 isort==5.10.1; python_full_version >= "3.6.2" and python_version < "4.0" jedi==0.18.1; python_full_version >= "3.6.2" and python_version >= "3.8" -jinja2==3.0.3; python_version >= "3.7" -jmespath==0.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6" +jinja2==3.1.0; python_version >= "3.7" joblib==1.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" json5==0.9.6; python_version >= "3.7" jsonschema==3.2.0 jupyter-client==7.1.2; python_full_version >= "3.7.0" and python_version >= "3.7" jupyter-console==6.4.3; python_version >= "3.6" jupyter-core==4.9.2; python_full_version >= "3.7.0" and python_version >= "3.7" -jupyter-server==1.15.4; python_version >= "3.7" +jupyter-server==1.15.6; python_version >= "3.7" jupyter==1.0.0 jupyterlab-pygments==0.1.2; python_version >= "3.7" -jupyterlab-server==2.10.3; python_version >= "3.7" -jupyterlab-widgets==1.0.2; python_version >= "3.6" +jupyterlab-server==2.11.2; python_version >= "3.7" +jupyterlab-widgets==1.1.0; python_version >= "3.6" jupyterlab==3.3.2; python_version >= "3.7" keras-preprocessing==1.1.2 keras==2.8.0 @@ -144,7 +141,7 @@ multitasking==0.0.10 mypy-extensions==0.4.3; python_version >= "3.7" and python_full_version >= "3.6.2" mypy==0.930; python_version >= "3.6" myst-parser==0.15.2; python_version >= "3.6" -nbclassic==0.3.6; python_version >= "3.7" +nbclassic==0.3.7; python_version >= "3.7" nbclient==0.5.13; python_full_version >= "3.7.0" and python_version >= "3.7" nbconvert==6.4.4; python_version >= "3.7" nbformat==5.2.0; python_full_version >= "3.7.0" and python_version >= "3.7" @@ -209,7 +206,7 @@ pyobjc-framework-cocoa==8.4.1; python_version >= "3.6" and sys_platform == "darw pyopenssl==22.0.0; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" pyotp==2.6.0; python_version >= "3" pyparsing==3.0.7; python_version >= "3.7" -pyportfolioopt==1.5.1; python_full_version >= "3.6.1" and python_full_version < "4.0.0" +pyportfolioopt==1.5.2; python_full_version >= "3.6.1" and python_full_version < "4.0.0" pyprind==2.11.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" pyrsistent==0.18.1; python_version >= "3.7" pysocks==1.7.1; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" @@ -219,11 +216,11 @@ pytest-recording==0.12.0; python_version >= "3.5" pytest==6.2.5; python_version >= "3.6" python-binance==1.0.15 python-coinmarketcap==0.2 -python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") +python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") python-dotenv==0.19.2; python_version >= "3.5" pytrends==4.8.0 pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7") +pytz==2022.1; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7") pyupgrade==2.31.1; python_version >= "3.7" pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" and python_full_version >= "3.7.0" pywinpty==2.0.5; os_name == "nt" and python_version >= "3.7" @@ -241,7 +238,6 @@ requests==2.27.1; (python_version >= "2.7" and python_full_version < "3.0.0") or rich==10.16.2; python_full_version >= "3.6.2" and python_full_version < "4.0.0" robin-stocks==2.1.0; python_version >= "3" rsa==4.8; python_version >= "3.6" and python_version < "4" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") -s3transfer==0.5.2; python_version >= "3.6" scikit-learn==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") screeninfo==0.6.7 @@ -291,36 +287,36 @@ toml==0.10.2; python_full_version >= "3.6.2" and python_version >= "3.6" and (py tomli==2.0.1; python_version >= "3.7" and python_full_version >= "3.6.2" toolz==0.11.2; python_version >= "3.7" and python_full_version >= "3.7.0" tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7" -tqdm==4.63.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" +tqdm==4.63.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" tradingview-ta==3.2.10; python_version >= "3.6" traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8" trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0" trio==0.20.0; python_version >= "3.7" and python_version < "4.0" -types-python-dateutil==2.8.9 -types-pytz==2021.3.5 -types-pyyaml==6.0.4 -types-requests==2.27.12 -types-setuptools==57.4.10 +types-python-dateutil==2.8.10 +types-pytz==2021.3.6 +types-pyyaml==6.0.5 +types-requests==2.27.14 +types-setuptools==57.4.11 types-urllib3==1.26.11 typing-extensions==4.1.1; python_version < "3.10" and python_full_version >= "3.6.2" and python_version >= "3.6" -tzdata==2021.5; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") +tzdata==2022.1; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") tzlocal==4.1; python_version >= "3.6" ujson==5.1.0; python_version >= "3.7" unidecode==1.3.4; python_version >= "3.7" update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0" -urllib3==1.26.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.6") +urllib3==1.26.9; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") user-agent==0.1.10 vadersentiment==3.3.2 valinvest==0.0.2; python_version >= "3.6" vcrpy==4.1.1; python_version >= "3.5" -virtualenv==20.13.3; python_full_version >= "3.6.1" -voila==0.3.3; python_version >= "3.7" +virtualenv==20.13.4; python_full_version >= "3.6.1" +voila==0.3.4; python_version >= "3.7" wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" webencodings==0.5.1; python_version >= "3.7" websocket-client==1.3.1; python_version >= "3.8" and python_version < "4.0" websockets==9.1; python_full_version >= "3.6.1" and python_version >= "3.7" werkzeug==2.0.3; python_version >= "3.6" -widgetsnbextension==3.5.2 +widgetsnbextension==3.6.0 wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") wsproto==1.1.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" yarl==1.7.2; python_version >= "3.6" diff --git a/requirements.txt b/requirements.txt index e0b67aaed83..a6325817a21 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,40 +9,32 @@ argon2-cffi-bindings==21.2.0; python_version >= "3.7" argon2-cffi==21.3.0; python_version >= "3.7" ascii-magic==1.6; python_version >= "3.5" astor==0.8.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -astroid==2.9.3; python_full_version >= "3.6.2" asttokens==2.0.5; python_full_version >= "3.6.2" and python_version >= "3.8" async-generator==1.10; python_version >= "3.7" and python_version < "4.0" async-timeout==4.0.2; python_version >= "3.6" -atomicwrites==1.4.0; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.4.0" attrs==21.4.0; python_full_version >= "3.7.0" and python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.5.0") babel==2.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" backcall==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8" backports.zoneinfo==0.2.1; python_version < "3.9" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") -bandit==1.7.4; python_version >= "3.7" beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.6.0" beautifulsoup4==4.10.0; python_full_version > "3.0.0" and python_version >= "3.7" black==22.1.0; python_full_version >= "3.6.2" bleach==4.1.0; python_version >= "3.7" -boto3==1.21.19; python_version >= "3.6" -botocore==1.24.19; python_version >= "3.6" bs4==0.0.1 bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") certifi==2021.10.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") cffi==1.15.0; python_version >= "3.7" -cfgv==3.3.1; python_full_version >= "3.6.1" charset-normalizer==2.0.12; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") click==8.0.4; python_version >= "3.6" and python_full_version >= "3.6.2" -codespell==2.1.0; python_version >= "3.5" -colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and platform_system == "Windows" and python_version >= "3.8" +colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and platform_system == "Windows" and python_version >= "3.8" and sys_platform == "win32" commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" convertdate==2.4.0; python_version >= "3.7" and python_version < "4" -coverage==6.3.2; python_version >= "3.7" -cryptography==36.0.1; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" +cryptography==36.0.2; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" cssselect==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" cvxpy==1.2.0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" cycler==0.11.0; python_version >= "3.7" cython==0.29.28; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "darwin" or python_full_version >= "3.3.0" and sys_platform == "darwin" and python_version >= "3.7" -dateparser==1.1.0; python_version >= "3.5" +dateparser==1.1.1; python_version >= "3.5" datetime==4.4; python_version >= "3.5" debugpy==1.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" decorator==5.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") @@ -50,24 +42,20 @@ defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or degiro-connector==2.0.18; python_full_version >= "3.7.1" and python_full_version < "4.0.0" deprecation==2.1.0 detecta==0.0.5; python_version >= "3.6" -distlib==0.3.4; python_full_version >= "3.6.1" dnspython==2.2.1; python_version >= "3.6" and python_version < "4.0" docutils==0.16; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" ecos==2.0.10; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" entrypoints==0.4; python_full_version >= "3.7.0" and python_version >= "3.7" et-xmlfile==1.1.0; python_version >= "3.6" -exchange-calendars==3.6.1; python_version >= "3.7" and python_full_version >= "3.7.0" executing==0.8.3; python_full_version >= "3.6.2" and python_version >= "3.8" fear-greed-index==0.1.4; python_version >= "3.7" ffn==0.3.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" -filelock==3.6.0; python_version >= "3.7" and python_full_version >= "3.6.1" financedatabase==1.0.2 finnhub-python==2.4.11 finviz==1.4.4 finvizfinance==0.12.2; python_version >= "3.5" -flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") -fonttools==4.30.0; python_version >= "3.7" -formulaic==0.3.1; python_version >= "3.7" +fonttools==4.31.2; python_version >= "3.7" +formulaic==0.3.2; python_version >= "3.7" fred==3.1 fredapi==0.4.3 frozendict==2.3.0; python_version >= "3.6" @@ -76,66 +64,55 @@ fundamentalanalysis==0.2.12 future==0.18.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" gitdb==4.0.9; python_version >= "3.7" gitpython==3.1.27; python_version >= "3.7" -grpcio==1.44.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6" +grpcio==1.45.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6" h11==0.13.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" hijri-converter==2.2.3; python_version >= "3.6" holidays==0.11.3.1; python_version >= "3.6" -identify==2.4.11; python_version >= "3.7" and python_full_version >= "3.6.1" idna==3.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") imagesize==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" inflection==0.5.1; python_version >= "3.6" -iniconfig==1.1.1; python_version >= "3.7" -interface-meta==1.2.4; python_version >= "3.7" +interface-meta==1.2.5; python_version >= "3.7" investpy==1.0.8; python_version >= "3.7" ipykernel==6.9.2; python_version >= "3.7" ipympl==0.8.8 ipython-genutils==0.2.0; python_version >= "3.6" ipython==8.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" -ipywidgets==7.6.5 +ipywidgets==7.7.0 iso8601==0.1.16 -isort==5.10.1; python_full_version >= "3.6.2" and python_version < "4.0" jedi==0.18.1; python_full_version >= "3.6.2" and python_version >= "3.8" -jinja2==3.0.3; python_version >= "3.7" -jmespath==0.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6" +jinja2==3.1.0; python_version >= "3.7" joblib==1.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" json5==0.9.6; python_version >= "3.7" jsonschema==3.2.0 jupyter-client==7.1.2; python_full_version >= "3.7.0" and python_version >= "3.7" jupyter-console==6.4.3; python_version >= "3.6" jupyter-core==4.9.2; python_full_version >= "3.7.0" and python_version >= "3.7" -jupyter-server==1.15.4; python_version >= "3.7" +jupyter-server==1.15.6; python_version >= "3.7" jupyter==1.0.0 jupyterlab-pygments==0.1.2; python_version >= "3.7" -jupyterlab-server==2.10.3; python_version >= "3.7" -jupyterlab-widgets==1.0.2; python_version >= "3.6" +jupyterlab-server==2.11.2; python_version >= "3.7" +jupyterlab-widgets==1.1.0; python_version >= "3.6" jupyterlab==3.3.2; python_version >= "3.7" kiwisolver==1.4.0; python_version >= "3.7" -korean-lunar-calendar==0.2.1; python_version >= "3.7" and python_full_version >= "3.7.0" -lazy-object-proxy==1.7.1; python_version >= "3.6" and python_full_version >= "3.6.2" +korean-lunar-calendar==0.2.1; python_version >= "3.6" linearmodels==4.25; python_version >= "3.7" lxml==4.8.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" m2r2==0.2.8; python_version >= "3.7" -markdown-it-py==1.1.0; python_version >= "3.6" and python_version < "4.0" markupsafe==2.1.1; python_version >= "3.7" matplotlib-inline==0.1.3; python_full_version >= "3.6.2" and python_version >= "3.8" matplotlib==3.5.1; python_version >= "3.7" -mccabe==0.6.1; python_full_version >= "3.6.2" -mdit-py-plugins==0.2.8; python_version >= "3.6" and python_version < "4.0" mistune==0.8.4; python_version >= "3.7" -mock==4.0.3; python_version >= "3.6" more-itertools==8.12.0; python_version >= "3.6" mplfinance==0.12.8b9 multidict==6.0.2; python_version >= "3.7" multitasking==0.0.10 mypy-extensions==0.4.3; python_version >= "3.7" and python_full_version >= "3.6.2" mypy==0.930; python_version >= "3.6" -myst-parser==0.15.2; python_version >= "3.6" -nbclassic==0.3.6; python_version >= "3.7" +nbclassic==0.3.7; python_version >= "3.7" nbclient==0.5.13; python_full_version >= "3.7.0" and python_version >= "3.7" nbconvert==6.4.4; python_version >= "3.7" nbformat==5.2.0; python_full_version >= "3.7.0" and python_version >= "3.7" nest-asyncio==1.5.4; python_full_version >= "3.7.0" and python_version >= "3.7" -nodeenv==1.6.0; python_full_version >= "3.6.1" notebook-shim==0.1.0; python_version >= "3.7" notebook==6.4.10; python_version >= "3.7" numpy==1.21.2; python_version >= "3.7" and python_version < "3.11" @@ -147,7 +124,6 @@ osqp==0.6.2.post5; python_full_version >= "3.6.1" and python_full_version < "4.0 outcome==1.1.0; python_version >= "3.7" and python_version < "4.0" packaging==21.3; python_version >= "3.7" pandas-datareader==0.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -pandas-market-calendars==3.2; python_full_version >= "3.7.0" pandas-ta==0.3.14b pandas==1.3.5; python_full_version >= "3.7.1" pandocfilters==1.5.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" @@ -155,17 +131,14 @@ papermill==2.3.4; python_version >= "3.6" parso==0.8.3; python_full_version >= "3.6.2" and python_version >= "3.8" pathspec==0.9.0; python_full_version >= "3.6.2" patsy==0.5.2; python_version >= "3.7" -pbr==5.8.1; python_version >= "3.7" pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" and python_full_version >= "3.6.2" pickleshare==0.7.5; python_full_version >= "3.6.2" and python_version >= "3.8" pillow==9.0.1; python_version >= "3.7" and python_version < "4" platformdirs==2.5.1; python_version >= "3.7" and python_full_version >= "3.6.2" plotly==5.6.0; python_version >= "3.6" -pluggy==1.0.0; python_version >= "3.7" pmdarima==1.8.5; python_version >= "3.7" praw==7.5.0; python_version >= "3.6" and python_version < "4.0" prawcore==2.3.0; python_version >= "3.6" and python_version < "4.0" -pre-commit==2.17.0; python_full_version >= "3.6.1" prometheus-client==0.13.1; python_version >= "3.7" prompt-toolkit==3.0.28; python_full_version >= "3.6.2" property-cached==1.6.4; python_version >= "3.7" @@ -174,17 +147,13 @@ psaw==0.0.12; python_version >= "3" psutil==5.9.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" and python_full_version >= "3.6.2" pure-eval==0.2.2; python_full_version >= "3.6.2" and python_version >= "3.8" -py==1.11.0; python_full_version >= "3.7.0" and python_version >= "3.7" and implementation_name == "pypy" +py==1.11.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.7.0" pyally==1.1.2 -pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" pycoingecko==2.2.0 pycparser==2.21; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" pyex==0.5.0 -pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" pygments==2.11.2; python_version >= "3.5" pyhdfe==0.1.0; python_version >= "3.7" -pylint==2.12.2; python_full_version >= "3.6.2" -pyluach==1.4.0; python_version >= "3.7" and python_full_version >= "3.7.0" pymeeus==0.5.11; python_version >= "3.7" and python_version < "4" pymongo==3.11.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0") pyobjc-core==8.4.1; python_version >= "3.6" and sys_platform == "darwin" @@ -192,25 +161,20 @@ pyobjc-framework-cocoa==8.4.1; python_version >= "3.6" and sys_platform == "darw pyopenssl==22.0.0; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" pyotp==2.6.0; python_version >= "3" pyparsing==3.0.7; python_version >= "3.7" -pyportfolioopt==1.5.1; python_full_version >= "3.6.1" and python_full_version < "4.0.0" +pyportfolioopt==1.5.2; python_full_version >= "3.6.1" and python_full_version < "4.0.0" pyprind==2.11.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" pyrsistent==0.18.1; python_version >= "3.7" pysocks==1.7.1; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" -pytest-cov==3.0.0; python_version >= "3.6" -pytest-mock==3.7.0; python_version >= "3.7" -pytest-recording==0.12.0; python_version >= "3.5" -pytest==6.2.5; python_version >= "3.6" python-binance==1.0.15 python-coinmarketcap==0.2 -python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") +python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") python-dotenv==0.19.2; python_version >= "3.5" pytrends==4.8.0 pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7") -pyupgrade==2.31.1; python_version >= "3.7" +pytz==2022.1; python_full_version >= "3.7.1" and python_version >= "3.7" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7") pywin32==303; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" and python_full_version >= "3.7.0" pywinpty==2.0.5; os_name == "nt" and python_version >= "3.7" -pyyaml==6.0; python_version >= "3.7" and python_full_version >= "3.6.1" +pyyaml==6.0; python_version >= "3.6" pyzmq==22.3.0; python_full_version >= "3.7.0" and python_version >= "3.7" qdldl==0.1.5.post0; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and python_version >= "3.7" qtconsole==5.2.2; python_version >= "3.6" @@ -223,7 +187,6 @@ requests-oauthlib==1.3.1; python_version >= "2.7" and python_full_version < "3.0 requests==2.27.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") rich==10.16.2; python_full_version >= "3.6.2" and python_full_version < "4.0.0" robin-stocks==2.1.0; python_version >= "3" -s3transfer==0.5.2; python_version >= "3.6" scikit-learn==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" scipy==1.8.0; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.6.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") screeninfo==0.6.7 @@ -233,7 +196,7 @@ selenium==4.1.3; python_version >= "3.7" and python_version < "4.0" send2trash==1.8.0; python_version >= "3.7" sentiment-investor==2.1.0; python_version >= "3.8" and python_version < "4.0" setuptools-scm==6.4.2; python_version >= "3.7" -six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") +six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") smmap==5.0.0; python_version >= "3.7" sniffio==1.2.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.2" snowballstemmer==2.2.0; python_version >= "3.7" @@ -252,7 +215,6 @@ squarify==0.4.3 sseclient==0.0.27 stack-data==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8" statsmodels==0.13.2; python_version >= "3.7" -stevedore==3.5.0; python_version >= "3.7" tabulate==0.8.9; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" temporal-cache==0.1.4 tenacity==8.0.1; python_version >= "3.6" @@ -261,41 +223,30 @@ testpath==0.6.0; python_version >= "3.7" textwrap3==0.9.2; python_version >= "3.6" thepassiveinvestor==1.0.10 threadpoolctl==3.1.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" -tokenize-rt==4.2.1; python_full_version >= "3.6.2" and python_version >= "3.7" -toml==0.10.2; python_full_version >= "3.6.2" and python_version >= "3.6" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") tomli==2.0.1; python_version >= "3.7" and python_full_version >= "3.6.2" -toolz==0.11.2; python_version >= "3.7" and python_full_version >= "3.7.0" tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7" -tqdm==4.63.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" +tqdm==4.63.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" tradingview-ta==3.2.10; python_version >= "3.6" traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8" trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0" trio==0.20.0; python_version >= "3.7" and python_version < "4.0" -types-python-dateutil==2.8.9 -types-pytz==2021.3.5 -types-pyyaml==6.0.4 -types-requests==2.27.12 -types-setuptools==57.4.10 -types-urllib3==1.26.11 typing-extensions==4.1.1; python_version < "3.10" and python_full_version >= "3.6.2" and python_version >= "3.6" -tzdata==2021.5; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") +tzdata==2022.1; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") tzlocal==4.1; python_version >= "3.6" ujson==5.1.0; python_version >= "3.7" unidecode==1.3.4; python_version >= "3.7" update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0" -urllib3==1.26.8; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "3.6") +urllib3==1.26.9; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") user-agent==0.1.10 vadersentiment==3.3.2 valinvest==0.0.2; python_version >= "3.6" -vcrpy==4.1.1; python_version >= "3.5" -virtualenv==20.13.3; python_full_version >= "3.6.1" -voila==0.3.3; python_version >= "3.7" +voila==0.3.4; python_version >= "3.7" wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" webencodings==0.5.1; python_version >= "3.7" websocket-client==1.3.1; python_version >= "3.8" and python_version < "4.0" websockets==9.1; python_full_version >= "3.6.1" and python_version >= "3.7" -widgetsnbextension==3.5.2 -wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") +widgetsnbextension==3.6.0 +wrapt==1.13.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") wsproto==1.1.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" yarl==1.7.2; python_version >= "3.6" yfinance==0.1.70 diff --git a/scripts/test_forex_load.gst b/scripts/test_forex_load.gst new file mode 100644 index 00000000000..5293bf4482a --- /dev/null +++ b/scripts/test_forex_load.gst @@ -0,0 +1,12 @@ +forex +to gbp +load +load --source av +load --source av -s 2021-01-02 +load --source yf -s 2021-01-02 +load -s 2022-01-20 +load -s 2022-02-20 +load -i 1week -s 2021-01-20 +load -r w --source av +load -r w --source av -s 2021-01-20 +exit diff --git a/scripts/test_forex_ta.gst b/scripts/test_forex_ta.gst new file mode 100644 index 00000000000..398d6fff01b --- /dev/null +++ b/scripts/test_forex_ta.gst @@ -0,0 +1,20 @@ +forex +from EUR +to USD +load +ta +ema +sma +zlma +cci +macd +rsi +stoch +fisher +cg +adx +aroon +bbands +donchian +fib +exit diff --git a/styles/default/light.mplstyle b/styles/default/light.mplstyle index 419801b7d7b..807f6caa752 100644 --- a/styles/default/light.mplstyle +++ b/styles/default/light.mplstyle @@ -45,7 +45,7 @@ axes.labelcolor: 0F0F0F axes.grid: true axes.grid.which: major -axes.prop_cycle: cycler('color', ['254495', '48277c', '822661', 'c13246', 'e4003a', 'ef7d00', 'ffed00', '00aaff', '9b30d9', 'af005f', '5f00af', 'af87ff']) +axes.prop_cycle: cycler('color', ['254495', 'c13246', '48277c', 'e4003a', 'ef7d00', '822661', 'ffed00', '00aaff', '9b30d9', 'af005f', '5f00af', 'af87ff']) xtick.color: 0F0F0F ytick.color: 0F0F0F diff --git a/terminal.py b/terminal.py index 316e9177b06..d44fd3c56ad 100644 --- a/terminal.py +++ b/terminal.py @@ -24,7 +24,7 @@ from gamestonk_terminal.helper_funcs import ( replace_user_timezone, set_export_folder, ) -from gamestonk_terminal.loggers import setup_logging, upload_archive_logs_s3 +from gamestonk_terminal.loggers import setup_logging from gamestonk_terminal.menu import session from gamestonk_terminal.parent_classes import BaseController from gamestonk_terminal.rich_config import console @@ -441,8 +441,6 @@ def terminal(jobs_cmds: List[str] = None, appName: str = "gst"): """Terminal Menu""" setup_logging(appName) logger.info("START") - logger.info("Python: %s", platform.python_version()) - logger.info("OS: %s", platform.system()) log_settings() if jobs_cmds is not None and jobs_cmds: @@ -502,7 +500,6 @@ def terminal(jobs_cmds: List[str] = None, appName: str = "gst"): # If the command is quitting the menu we want to return in here if t_controller.queue[0] in ("q", "..", "quit"): print_goodbye() - upload_archive_logs_s3(log_filter=r"gst_") break if gtff.ENABLE_EXIT_AUTO_HELP and len(t_controller.queue) > 1: @@ -528,7 +525,6 @@ def terminal(jobs_cmds: List[str] = None, appName: str = "gst"): ) except KeyboardInterrupt: print_goodbye() - upload_archive_logs_s3(log_filter=r"gst_") break # Get input from user without auto-completion else: @@ -539,7 +535,6 @@ def terminal(jobs_cmds: List[str] = None, appName: str = "gst"): t_controller.queue = t_controller.switch(an_input) if an_input in ("q", "quit", "..", "exit"): print_goodbye() - upload_archive_logs_s3(log_filter=r"gst_") break # Check if the user wants to reset application @@ -547,7 +542,6 @@ def terminal(jobs_cmds: List[str] = None, appName: str = "gst"): ret_code = reset(t_controller.queue if t_controller.queue else []) if ret_code != 0: print_goodbye() - upload_archive_logs_s3(log_filter=r"gst_") break except SystemExit: @@ -595,33 +589,24 @@ def insert_start_slash(cmds: List[str]) -> List[str]: def log_settings() -> None: """Log settings""" settings_dict = {} - settings_dict["tab"] = "activated" if gtff.USE_TABULATE_DF else "deactivated" - settings_dict["cls"] = "activated" if gtff.USE_CLEAR_AFTER_CMD else "deactivated" - settings_dict["color"] = "activated" if gtff.USE_COLOR else "deactivated" - settings_dict["promptkit"] = ( - "activated" if gtff.USE_PROMPT_TOOLKIT else "deactivated" - ) - settings_dict["predict"] = "activated" if gtff.ENABLE_PREDICT else "deactivated" - settings_dict["thoughts"] = ( - "activated" if gtff.ENABLE_THOUGHTS_DAY else "deactivated" - ) - settings_dict["reporthtml"] = ( - "activated" if gtff.OPEN_REPORT_AS_HTML else "deactivated" - ) - settings_dict["exithelp"] = ( - "activated" if gtff.ENABLE_EXIT_AUTO_HELP else "deactivated" - ) - settings_dict["rcontext"] = "activated" if gtff.REMEMBER_CONTEXTS else "deactivated" - settings_dict["rich"] = "activated" if gtff.ENABLE_RICH else "deactivated" - settings_dict["richpanel"] = ( - "activated" if gtff.ENABLE_RICH_PANEL else "deactivated" - ) - settings_dict["ion"] = "activated" if gtff.USE_ION else "deactivated" - settings_dict["watermark"] = "activated" if gtff.USE_WATERMARK else "deactivated" - settings_dict["autoscaling"] = ( - "activated" if gtff.USE_PLOT_AUTOSCALING else "deactivated" - ) - settings_dict["dt"] = "activated" if gtff.USE_DATETIME else "deactivated" + settings_dict["tab"] = "True" if gtff.USE_TABULATE_DF else "False" + settings_dict["cls"] = "True" if gtff.USE_CLEAR_AFTER_CMD else "False" + settings_dict["color"] = "True" if gtff.USE_COLOR else "False" + settings_dict["promptkit"] = "True" if gtff.USE_PROMPT_TOOLKIT else "False" + settings_dict["predict"] = "True" if gtff.ENABLE_PREDICT else "False" + settings_dict["thoughts"] = "True" if gtff.ENABLE_THOUGHTS_DAY else "False" + settings_dict["reporthtml"] = "True" if gtff.OPEN_REPORT_AS_HTML else "False" + settings_dict["exithelp"] = "True" if gtff.ENABLE_EXIT_AUTO_HELP else "False" + settings_dict["rcontext"] = "True" if gtff.REMEMBER_CONTEXTS else "False" + settings_dict["rich"] = "True" if gtff.ENABLE_RICH else "False" + settings_dict["richpanel"] = "True" if gtff.ENABLE_RICH_PANEL else "False" + settings_dict["ion"] = "True" if gtff.USE_ION else "False" + settings_dict["watermark"] = "True" if gtff.USE_WATERMARK else "False" + settings_dict["autoscaling"] = "True" if gtff.USE_PLOT_AUTOSCALING else "False" + settings_dict["dt"] = "True" if gtff.USE_DATETIME else "False" + settings_dict["python"] = str(platform.python_version()) + settings_dict["os"] = str(platform.system()) + logger.info("SETTINGS: %s ", str(settings_dict)) diff --git a/tests/README.md b/tests/README.md index 80ea3682ee3..b0106f5b4cd 100644 --- a/tests/README.md +++ b/tests/README.md @@ -418,7 +418,7 @@ After updating the `pyproject.toml` you will have to export the `requirements` f ```bash poetry export -f requirements.txt -o requirements.txt --without-hashes --dev poetry export -f requirements.txt -o requirements-full.txt --extras prediction --without-hashes --dev -poetry export -f requirements.txt -o discordbot/requirements.txt --extras discord --without-hashes --dev +poetry export -f requirements.txt -o bots/requirements.txt --extras bots --without-hashes --dev ``` # 4. Maintain `unit tests` diff --git a/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_model/test_get_screener_data.yaml b/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_model/test_get_screener_data.yaml index 27e025a2bf5..739506eac83 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_model/test_get_screener_data.yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_model/test_get_screener_data.yaml @@ -13,29 +13,29 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA+yXTY/bNhCG7/kVhIEUCVBOOfwUdWvXmyZBFyjWu4ccCYephdpSIXELBEH+eyF7 - 0zUpiSb2lEOunrE4fJ/54pcXhBCyuvHBkbULblWTL8efjj+/az91/cGFpmtXNVndb8jGb0PXkz99 - f7S0W09e9d7tQ3Pw5Ceya4bQ9c3W7V+vfn76zh9uCOTWf+r9sPMfx09xxjllgqIhTNZK1kqR+80v - 124Ivm9Xx79+PX1hdevav8mvNbn1bk/vxoPOjo8Dvura4eHge7Juhm3vx8Bd/3k8UIBQL89jOrsb - ufPbXdvtu7++ufLI9ao7HB7aZnty3vj+32brh+M1wIrI9U3TunbbuP2juaoi840Lvv9mRVBVEtLH - 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0i49edl7tw7NxpMXZNUMoeubpVu/mr1+WOezGwK58N96P6z813EpwYSgDKnghJmaYa04uVq8OXVD + 8H072776Z7fC7MK1P8jbmlx4t6aX44cefT65YXLpl6u2W3ffb7cfBG6PH+/ppGuHm43vybwZlr0f + X3H9fWoVpW42N22z3K278P2vZumHMZWDlJPUj6MWK3Li+nFnMwZaTeLnLvi+cevhLsom0bP2680Q + HsXlNP6+aV27fAiLREmL4H6u/X2SmSSNKpLTIbhwt0PBJ/HT1vc7yRiw6fpXoVk3odktTBlYdryH + 1LuacDJ3t/8p/cuUTmqi8pTS0tt4t2mgBiqTEccAz5LRIGQhZAXGpiGquFsiiAqYOEgJoRI5SgJ0 + BgUDJRIMKYKq9lGaj2fpvGvDKs3pYRkDVZVXU5r0/jQg5qqTUOmchBjXNwEgngJI9QyLZU53IgNd + agJPUqedSBkYPNgBVIDU+0Cd1gTLQaEGmzsWPLbBCSgeH5pDhhS1AWWg5eFSGVie5kk5GJ5rF6pA + YSFGqsHqwu6gBlSpJ1DOt6U+5fW+Jtfe9TR0dO6CJy+vL+evitAJkCLfR1Jn/JjHHhHBowjMZOk9 + SdhLD8FmTiNVgDpLzwLKUnqcgSnGxxlUtpgfAlf7+H0YjXEkWMJMyvg8xcbNYjki0bkFVIUVcg2s + OgiI4/ZiTp5xjCWNHAJBZmYWC6IqpKdB6PzNGG0kg0uCFvtofRzdMU8roycX8fyWqUbGoKZjgAKR + 9UZpQZWWKzUonmEkdTy1xTdmBVYdnjo0IMt1LyIYmW4lwYHJxODBVeI+OxvHw+cSE4wD04XEONOg + bI5JhcBNmqlG0LnLVFlgmO1wBJ2dfJQAmxEYDaAtwShVYWehhAoTyCyg2UfsU004ezYyqVQschoZ + MhtLHiETBkHmzoZg6oDvcL1zkxR2Lqp4QNnrswJBYsZomdgKWsTFSJA2+R/2wOXoz18AAAD//wMA + APQvX1gRAAA= headers: Allow: - GET, HEAD, OPTIONS CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed481b67f8c0b88-AMS + - 6ef63413a9db1e91-AMS Connection: - keep-alive Content-Encoding: @@ -43,7 +43,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 17 Mar 2022 08:54:55 GMT + - Mon, 21 Mar 2022 11:03:51 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" Server: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml b/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml index 6115a13205b..f839118f75e 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml @@ -13,29 +13,29 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA+yXTY/bNhCG7/kVhIEUCVBOOfwUdWvXmyZBFyjWu4ccCYephdpSIXELBEH+eyF7 - 0zUpiSb2lEOunrE4fJ/54pcXhBCyuvHBkbULblWTL8efjj+/az91/cGFpmtXNVndb8jGb0PXkz99 - f7S0W09e9d7tQ3Pw5Ceya4bQ9c3W7V+vfn76zh9uCOTWf+r9sPMfx09xxjllgqIhTNZK1kqT+80v - 124Ivm9Xx79+PX1hdevav8mvNbn1bk/vxoPOjo8Dvura4eHge7Juhm3vx8Bd/3k8UIBQL89jOrsb - ufPbXdvtu7++ufLI9ao7HB7aZnty3vj+32brh+M1wIrI9U3TunbbuP2juaoi840Lvv9mRVBVEtLH - hyGc2ZM43o5C78iV6/3JzmP7KBC5HoILj3YUyT0e1dkE98/+dAOWOt2HZt+E5mSlDNBE5uvW9yeZ - KAMpXs6g+q0mSNbu8w9M3zWmq5qo52KSIFhGUAFW5iQRwEROUg7c5IBYu6zFBMdyBiGgLFC+koVp - xkCLHGkGii9RklCZOUrrsZhuujbsljlF9zfA9MIhEiqVw8LTTE/i5yBtDhsDkcFGESS/KDflabnE - qUVlGuQyDyrB6MJcoArQzkc3yX6qQLE5WNc1EZdgPeEQDJAvJzIDJXNCIEgskbPKMqP8Qq1OHWKq - EqTMJQ3VYFmpsDjTGRaJoQBenAoowdg5Zm9q8sG7noaOrl3w5NWHu/XrEnzcpJ0h7skIXOX5WZ0l - I8FcHgyjV7aoqUoLKuZnwGTHE0WWnpBnIstpS+C6GKA6zrgpwN/HDjkiLKo5nVZVOo2rNKnSca4B - q9IBI6bNaooQJ+tJPNA4qNz2gggclwFrUNkGIEBjITCezrcMLgVmltbbsUXmaWX0ZBqkKYxWmbTx - RLooBJFtfFIDzzVgKYFhoRxSALIsBQ3KXMwUodI1MMlegaDNcirxyS77VBmIR2mnxN6Ne+KziVkB - UhcSs9UxaZaRGA1KZ5BaEDZDTCmQ2VVeYTqRE30VS9tbpK8w6V5cRnE5ccRk2Y3WudmJ9r4myJ5N - TAo7LfPFjmB5+vJId0ot09dV8vhimLbk9IGlbDpp4/cAF+konW+zmBZ03GcZB1O66huWbl9nuzyC - /b+SXnz9DwAA//8DAAkCAUVjEQAA + H4sIAAAAAAAAA+yXTW/UMBCG7/0V1kpFIOHB9vgjzg26BYqohLrtoUdrMWzEboISF6lC/HeU3VZt + 3LXX6okD18zE8bzP+PXk9xEhhMzOfXBk7oKb1eT39tH28Vn7res3LjRdO6vJ7GpBFn4Zup588f02 + 0i49edl7tw7NxpMXZNUMoeubpVu/mr1+WOezGwK58N96P6z813EpwYSgDKnghJmaYa0EuVq8OXVD + 8H072776Z7fC7MK1P8jbmlx4t6aX44cefT65YXLpl6u2W3ffb7cfBG6PH+/ppGuHm43vybwZlr0f + X3H9fWoVpW42N22z3K278P2vZumHMZWDlJPUj6MWK3Li+nFnMwZaTeLnLvi+cevhLsom0bP2680Q + HsXlNP6+aV27fAiLREmL4H6u/X2SmSSNKpLTIbhwt0PBJ/HT1vc7yRiw6fpXoVk3odktTBlYdryH + 1LuacDJ3t/8p/cuUTmqi8pTS0tt4t2mgBiqTEccAz5LRIGQhZAXGpiGquFsiiAqYOEgJoRI5SgJ0 + BgUDJRIMKYKq9lGaj2fpvGvDKs3pYRkDVZVXU5r0/jQg5qqTUOmchBjXNwEgngJI9QyLZU5DZ6Cr + 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a/tests/gamestonk_terminal/economy/cassettes/test_cnn_model/test_get_feargreed_report.yaml b/tests/gamestonk_terminal/economy/cassettes/test_cnn_model/test_get_feargreed_report.yaml index b5a75ade6ff..1a7ec11284d 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_cnn_model/test_get_feargreed_report.yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_cnn_model/test_get_feargreed_report.yaml @@ -9,459 +9,460 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:82.1) Gecko/20100101 Firefox/82.1 + - Mozilla/5.0 (Windows NT 6.1; WOW64; rv:84.0) Gecko/20100101 Firefox/84.0 method: GET uri: https://money.cnn.com/data/fear-and-greed/ response: body: string: !!binary | - H4sIAAAAAAAAA+2963YbObIm+lu1Vr8DmtVtyrucTFLU3aZqZEkuq9uWvUuyq7trarSSZJJKi2Sy - mElJtMvz6zzJeY3z7zzKeZLzReCSyAuT1MVuz8y2qiQyEwgAgYhAIBCIePbnwzcHZ/98eyQu4uFA - vH33/NXxgag4rvtL88B1D88OxT9enr1+JRq1ujibeKMoiINw5A1c9+ikIioXcTzedd3r6+vadbMW - 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'{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=IXp62RvCS1PWUUl9b8A2WRdTWK%2FptPFczQ7XCow94Cuv4EKrvNQStC0W3DplhYAdLK5DMCyc%2BEmPpRbbYjtCnjHmsh%2FNnZY318J2V9p7CAezD4X1v3QgMhF%2BRCZcZeXe"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare + Transfer-Encoding: + - chunked Vary: - - Accept, Origin + - Accept-Encoding + - Origin X-Content-Type-Options: - nosniff X-Frame-Options: @@ -52,34 +78,80 @@ interactions: - request: body: null headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate Connection: - - close - Host: - - www.econdb.com + - keep-alive User-Agent: - - Python-urllib/3.8 + - python-requests/2.27.1 method: GET - uri: https://www.econdb.com/api/series/PPIFR/?format=csv + uri: https://www.econdb.com/series/context/?tickers=PPIFR response: body: - string: 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same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=YTUDxECKbD8dAUDsiGwINukCxL0jqw6S7qAjyvnGfGnP6ZVH3SrM5JU0hcd7D%2Bxxju8A%2BLTZbSJytO2x6GXhIIqqyP13mvdootKyU1CkiuzYyDXM%2BoJG7fqWWhR2NzVB"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=zuYW0D8xaa0vecdVTpQ%2FBRLQFfaTIBIQp%2BuFtc9islgpq6Y%2B8CDApWxXCFagS6M6WceId9j6ZNTyF0ljsXuIT8EVIXTL9Oqroo6Heu9Ile5CMHNdT9W4ldloEI1bYxjB"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare + Transfer-Encoding: + - chunked Vary: - - Accept, Origin + - Accept-Encoding + - Origin X-Content-Type-Options: - nosniff X-Frame-Options: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_macro_data[RGDP-United States-2005-05-05-2006-01-01-False].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_macro_data[RGDP-United States-2005-05-05-2006-01-01-False].yaml index fb08552a2e2..9f472f45e58 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_macro_data[RGDP-United States-2005-05-05-2006-01-01-False].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_macro_data[RGDP-United States-2005-05-05-2006-01-01-False].yaml @@ -2,34 +2,93 @@ interactions: - request: body: null headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate Connection: - - close - Host: - - www.econdb.com + - keep-alive User-Agent: - - Python-urllib/3.8 + - python-requests/2.27.1 method: GET - uri: https://www.econdb.com/api/series/RGDPUS/?format=csv + uri: https://www.econdb.com/series/context/?tickers=RGDPUS response: body: - string: 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GET, HEAD, OPTIONS CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed482135d1c1ffc-AMS + - 6ef63474381b978b-AMS Connection: - - close - Content-Disposition: - - attachment;filename="econdb_1647507312.csv" - Content-Length: - - '6126' + - keep-alive + Content-Encoding: + - gzip Content-Type: - - text/csv; charset=utf-8 + - application/json Date: - - Thu, 17 Mar 2022 08:55:12 GMT + - Mon, 21 Mar 2022 11:04:07 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -37,11 +96,14 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=2w9Y89uM16BowbKjQN304%2FTAGo5zhsuxg9u0yX4SFJ7KusQPPHt9Nb3I%2BFc5axbPrwWutkHN5ARX9ojrybQQ5fFqpKTJBSn37LP0TbQfEzVmbzcQhTBIWl7KFnjGHelc"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=MIlrRpiHTcIw9QJex18hOg%2BD5g3%2F%2FU3LMJFKeddoac%2Fd8o9me%2Fkg41%2Bm15%2BHtltAT3QCUqY6LrgXBI2B10VGOYwyGmBuSMeov66t2kSFXxuWKOGMARU4rzkWP122vy%2FB"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare + Transfer-Encoding: + - chunked Vary: - - Accept, Origin + - Accept-Encoding + - Origin X-Content-Type-Options: - nosniff X-Frame-Options: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-04-05].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-04-05].yaml index 6ddbce7d60f..decffc9d06a 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-04-05].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-04-05].yaml @@ -37,7 +37,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed48275be625977-AMS + - 6ef634864e9a0c65-AMS Connection: - keep-alive Content-Encoding: @@ -45,7 +45,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 17 Mar 2022 08:55:27 GMT + - Mon, 21 Mar 2022 11:04:10 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -53,7 +53,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=NerL9P7cH%2FDZCvEzarSItCUHnl4Mpm%2FAkoDOHQYO%2BhHE0bvRuibE0KqePTcSVCA%2FSCPu7TuON79x7DqyXgviF%2FRgkRWJWzgSacq4cHg6ZzV%2F9NiX0D%2FUgRNHK%2BazZgPg"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=UZ%2BNGEcvMUbngRfEkdFDu6pSPyYAeE2XXz%2BjkWeMj6X3DMC5dBh%2BV1i8ZvcM8q9fiGmunn67jjxhX8jC%2FMNWBDgNRR%2BvqWVwuFTvA73VQ6SnanVmWamL5%2B9FUdjzODKg"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: @@ -102,7 +102,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed4827acb0a0121-AMS + - 6ef6348b8baf976d-AMS Connection: - keep-alive Content-Encoding: @@ -110,7 +110,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 17 Mar 2022 08:55:27 GMT + - Mon, 21 Mar 2022 11:04:11 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -118,7 +118,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=15F%2FNMZ7nyLBpyV6K91Kg3foL9uoacerBRzyXVtR5cKjvsaU4i6QTOZLhoy7B5f6%2F2XckUS87TVTckD01QoCf1BjX%2BBlZ3u%2BpkQeApqO8Qnxg6QQ5QWgzdK9AUKkBowP"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=cO6GsCp4m6Ta7%2FrZUegzaxTNwqfspvmo24EyVRJVBQ%2BjqVI9H%2F5URgMbb8mmSAmKc8X86YXG6fc0gM5O3H6hnqDupHKXVKuZqNj8hha3Xn2adExeOMBKJuolxLgJaNh2"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml index 9995703edef..0751e3f7ca7 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml @@ -34,7 +34,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed4827d7a6f0c15-AMS + - 6ef6348e78f85953-AMS Connection: - keep-alive Content-Encoding: @@ -42,7 +42,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 17 Mar 2022 08:55:27 GMT + - Mon, 21 Mar 2022 11:04:11 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -50,7 +50,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=589FhLG7JzcG7TI8na6JanB7RI%2B7%2F9oaDndmmXmljBq%2FZCaExmPHqMk8%2BSHFXaRUCykyVlKrd4k7dgETeRpuXK6oCf%2FL%2F05k4hZgIGxLZWkO%2FVV%2FZeW5S%2FmTvwzTIbgc"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=pBO7yrOGsxR80hCNOlUKvNCcot63YjVLIgsHpVOgrdG2%2BUK%2B89uEZcrwZlfMlzaWdVKxMlPou90C1c7lI06zmvNHW5Nmh25bzCpIOQWGW3uZOEgiFAtm9Dsh63Q%2BTZse"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml index f74031adced..2b1753fe15f 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_model/test_get_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml @@ -31,7 +31,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed482804f7e6b3e-AMS + - 6ef634918aad417e-AMS Connection: - keep-alive Content-Encoding: @@ -39,7 +39,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 17 Mar 2022 08:55:28 GMT + - Mon, 21 Mar 2022 11:04:12 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -47,7 +47,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=lJGN8YF615ivCu3nF0VlktN1O3ax4e5DgCL24VuoGJPp9I7dhcplYq4dY5h%2FrN0dC%2FmGhoS89w%2B4dyakdr2rS0%2FxkFcJr1qB1TQD1GLG6cDZHWwyhdm4vCkrV2TbvtCC"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=A8BWBJuMNGUDgbepsT6wAGDOjGnOG8D7ySaWeXqAhVY2TTT%2F%2BO4QTmCVMFFPGcVmbmov5IxERXRcl9hjXcnymeI4ClzmSZh%2F5ZL2u%2Fer%2FCZfBJqO249EfMtCrk1eLXgr"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: @@ -96,7 +96,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ed482825ce50c11-AMS + - 6ef63493d956d919-AMS Connection: - keep-alive Content-Encoding: @@ -104,7 +104,7 @@ interactions: Content-Type: - application/json Date: - - Thu, 17 Mar 2022 08:55:28 GMT + - Mon, 21 Mar 2022 11:04:12 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -112,7 +112,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=rG%2BJ2K9yc1czR64NkguczL898qE21HinIbi5s6bMfbFqP5SZI1xsmw%2FYV2C0s4truLkYx4T2ejXn7w12YBNHFPz1xsxD4lF1%2Ff4aC%2FrXd9SFlpATM88n6p9dRG246Z5z"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=k0PSqA8jc4xHzYLRwrcXZXFYLK629oFKw3cMvwRKyECU1QIyv7nCdAccNvxcBbtiRMHjBLkymNuuznndjFcOPfsZa2FDQ7B8mtJ8G1fQIQFdaKWUmFFpSdFWewlktaCE"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_macro_data[parameters0-countries0-2020-01-01-2020-10-02-False].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_macro_data[parameters0-countries0-2020-01-01-2020-10-02-False].yaml index bbcff41ad91..3adace23e03 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_macro_data[parameters0-countries0-2020-01-01-2020-10-02-False].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_macro_data[parameters0-countries0-2020-01-01-2020-10-02-False].yaml @@ -2,34 +2,93 @@ interactions: - request: body: null headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate Connection: - - close - Host: - - www.econdb.com + - keep-alive User-Agent: - - Python-urllib/3.9 + - python-requests/2.27.1 method: GET - uri: https://www.econdb.com/api/series/RGDPUS/?format=csv + uri: https://www.econdb.com/series/context/?tickers=RGDPUS response: body: - string: 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'{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=UM9%2FWxvLZo7ccOnHl4VlvRmfS34HYtFnLkaXd2eRBbzgl133gplX%2FcpVYtruDYZ%2FfkKQ8Wz147oekFv%2BK7%2BBrqyjpczvzNCVv3qM8t4uHjRflLm5IgLu5h2osZZBCxnE"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare + Transfer-Encoding: + - chunked Vary: - - Accept, Origin + - Accept-Encoding + - Origin X-Content-Type-Options: - nosniff X-Frame-Options: @@ -52,34 +78,80 @@ interactions: - request: body: null headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate Connection: - - close - Host: - - www.econdb.com + - keep-alive User-Agent: - - Python-urllib/3.9 + - python-requests/2.27.1 method: GET - uri: https://www.econdb.com/api/series/PPIFR/?format=csv + uri: https://www.econdb.com/series/context/?tickers=PPIFR response: body: - string: 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'{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=7KkbB56WzwJ%2Bx3LN8Oye3oDjlojImEqgyjdsh4TxWK9F3jG9M2tHtQAxdaHYBhQMVICSx87Yo6Ygm%2Fr4KBPUJP6S0PRa%2BM%2Bfsk8MSXfZljydSrSYp5NYffYW9OoZQ4nN"}],"group":"cf-nel","max_age":604800}' + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Accept-Encoding + - Origin + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - DENY + status: + code: 200 + message: OK +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.econdb.com/series/context/?tickers=GDPNL + response: + body: + string: !!binary | + H4sIAAAAAAAAA51ZbW/UOBD+K9F+OIG0S/0SOzYInXqlVJxoj/LyqYcis0nb3GWTkDjQCvW/39hp + r2UzaAdERbuun5l5xjPjJ+nZt0XhvHN9764XT5Oz+LGEnxaCCb7ibMX4Ypksjl68OXkNy0JIxgUs + DN75cQgbX1aNqxc3y2QLy7IZlolU0rDpNpZzkxL98m0sk0YZApYhfJlSjOKXIXyZlIbTsNt8udFW + 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X-Content-Type-Options: + - nosniff + X-Frame-Options: + - DENY + status: + code: 200 + message: OK +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.econdb.com/series/context/?tickers=RGDPNL + response: + body: + string: !!binary | + H4sIAAAAAAAAA61ZYU/cOBD9K1E+nEDaLY5jO3ar6sRRinoC7qDtfeFQ5G4M5JpN0sShoIr/fmMv + XLfdQUzvTiBgvXnjeTPjl5fl7EtaWW/tMNjb9HlyFl86+CvljGfzjM1Zls6S9PTg1e/Hh7CeGaml + gZXRWz+N4crXdWub9G6WfAdmxSZYMMVoYLEJZkwqGjjbAOs8KwQBzDDOWiimSWCEcwAXNPAm5wK+ + iDsjnAuREQqWGbTPTArCzgDGOBtpOA28yVlrw3IaGOGsM0rBMo32WZmCUjCNcoY2E/ocwAhnKQVh + PAMY4SxyQzhVWYFyzpWkpF2gnDnPKX0uUM7MkMazwDgXJhcUzgrjXGiRUTgrjHOhTC5pYOQ8C6Np + aWOchVQE6c0kypkb0nhKlDM3kpK2RDnznHLHCGCEM8wYBSxQzgzUgARGOCvDDeVICoyz0rqgaJjA + OKtCk85zjnFWmglKwXKUszKaCEY4S6Mp0pujnBUn7cxRzrKQlFbxRzhzioZxlHOhBGXCON5nSUob + 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GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + NEL: + - '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}' + Referrer-Policy: + - same-origin + Report-To: + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=JPGjgwKC0VDYThWADdg0kyhY4wvdlu%2Bm3CuIkmUoYcJZAaLxZm2EZ%2FCmKihTRZ%2FTinuFoS17AgUMVKYVmaLR6HgLv6u%2B6G9xlExDHYgtvnsVst%2FU96AvTUsLaVfqVGG%2F"}],"group":"cf-nel","max_age":604800}' + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Accept-Encoding + - Origin + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - DENY + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].yaml index ddadccdbbd5..6a46fcd6dd2 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].yaml @@ -36,7 +36,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ee79d3a2f7b5746-IAD + - 6f0a29fb9e375872-IAD Connection: - keep-alive Content-Encoding: @@ -44,7 +44,7 @@ interactions: Content-Type: - application/json Date: - - Sat, 19 Mar 2022 16:34:13 GMT + - Wed, 23 Mar 2022 21:12:14 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -52,7 +52,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=iilxj%2FbGy7kNdFHWd4OEYetx%2FK91xfFoEOMRDkuecV0BQ5%2BPoGp9Va5i26k1NHmAUUuw0qcjbrkVpeicKsB7KLSemggNWaCWE1gGw6cg59aSp93pqqxmNxBR73TKsACH"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=pSMQ3KN3jKq4dcTu6yERmQpt6JNaQ9nzw8NbzOK3DplHTEsoc6rcwqUgegM8ElOypjMwAwtHI4lkmvVxwEymQZNOD7P0lBTp2A2k1gEW6KWUh7wqoEdz7ZRnwveC0fjk"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: @@ -101,7 +101,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ee79d40787b5a0a-IAD + - 6f0a2a008fe59c2e-IAD Connection: - keep-alive Content-Encoding: @@ -109,7 +109,7 @@ interactions: Content-Type: - application/json Date: - - Sat, 19 Mar 2022 16:34:13 GMT + - Wed, 23 Mar 2022 21:12:14 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -117,7 +117,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=AnhozhMgErA0FlQeO0%2BqpPj8kAc97%2Fe7%2FoB1i1yhzWrbW8ylJ%2F7AdD9i%2BojCuhSYxouxgHpnWPyPVKo%2Fw3e7xCVAasVLNtSjKU1cKBK5NrhKKFuogpgQdyi%2Fv1l8BXXR"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=l3z4LkdVckE%2F%2BX6OnZxMBpAmPHHk57IaUtejCdzdTj3ea7yn5iH7SXhJJdwaSmHJjgoI9B1okXSmfiVJFAKSrVr61qGxmpqMcrnvWdWULEP8cbNMHzLVraKtNpKUz0zp"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml index 077a1459862..e0af9043332 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].yaml @@ -34,7 +34,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ee79d439bb157cd-IAD + - 6f0a2a039e278242-IAD Connection: - keep-alive Content-Encoding: @@ -42,7 +42,7 @@ interactions: Content-Type: - application/json Date: - - Sat, 19 Mar 2022 16:34:14 GMT + - Wed, 23 Mar 2022 21:12:14 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -50,7 +50,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=pXwlQjpKGTwuXCAFwixO8W1ACFRJk2fCrSMNne1ebEIb00MIQfo%2FuoKiP7D5Gz70C1Cj6bESkDqsd4zftK3oIlg8JeQrtGZW5pvap0Dvz978qETrkk%2BHSdQZvun373AX"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=QmGz3jO9vax3C%2FkoRl%2F83R2uqsoTA68H3lP3QO%2BTHdN1PS3sp55uMRI8zCj57E5socNVkQ7r6anTxf%2BLS8SdYHmzJnvxysL1dlwgHMsdva%2BlR5Lg%2B7%2FcW%2BO%2B2WGVXpWg"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml index 92deec33f20..f1a8b964d68 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].yaml @@ -31,7 +31,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ee79d46d9fb82aa-IAD + - 6f0a2a0709ea8236-IAD Connection: - keep-alive Content-Encoding: @@ -39,7 +39,7 @@ interactions: Content-Type: - application/json Date: - - Sat, 19 Mar 2022 16:34:14 GMT + - Wed, 23 Mar 2022 21:12:15 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -47,7 +47,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=CgooxJewSOcBL9tiR08jvht2SZqzzwfI8bD7b1GWnAgVi8smay9TUc6XmRqLQiJIq0s7lJDOUtB6%2BHra73oPgxraOe%2BRS1pHW4ikmKjKQwEoTVqt01q14ISuQWXsnRVx"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=DVeIUYDx3EA3LWs4vLAECskkR8te1aio37vjfNXyuyjFdrGYZP8WIkJSotjsfe4dpu1dUtoc%2F2Ta%2Fma5Xs7HFczYRx8M1IIcBvTAE0fWdh60ky8Bjp3B9o17Y2aTkO92"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: @@ -96,7 +96,7 @@ interactions: CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ee79d492e2f6fb0-IAD + - 6f0a2a0958457f86-IAD Connection: - keep-alive Content-Encoding: @@ -104,7 +104,7 @@ interactions: Content-Type: - application/json Date: - - Sat, 19 Mar 2022 16:34:15 GMT + - Wed, 23 Mar 2022 21:12:16 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" NEL: @@ -112,7 +112,7 @@ interactions: Referrer-Policy: - same-origin Report-To: - - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=0%2FjKTyurwOk4piS6ZEbOeLwSV5rQM9xgFnz0LN5AIa7aKwTgcoFH1U%2B%2FMZ39IrdAf7eVsZzbMWlcUeSM6bZfF6xHdWq%2Ftg06SCtZeO2UxjS4XmiCm4IN9wMLvgF69PCt"}],"group":"cf-nel","max_age":604800}' + - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v3?s=WZol2DJL7AJ7QzXVnoUfB44K%2BxFKhKg1vzp0Us3ZMOsATI2bLw4D6KHC6RnZ2pmTYRQ5eyDNA8s7vuKvkKQe5Odq1yQyvEAQ1ZqZVuRMuVd2lYFVUO3540HO5MNgykkS"}],"group":"cf-nel","max_age":604800}' Server: - cloudflare Transfer-Encoding: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_futures.yaml b/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_futures.yaml index 3065aadc6c0..22858a41566 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_futures.yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_futures.yaml @@ -13,133 +13,133 @@ interactions: response: body: string: 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6ed482bf1ee8fa48-AMS + - 6ef634bcaf999785-AMS Cache-Control: - no-cache Connection: @@ -308,7 +307,7 @@ interactions: Content-Type: - text/html; charset=utf-8 Date: - - Thu, 17 Mar 2022 08:55:38 GMT + - Mon, 21 Mar 2022 11:04:18 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" Server: @@ -320,7 +319,7 @@ interactions: request-context: - appId=cid-v1:bdd405ef-7a83-4f9a-9228-0107d7f4bc90 set-cookie: - - preventRedirectLoop=false; expires=Wed, 16 Mar 2022 08:55:38 GMT; domain=.finviz.com; + - preventRedirectLoop=false; expires=Sun, 20 Mar 2022 11:04:18 GMT; domain=.finviz.com; path=/ vary: - Accept-Encoding diff --git a/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_valuation_performance_data[valuation].yaml b/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_valuation_performance_data[valuation].yaml index 38975051d3a..a2861da6632 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_valuation_performance_data[valuation].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_finviz_model/test_get_valuation_performance_data[valuation].yaml @@ -13,128 +13,128 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA7xYbXPbNhL+7pn7DyhuElJXkaLkd1lUp3WSa2biNqmT3t1kMh4IWJKISYAFQL3E - 9X+/ASnSkk3ajpupPkgisHh298HuYonJdy9+PX3/v7cvUWKydLozqX+AsOnOxHCTwvTfShY5OqcK - QIBCHvqdpAUxXAqkgRqpkCAZTAaV9M4kA0MQTYjSYEL84f0r7whPq9HEmNyDPwo+D/F/vQ8/eqcy - y4nhsxQwolIYECbEr1+GwGLA03/soK5PhWcVh3jOYZFLZTYgFpyZJBwGo71adSXKQFPFc2v8hvS5 - kfQS6drDSCrExRy0kUojIhgyijBQuo8iLoignKRoznVBUv6lJEL7uHa80nMJq4VUTN9RUtPYR6cJ - UUb30btCGtB9dEZy3Ue/wEL30atGze9bavroN9BAFE366L0ijIsYna+0gUw3BmxyzDMSg5EynRG1 - YYqQrdK5InFGtuQ8SmgCrdLljGdllUwfu+jlMucKNmnxhni6s73Rk5SLS6QgtSZBKgnDKFEQhXgQ - SWH0ICVGevPhoZcSw4UHS3NR/VMQFylR/kJG0QgjokNsV2BEldRaKh5zMf1Wyg6D4O9RdPyNFXFh - 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charset=utf-8 Date: - - Thu, 17 Mar 2022 08:55:37 GMT + - Mon, 21 Mar 2022 11:04:18 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" Server: @@ -326,7 +326,7 @@ interactions: request-context: - appId=cid-v1:bdd405ef-7a83-4f9a-9228-0107d7f4bc90 set-cookie: - - preventRedirectLoop=false; expires=Wed, 16 Mar 2022 08:55:37 GMT; domain=.finviz.com; + - preventRedirectLoop=false; expires=Sun, 20 Mar 2022 11:04:18 GMT; domain=.finviz.com; path=/ vary: - Accept-Encoding diff --git a/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_future.yaml b/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_future.yaml index b520123cf45..cf28f3c370b 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_future.yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_future.yaml @@ -13,133 +13,133 @@ interactions: response: body: string: !!binary | - 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no-cache Connection: @@ -308,7 +308,7 @@ interactions: Content-Type: - text/html; charset=utf-8 Date: - - Thu, 17 Mar 2022 08:55:39 GMT + - Mon, 21 Mar 2022 11:04:19 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" Server: @@ -320,7 +320,7 @@ interactions: request-context: - appId=cid-v1:bdd405ef-7a83-4f9a-9228-0107d7f4bc90 set-cookie: - - preventRedirectLoop=false; expires=Wed, 16 Mar 2022 08:55:39 GMT; domain=.finviz.com; + - preventRedirectLoop=false; expires=Sun, 20 Mar 2022 11:04:19 GMT; domain=.finviz.com; path=/ vary: - Accept-Encoding diff --git a/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_valuation.yaml b/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_valuation.yaml index 662be018557..714080a1e0a 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_valuation.yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_finviz_view/test_display_valuation.yaml @@ -13,128 +13,128 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA9Q97XLbOJK/11X3DlhuZWTvWhQ/JOrDlqYS20m8lWSSOJnZmamUCyJBCTFFcABQ - siabqnuL+3//7jXu3uSe5KrBD32YlBVK9uylKokIAt2NRqO70QDRp38+/+Hsw89vL9BYToLBwWn2 - H8He4OBUUhmQwQvO4ghduZyQkHBURz/iIMaSshAJ4krGUYgn5LSR1D44nRCJkTvGXBDZ1z5+eF7v - aIOkdCxlVCe/xXTa1/5R//i0fsYmEZZ0GBANuSyUJJR97fKiT7wR0Qb/doDK/iTwAHFfm1IyixiX - SyBm1JPjvmlYzQx1UtUjwuU0AuKXal9J5t4gkfXQZxzRcEqEZFwgHHpIcuwRLo6RT0McuhQHaEpF - jAP6u2KE0LWs4wmeGzKfMe6JO0gyNh6jszHmUhyjdzGTRByj1zgSx+gNmYlj9DxH8+MKmmP0ngiC - uTs+Rh849mg4QldzIclE5AQs85hO8IhIxoIh5kukhKywdsTxaIJX6tVd7I5JYW31pg51OQu2bXRx - G1FOltlSN7XBwepAnwY0vEGcBEASCRj2NDTmxO9rDZ+FUjQCLFl9arbrAZY0rJNbeZ384mQUB5jr - M+b7loaw6GvQQkMuZ0IwTkc0HOwLWdswHgdRd8+IaCgJT7BsC3cJppDzgIgxITIHi4UgUjQ8KmTD - p+GU/q473ZbttH1Td4XQkJxHpK9Jcisb8Dw42B7eBNNQ79rttuUYzv3QqAszO6mhpL8RhaNF//EU - 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request-context: - appId=cid-v1:bdd405ef-7a83-4f9a-9228-0107d7f4bc90 set-cookie: - - preventRedirectLoop=false; expires=Wed, 16 Mar 2022 08:55:39 GMT; domain=.finviz.com; + - preventRedirectLoop=false; expires=Sun, 20 Mar 2022 11:04:20 GMT; domain=.finviz.com; path=/ vary: - Accept-Encoding diff --git a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_check_output[get_series_data-kwargs_dict1].yaml b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_check_output[get_series_data-kwargs_dict1].yaml index 09c1c9e26d0..27f91e6685e 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_check_output[get_series_data-kwargs_dict1].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_check_output[get_series_data-kwargs_dict1].yaml @@ -10,777 +10,780 @@ interactions: uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2020-01-01&series_id=DGS10 response: body: - string: "\n\n\n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n" headers: Connection: - close @@ -788,13 +791,13 @@ interactions: Content-Type: - text/xml; charset=UTF-8 Date: - - Thu, 17 Mar 2022 08:55:43 GMT + - Mon, 21 Mar 2022 11:04:23 GMT Expires: - - Thu, 17 Mar 2022 08:56:43 GMT + - Mon, 21 Mar 2022 11:05:23 GMT Last-Modified: - - Wed, 16 Mar 2022 20:19:04 GMT + - Fri, 18 Mar 2022 20:24:46 GMT Set-Cookie: - - cookiesession1=678A3E1F9F2F218EB69CB403AA78F9DE;Expires=Fri, 17 Mar 2023 08:55:36 + - 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a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_load_data[get_series_data-kwargs_dict0].yaml b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_load_data[get_series_data-kwargs_dict0].yaml index 49e41d4238c..419915a1a82 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_load_data[get_series_data-kwargs_dict0].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_load_data[get_series_data-kwargs_dict0].yaml @@ -10,777 +10,780 @@ interactions: uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2020-01-01&series_id=DGS10 response: body: - string: "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n" + sort_order=\"asc\" count=\"577\" offset=\"0\" limit=\"100000\">\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n" headers: Connection: - close @@ -788,13 +791,13 @@ interactions: Content-Type: - text/xml; charset=UTF-8 Date: - - Thu, 17 Mar 2022 08:55:43 GMT + - Mon, 21 Mar 2022 11:04:24 GMT Expires: - - Thu, 17 Mar 2022 08:56:43 GMT + - Mon, 21 Mar 2022 11:05:24 GMT Last-Modified: - - Wed, 16 Mar 2022 20:19:04 GMT + - Fri, 18 Mar 2022 20:24:46 GMT Set-Cookie: - - cookiesession1=678A3E1FB7FDB3B412C050DCF43D6B94;Expires=Fri, 17 Mar 2023 08:55:37 + - cookiesession1=678A3E1F5FF48CDEC312A0DE606F5911;Expires=Tue, 21 Mar 2023 11:04:16 GMT;Path=/;HttpOnly Transfer-Encoding: - chunked diff --git a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_output[check_series_id-kwargs_dict0].yaml b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_output[check_series_id-kwargs_dict0].yaml index 3353ec35926..0ae6e3dedc8 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_output[check_series_id-kwargs_dict0].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_output[check_series_id-kwargs_dict0].yaml @@ -13,16 +13,16 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAEA6pWKkpNzCnJzE2NLy5JLCpRslIyMjAy0jUw1jU0V9JByKbmpaDLFacWZaYWFytZ + H4sIAAAAAAAEA6pWKkpNzCnJzE2NLy5JLCpRslIyMjAy0jUw1jUyVNJByKbmpaDLFacWZaYWFytZ RVcrZYJkXdyDTZH1kGxiSWZJTqqSlZJvYlF2aolCZGZqTopCfp5CqF6wnkJIUWpicWlRpUJwanJp UWZJZmqxQmKJgqluZGpikYJzfl5xSWJeiYJvYglItlJJRyk/qTi1qCyxJDM/D+47Q0szI10DQ10D - IzQFaB4EeSStKLWwNDUvuVLJSsklMTMHZCZcLL44I78IFF4uSjpKpXmZJcVKVkoBqUXJqXklMBG4 - GlUlHaXi1MTi/LzEnPjElKzS4pJckDorJb/8EoVgqExOpYIjWC41Bbt6uHl+wY5KOko5icUl8aUF - KYklqaDgh0ecmYKhqZWhpZWhga4ByB8F+QWlOYlFmSWVSlbm5jpKefklqSDXuuUXKaSVFpVkpBYp - 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Content-Type: - application/json; charset=UTF-8 Date: - - Thu, 17 Mar 2022 08:55:40 GMT + - Mon, 21 Mar 2022 11:04:21 GMT Expires: - - Thu, 17 Mar 2022 08:56:40 GMT + - Mon, 21 Mar 2022 11:05:21 GMT Last-Modified: - - Wed, 16 Mar 2022 20:19:10 GMT + - Fri, 18 Mar 2022 20:24:43 GMT Set-Cookie: - - cookiesession1=678A3E1F6AB80524C9355213CFDBE5C0;Expires=Fri, 17 Mar 2023 08:55:33 + - cookiesession1=678A3E1FCD2B52BC94FB0A1F60F713D4;Expires=Tue, 21 Mar 2023 11:04:14 GMT;Path=/;HttpOnly Vary: - Accept-Encoding diff --git a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_yield_curve[date0].yaml b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_yield_curve[date0].yaml new file mode 100644 index 00000000000..0b6efb50739 --- /dev/null +++ b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_yield_curve[date0].yaml @@ -0,0 +1,464 @@ +interactions: +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS1MO + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:21 GMT + Expires: + - Wed, 23 Mar 2022 18:20:21 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:12 GMT + Set-Cookie: + - cookiesession1=678A3E1F2691F40152B456FD7828FA5D;Expires=Thu, 23 Mar 2023 18:19:13 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '120' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS3MO + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:21 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:16 GMT + Set-Cookie: + - cookiesession1=678A3E1F1A9726F8BC3A2CED64B75208;Expires=Thu, 23 Mar 2023 18:19:15 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '119' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS6MO + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:18 GMT + Set-Cookie: + - cookiesession1=678A3E1FCE5D5CF90759C27D028015E2;Expires=Thu, 23 Mar 2023 18:19:14 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '118' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS1 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:07 GMT + Set-Cookie: + - cookiesession1=678A3E1F36DFCC12BD162D3459A47949;Expires=Thu, 23 Mar 2023 18:19:14 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '117' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS2 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:13 GMT + Set-Cookie: + - cookiesession1=678A3E1F24000460E9A0488E187416B8;Expires=Thu, 23 Mar 2023 18:19:15 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '116' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS3 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:15 GMT + Set-Cookie: + - cookiesession1=678A3E1FEB4F69E359AFF86C827E31C9;Expires=Thu, 23 Mar 2023 18:19:14 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '115' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS5 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:17 GMT + Set-Cookie: + - cookiesession1=678A3E1F8D48333C4B31130D6CD6430A;Expires=Thu, 23 Mar 2023 18:19:16 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '114' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS7 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:19 GMT + Set-Cookie: + - cookiesession1=678A3E1F0559625B0332F62F2355DDCE;Expires=Thu, 23 Mar 2023 18:19:14 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '113' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS10 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:08 GMT + Set-Cookie: + - cookiesession1=678A3E1F260F942C0B250B4E350FC1B9;Expires=Thu, 23 Mar 2023 18:19:16 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '112' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS20 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:22 GMT + Expires: + - Wed, 23 Mar 2022 18:20:22 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:14 GMT + Set-Cookie: + - cookiesession1=678A3E1F6CE17BEA04D09FFED4BE4989;Expires=Thu, 23 Mar 2023 18:19:15 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '111' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2022-03-21&series_id=DGS30 + response: + body: + string: "\n\n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '415' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:19:23 GMT + Expires: + - Wed, 23 Mar 2022 18:20:23 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:15 GMT + Set-Cookie: + - cookiesession1=678A3E1F5448BC889688A34DD5412397;Expires=Thu, 23 Mar 2023 18:19:15 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '110' + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_yield_curve_none[None].yaml b/tests/gamestonk_terminal/economy/cassettes/test_fred_model/test_yield_curve_none[None].yaml new file mode 100644 index 00000000000..0bd790d246c --- /dev/null +++ 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https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2021-07-17&series_id=DGS3 + response: + body: + string: "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '18246' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:25:25 GMT + Expires: + - Wed, 23 Mar 2022 18:26:25 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:15 GMT + Set-Cookie: + - 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\n \n \n \n \n \n \n \n \n \n\n\n\n\n" + headers: + Connection: + - close + Content-Length: + - '18246' + Content-Type: + - text/xml; charset=UTF-8 + Date: + - Wed, 23 Mar 2022 18:25:26 GMT + Expires: + - Wed, 23 Mar 2022 18:26:26 GMT + Last-Modified: + - Tue, 22 Mar 2022 20:23:14 GMT + Set-Cookie: + - cookiesession1=678A3E1FF799B215D2BB1A8A287091E7;Expires=Thu, 23 Mar 2023 18:25:19 + GMT;Path=/;HttpOnly + Vary: + - Accept-Encoding + x-rate-limit-limit: + - '120' + x-rate-limit-remaining: + - '111' + status: + code: 200 + message: OK +- request: + body: null + headers: + Connection: + - close + Host: + - api.stlouisfed.org + method: GET + uri: https://api.stlouisfed.org/fred/series/observations?api_key=MOCK_API_KEY&observation_start=2021-07-17&series_id=DGS30 + response: + body: + string: "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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a/tests/gamestonk_terminal/economy/cassettes/test_wsj_model/test_call_func[market_overview].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_model/test_call_func[market_overview].yaml @@ -9,50 +9,50 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.10; rv:86.1) Gecko/20100101 Firefox/86.1 + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:82.1) Gecko/20100101 Firefox/82.1 method: GET uri: 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b/tests/gamestonk_terminal/economy/cassettes/test_wsj_model/test_call_func[top_commodities].yaml index 73edf2cdddb..1f29d00e23e 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_model/test_call_func[top_commodities].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_model/test_call_func[top_commodities].yaml @@ -9,50 +9,51 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.10; rv:86.1) Gecko/20100101 Firefox/86.1 + - Mozilla/5.0 (Windows NT 6.1; WOW64; rv:84.0) Gecko/20100101 Firefox/84.0 method: GET uri: 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Cache-Control: - public, max-age=120, no-cache=Set-Cookie @@ -62,17 +62,17 @@ interactions: Content-Type: - application/json; charset=utf-8 Date: - - Thu, 17 Mar 2022 08:55:52 GMT + - Mon, 21 Mar 2022 11:04:33 GMT ETag: - - W/"3080-B1Ewl0OMtfYdN0PjtbyF5/ykRoA" + - W/"3074-rp3mTeEuAxeWqf0BwLrnp+rW6tE" GC-Versions: - 2.1.825|0.4.956|4.1.2 Transfer-Encoding: - chunked Via: - - 1.1 b61409af370dbf025ffc910b1252c65e.cloudfront.net (CloudFront) + - 1.1 ec5c4a66c1200ddcc562c6e98f77a48c.cloudfront.net (CloudFront) X-Amz-Cf-Id: - - VzSf4KhuWeY3mv6Cl2rTTTSH-pgwRQh5YF57euCttfHtONIAwIdWkA== + - dsU0ETplT0oRzCjy3r50G4k-_731PF_Seo6oekG4hPfLDw748wsmDw== X-Amz-Cf-Pop: - AMS54-C1 X-Cache: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_currencies-True].yaml b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_currencies-True].yaml index f16d1ba856d..b9e8bab0ec5 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_currencies-True].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_currencies-True].yaml @@ -15,43 +15,43 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA8VaXXObOBT9Kx5mH7IziUF8Gfzm2Ok23dabieNZZ5bODrVpwtZfBTyJN5P/vlcC - jCQkAjbtvtlX4kroHN0rncuLEi6UvvLiKf52uwznfhJu1p7S95Q/Jx885dxTwnWcRLtVsE5isP8F - PeP96stmSToNp7e3V+PhvTqdqOrV9HY6GZGH1v4qIB2udtGmcwYt0GP0q6e8nlc4gC4fbu5ZBx/8 - rb8O4qBzH6w7Z9BDhS5vOfrt8qY0k2n3927nZrNbLzpn0F5zPsP371g3k6cwjjvvIn89T6cDPer4 - GXPvNXwM09fa+dl7QZc6jgbcew39tb8IwckvmZtBnff6NBuzbj4Fz4D+unMTxJvUEXQRObq9v7n7 - g3F3eTcsrfZlmMw3IbwZNEqW+no8upqpsxk4mM5m7ONAvs5os1z6Ued6vQieZU/j4Uezex7pSZd/ - +vOrcq4k+20AZF8t5n9/322SIAbbwk98pf9C0xyzXPmy3AUPkR/Hw0d/vQ6WuM88/6mo/4YPK3+t - q6kfVUeaZSPd1NUo8JdJuArU2F9tl8GiGHYULP19sMj9vZ4rc2BjEu2hbTohUwmX+/fhw6PSR12k - 2U5m+rh5Si0aWIJnPIuH4DreDDcL7Hd2N72Bp79uopWfJMFiDOsAZnbnQYelHyc3UTjHjdibpYMR - 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a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-False].yaml b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-False].yaml index 150871d3a79..23b68f40af8 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-False].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-False].yaml @@ -15,44 +15,45 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA8VaXVPjOBb9KynXPOzWgG35M+YNQhoynU66krCwtZ7aMokHPOPEjOMMw1L8972S - nCDZkj/iVPULRWTrStY5upLO0bsSrZQL5d1XgpeXOFoGWZRsfOXCV+7nv/jKma9Em22W7tbhJttC - +X/gze3b+jGJyUtf7hZ3s6F2N9e0wezuetibjsa9895wPBwsZtPJaEBCbIJ1SF4fpLtV2JtGce/L - LtulIUT8OBNH/KppV7PhZNEjcfkwVyn0pkeD1QaCrt1Mx9d8hJskXjWqOh+N/zWc8ZXnUfxXmDaq - PrmEfy/HvZvLOR9jEkDlIO7dBNtGge4m4+Hl9fAaR5qOR5PCiNxt4jBYhSscL4mjTbNxGUy/fy9+ - 3CB5eWn4cYPpbFKsnG4aVb2/HV4u+Lr3z2GQySuPJtfDB+3hAWgxmH6D579+KGdK9vYSAn3Xq+V/ - 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GMT + - Mon, 21 Mar 2022 11:04:32 GMT ETag: - - W/"3082-RsMmKOW3ZSgVn+P37frioVTZP9E" + - W/"309e-EXNpmz4RO1OsNrxey2OPEKVPK54" GC-Versions: - 2.1.825|0.4.956|4.1.2 Transfer-Encoding: - chunked Via: - - 1.1 3af85c3075e12aff72b9e148b99d6622.cloudfront.net (CloudFront) + - 1.1 bb1fd0922e473ba97ff6a00f6c71141a.cloudfront.net (CloudFront) X-Amz-Cf-Id: - - AKqxBnuUovd76-zOEtq5uVlEFUL_zjKoit8Zq6sO-yOPEYhBHsqjsw== + - BVFcm3YcPGiTmD8Ldm18L-WysB6aUDpHnA9Zf2lDe36WoMguAtRTgQ== X-Amz-Cf-Pop: - AMS54-C1 X-Cache: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-True].yaml b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-True].yaml index 85f8a99a447..18f0c8b2cb7 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-True].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_futures-True].yaml @@ -9,50 +9,51 @@ interactions: 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b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_glbonds-True].yaml index 12a770d1ea5..74a53cfc17d 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_glbonds-True].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_glbonds-True].yaml @@ -9,34 +9,33 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:82.1) Gecko/20100101 Firefox/82.1 + - Mozilla/5.0 (Windows NT 6.1; WOW64; rv:86.1) Gecko/20100101 Firefox/86.1 method: GET uri: 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b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_indices-False].yaml index cbd0ccc0000..db1a49a96d4 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_indices-False].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_indices-False].yaml @@ -9,51 +9,51 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Windows NT 6.1; WOW64; rv:86.1) Gecko/20100101 Firefox/86.1 + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:82.1) Gecko/20100101 Firefox/82.1 method: GET uri: 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b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-False].yaml index 38b84c508e5..ac238d7788b 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-False].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-False].yaml @@ -9,54 +9,53 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:86.0) Gecko/20100101 Firefox/86.0 + - Mozilla/5.0 (Windows NT 6.1; WOW64; rv:84.0) Gecko/20100101 Firefox/84.0 method: GET uri: 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GMT ETag: - - W/"43a8-kqR8fO2XOYslvh7G8thGHpinYOE" + - W/"437e-bxdddsMlcmULadElyE3Q4yPMbeM" GC-Versions: - 2.1.825|0.4.956|4.1.2 Transfer-Encoding: - chunked Via: - - 1.1 b911c551065b8f78ad33b4c4564141be.cloudfront.net (CloudFront) + - 1.1 0f34c0d3b0e50b8875bcbb7d41684a58.cloudfront.net (CloudFront) X-Amz-Cf-Id: - - 2BQSuA8eNQ9_whlpaSCXM-f7lncJncEMecorcoGp-ISDPA7TOi3l-Q== + - IzsIxJJXTcUa6aijrzMfB1IK9ZTSf1GdhrC5ByI46HduMBbm4yRfog== X-Amz-Cf-Pop: - AMS54-C1 X-Cache: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-True].yaml b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-True].yaml index fdec87c7c81..9aa817b084e 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-True].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_wsj_view/test_call_func[display_usbonds-True].yaml @@ -9,54 +9,53 @@ interactions: Connection: - keep-alive User-Agent: 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headers: Age: - '0' @@ -36,11 +36,11 @@ interactions: cache-control: - public, max-age=10, stale-while-revalidate=20 content-length: - - '1029' + - '1030' content-type: - application/json;charset=utf-8 date: - - Thu, 17 Mar 2022 08:55:52 GMT + - Mon, 21 Mar 2022 11:04:33 GMT server: - ATS vary: @@ -48,13 +48,13 @@ interactions: x-envoy-decorator-operation: - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '3' + - '2' x-request-id: - - 6e5c8611-e214-420a-99dd-936473acfa12 + - de69ead3-565b-4403-ba11-398acfad5c3d x-yahoo-request-id: - - c4kvoq9h35tso + - 16dtef9h3gmu1 y-rid: - - c4kvoq9h35tso + - 16dtef9h3gmu1 status: code: 200 message: OK diff --git a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[nasdaq-1wk-2020-06-06-2020-07-07-Close].yaml b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[nasdaq-1wk-2020-06-06-2020-07-07-Close].yaml index 05b593ce0dc..89196c0ee46 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[nasdaq-1wk-2020-06-06-2020-07-07-Close].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[nasdaq-1wk-2020-06-06-2020-07-07-Close].yaml @@ -15,7 +15,7 @@ interactions: uri: https://query2.finance.yahoo.com/v8/finance/chart/%5EIXIC?period1=1591394400&period2=1594072800&interval=1wk&includePrePost=False&events=div%2Csplits response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"^IXIC","exchangeName":"NIM","instrumentType":"INDEX","firstTradeDate":34612200,"regularMarketTime":1647465359,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":13436.553,"chartPreviousClose":9814.08,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1647504000,"end":1647523800,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1647523800,"end":1647547200,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1647547200,"end":1647561600,"gmtoffset":-14400}},"dataGranularity":"1wk","range":"","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1590984000,1591588800,1592193600,1592798400,1593403200,1594008000],"indicators":{"quote":[{"open":[9703.5400390625,9823.4404296875,9426.900390625,9945.490234375,9771.7197265625,10360.3798828125],"volume":[6572850000,26190570000,23693060000,27718740000,17393620000,4529480000],"high":[9845.6904296875,10086.8896484375,10053.91015625,10221.849609375,10310.3603515625,10462.0498046875],"close":[9814.080078125,9588.8095703125,9946.1201171875,9757.2197265625,10207.6298828125,10433.650390625],"low":[9685.349609375,9413.6201171875,9403.0,9749.0703125,9663.6103515625,10354.98046875]}],"adjclose":[{"adjclose":[9814.080078125,9588.8095703125,9946.1201171875,9757.2197265625,10207.6298828125,10433.650390625]}]}}],"error":null}}' + string: 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headers: Age: - '0' @@ -40,7 +40,7 @@ interactions: content-type: - application/json;charset=utf-8 date: - - Thu, 17 Mar 2022 08:55:52 GMT + - Mon, 21 Mar 2022 11:04:33 GMT server: - ATS vary: @@ -50,11 +50,11 @@ interactions: x-envoy-upstream-service-time: - '2' x-request-id: - - c44e47df-159f-4b6f-a230-3f5052d13638 + - ce51e5cf-c2fa-49d8-a0a6-e21425fc0b95 x-yahoo-request-id: - - 152jsrph35tso + - c4g1ttlh3gmu1 y-rid: - - 152jsrph35tso + - c4g1ttlh3gmu1 status: code: 200 message: OK diff --git a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[sp500-1d-2022-01-01-2022-02-02-Adj Close].yaml b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[sp500-1d-2022-01-01-2022-02-02-Adj Close].yaml index 195cde6d2d0..eaa226fbfd7 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[sp500-1d-2022-01-01-2022-02-02-Adj Close].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_model/test_get_index[sp500-1d-2022-01-01-2022-02-02-Adj Close].yaml @@ -16,26 +16,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAO1WwW7jNhD9F51dlpwhh+Teis1i20ODoEmBFkFaqLGSuGtbqSxnNw38732UqJhy - Cuytp17oITmceXzzRvRLdftQd3317qXqmt1+Dev6pdo0fZ2Wbvdd12xvn6t31c+XZ9Wi2j1v/mjX - mP728fLiPRaaLzi/vW/O602D5cvzCyyutru+22+abX/1/JiWfzg/+/ALNu5W3a6/6uplc1b32PjG - MDkXWGu9QP77/brufqy7T01/tUrxjFhvxRnvFtX9pm/v7nZNn45Zm070cPq73aYMH86uCjRXeSOj - +m7TdKvb+tvz5vPvv7bdp+ok2QV24WfZeRVkMVJy0TVPq3a/e79ud2nTiygTFtVjcv5+tQUOWmSG - hjuttvcXyNMuE3OPXZN+3iLcLsdrOeKQ7rDrB/qHJW0HIt7c9PCK9ysxrafTmDnNv8V8bHf9VwKK - kdOAOcfbgIi4rPv6Y1dvE9RVn2RjlonsVBNMYD7V69XypzTfQWnjtkuD2bQYeRhlGM0zBkqDS4PR - aXzuk/Om/lLdHEYBANvmEbEATkcxiUGYhkhTNlmLTGbkycH6aLPpxIVsBnKTQzRmcojRZwfSwea4 - ZLzOcckZmVZF07QqIeZg5EHcaLJmmx3Y0ISXSfvJgSLnbAwjx2XhCS97SnhvUqMtoeu+7Xapjn/t - 29RU6N/bUbPXWbQaDeSDIbfApUU5pHfBRfbDAqsEjaOWwUFrlaJnf4G/tjFoKyG5i/dKE+hwYG1w - 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status: code: 200 message: OK diff --git a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_search_indices[keyword0-5].yaml b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_search_indices[keyword0-5].yaml index 9443ba204b3..d52048ab991 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_search_indices[keyword0-5].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_search_indices[keyword0-5].yaml @@ -12698,11 +12698,11 @@ interactions: Content-Type: - text/plain; charset=utf-8 Date: - - Thu, 17 Mar 2022 08:55:53 GMT + - Mon, 21 Mar 2022 11:04:35 GMT ETag: - W/"102674c9e209f742bfbfadbbd79f7f15a3af55a960b4274796f83fe80911e02f" Expires: - - Thu, 17 Mar 2022 09:00:53 GMT + - Mon, 21 Mar 2022 11:09:35 GMT Source-Age: - '0' Strict-Transport-Security: @@ -12718,15 +12718,15 @@ interactions: X-Content-Type-Options: - nosniff X-Fastly-Request-ID: - - c6e8fe0a1f10c71d91701e3045ecc276bea331da + - 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a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_search_indices[keyword2-10].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_search_indices[keyword2-10].yaml @@ -12698,11 +12698,11 @@ interactions: Content-Type: - text/plain; charset=utf-8 Date: - - Thu, 17 Mar 2022 08:55:54 GMT + - Mon, 21 Mar 2022 11:04:35 GMT ETag: - W/"102674c9e209f742bfbfadbbd79f7f15a3af55a960b4274796f83fe80911e02f" Expires: - - Thu, 17 Mar 2022 09:00:54 GMT + - Mon, 21 Mar 2022 11:09:35 GMT Source-Age: - '1' Strict-Transport-Security: @@ -12718,15 +12718,15 @@ interactions: X-Content-Type-Options: - nosniff X-Fastly-Request-ID: - - 1387a32085f8dfe756c7f042758e6ac0505b3fd9 + - 2859c465cbc9b16d0d9418b6e919db4622a5297c X-Frame-Options: - deny X-GitHub-Request-Id: - - B5F4:74B9:4C8F36:659B5C:6232EBE1 + - 5090:3524:2A72B7:42ACE7:623851F5 X-Served-By: - - cache-ams21036-AMS + - cache-ams21044-AMS X-Timer: - - S1647507355.769163,VS0,VE1 + - S1647860676.964055,VS0,VE1 X-XSS-Protection: - 1; mode=block status: diff --git a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False-False].yaml b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False-False].yaml new file mode 100644 index 00000000000..d311d88e5b5 --- /dev/null +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False-False].yaml @@ -0,0 +1,164 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, + like Gecko) Chrome/39.0.2171.95 Safari/537.36 + method: GET + uri: https://query2.finance.yahoo.com/v8/finance/chart/%5EGSPC?period1=1641013200&period2=1643778000&interval=1d&includePrePost=False&events=div%2Csplits + response: + body: + string: !!binary | + H4sIAAAAAAAAAO1WTW/jNhD9Lzq7LMnh8GNvxWax7aFB0KRAi0VaqLGSuGtbrizvbhr4v/eNRMWi + U2BPvfVCz5DD+XjzhtZzdfdYd3315rnqmv1hDenDc7Vp+lq27g5d12zvnqo31c/XF9Wi2j9t/mjX + UH97f331FhvNF9zfPjSX9abB9vXlFTZX233fHTbNtr952sn2D5cX737Bwf2q2/c3Xb1sLuoeB98Y + ssyRtNYLxH84rOvux7r72PQ3K/FnvIvaUXB+UT1s+vb+ft/0cs05udHD6O92KxHeXdzMsrnJBzmr + 7zZNt7qrv71sPv/+a9t9rM6CXeEUdg7BlDGLEZKrrvm0ag/7t+t2L4fBe2XiotqJ8ferLfKwi4zQ + UNNq+3CFOO1SkNt1jfy8znC7nMqyVmrY9wP8w5a1Q1mvKj2+5PsVn579uc8c5t987tp9/xWHUetz + hznGa4fwuKz7+n1XbyXVVS+0MUsBW3oCBeKner1a/iT6Hkwbj1kWs2mx0rD6YTVPWKwsLIvRsj71 + Yrypv1S3x5EAyG2zgy8kZ1CrVCsiae8nMQ0EE9GF5LLInmMWo+XJIBkzGaQUsoHV0WW/1gSd/Vo2 + ftr12k67PqbszIbA2RlpctmAjDXZgKwOk4FNlKMRhOyXPE35UrCS761M1xJk7ttuL83769DKJGFo + H1cPjxBQolfeaUM+mBh44aKJCpkak/WQggpam8TIRXTLSusU0NlB1UElPh37QEoSOtkbp4xNMdo4 + 6i4qmeEwqeWx91zqZBWIbgk1iWqMYpfAVD9kByiV0SnCbLR3ySmex3cmKOJZxg4u5iqTsjadXDob + VThFBNgqgCnGxXEDjVTo2gkxZq1cyhcA+ad2fZCX5IO01KRhKAgU0TyKgUg6LSIFY/OuDSmGbGvN + MEoQQSGfRWFSvsaUhmdQDCK07My65LMYA2VnLgw3RUyOoxlENmhjdpZiEr5CdIDAhiwGUCsbkNeU + dwXXXEXybhBR8br9PHCJozI+JZuZE5yyc+rgSDlKCZOl/UgWoyy/tMJ7O0eeY9l6iqU7zBaYmVBz + Gg3QBwu4KWXvYN7gx8eCwcBLFQwJJYUpsSLAMoa11hbzYGMoGUcaYWb8IZ2U2CWrHJ+yQ9HlYKBz + YCqeDTZSPmC8G/88xqGUh5Mjj5dDgq07lYZylAx4HiIPez2fClSkT65lo4QmGEypT9G6qVfWl1Pi + OZ25QHPi3ILDGRAsk3LqposyOHNg8JKkefsxEsWsE3tANmuES7igTeBcJyFLno0umbOUDBevAzs/ + gw0It7tmOwAcwNTi1UMTZ54DmpzSiXqAn+Yj71EK/jEmaHhOGO9T6TvgQeE4HWP6y956KmOVDx7H + WPaB3fl7hiHCNBYokpn1lvzZE+90xBzOcY0FnYjk42XeKAAdZvTBq4YaXkLcHgFtvfxz4u/zXPmf + zP8NmY+3R4G96boWH3r4iFofj/8A92aDuaALAAA= + headers: + Age: + - '1' + Connection: + - keep-alive + Expect-CT: + - max-age=31536000, report-uri="http://csp.yahoo.com/beacon/csp?src=yahoocom-expect-ct-report-only" + Referrer-Policy: + - no-referrer-when-downgrade + Strict-Transport-Security: + - max-age=15552000 + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-encoding: + - gzip + content-length: + - '1112' + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:46 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '5' + x-request-id: + - 3f72de96-066c-4e7f-bf1b-3fd81ebc69e7 + x-yahoo-request-id: + - b2nngmlh3m9n3 + y-rid: + - b2nngmlh3m9n3 + status: + code: 200 + message: OK +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, + like Gecko) Chrome/39.0.2171.95 Safari/537.36 + method: GET + uri: https://query2.finance.yahoo.com/v8/finance/chart/%5EIXIC?period1=1641013200&period2=1643778000&interval=1d&includePrePost=False&events=div%2Csplits + response: + body: + string: !!binary | + H4sIAAAAAAAAAO1WTXPbNhD9LzyrKLCLr82tE2daH+LxtO5MOhm3w0q0rUYSVYpy4nr03/NAgiIZ + p5NLj71AS2CxH2/fLvRcLB/Kpi1ePRdNdThuIL1/LrZVW6at5bFpqt3yqXhV/PrLRbEoDk/bP+sN + Pn+/fHf5GhvVJ9zf3VdX5bbC9tXlW2yud4e2OW6rXXvztE/bl1cXb97h4G7dHNqbplxVF2WLA7be + EGm9gPP746Zs3pbNh6q9WSdjxtuoLQfLi+J+29Z3d4cK4X1nrE03Wij9U++S+TcXN5NQbvJBDumH + bdWsl+X3V9XHP36rmw/FF86ucZq8sXhWwcZFj8h1Uz2u6+Ph9aY+pGPnrVUSFsU+6f+03iEUWmSE + upzWu/truKpXCbl9U6WfF0Ee2g7tLjeiLpFqtxqS7bF4kezpHPI3bWYTo03v/L/Z3NeH9psG8/3R + YNT66wZhcVW25Y9NuUuhrttEG7NKeKey4APiY7lZr35O3wcwrT92aTHbGit3q+9W84SF0uLSYnRa + n9qkvC0/FbenngMIdruHLQSXuJTSTyJr7wdRWGfRBrFZdN7FLEZyg4IYMyiIhKxAOtpsl0zQ2S45 + 44ddr2nY9VGyMQrBZWOsQfUsGjJZgUmHQYGEszeGkO2y5yFeDpTivU3dtQKf27o5pOL9faxTJ6Fp + N/XHBENHVB0l4opbGOcMKWs09il9anHKBPExHVoxVsWgjQmm34ghKE8SI0WT9K1jrYi1zRciz88d + ripCxiy6c2BDJAViE/f+cVGUpcGhxSlSGjeYjfJuvA74FXCnkO9z8HZqj7XY6QXGDZWa1+aEmYWU + kdEFM5ECGTICTOyVc+KC5i4FeAgqaBGvpQ9ZB8TgxaVzAF7vq12HbGBSKefoaObSORuU8SPGhJDG + EDvMg5FA3mWMnJkliTqIIichKzjCLJpE5Mg6pe0UZR2VNn2IHchGRbA8DFVLRZh6tJ5UtJMUrZ8p + sIWF9OuNnjGCYzBnMBJYOBcZywGOzsBjRDCDP7XBQACAueznKdCMQDOIxIFbCIRmMDn0CWAVoT4U + h9mjotfsMtBW2Kkp98TSFAbosbJuxMmZGBXHsXJpwKmoRzLYKHgFOghxMrXNIOakRBggdlIBkCgq + VHGIlaNDn00ahR3LrFHYIVhDE6QZtJpTHzwcIyMYiGHSmhgpKg2Eh/X9w9cRfUnUiLJPNYzELxBC + uSfEgjDlDRCNs3JDT5TmMWgHjObdFAUj4zxxMPCUmzIZY1jFaUkwOGcWbQppygtQ0ivw9MwDRr0x + lSZjTGuaDQlUDdU5j4AAi+OnJXiclRoh+TOFgfBjvTmmPxPvLd4Yw90LCHYKhV6EZ5FOtCGkFDuR + 6KyA+U/9NeQU08vUiWRMzLqgbci71oWY7VIY7OIlyKIDva3rRQixt+slItxeV6LJxlwCMV9D9SW7 + EIg+7+LBy9cAWC/enpBzufpr6NXn6cf/jfsfNe7p9pRwrpqmxv86/GfanE6fATjRXqOPCwAA + headers: + Age: + - '1' + Connection: + - keep-alive + Expect-CT: + - max-age=31536000, report-uri="http://csp.yahoo.com/beacon/csp?src=yahoocom-expect-ct-report-only" + Referrer-Policy: + - no-referrer-when-downgrade + Strict-Transport-Security: + - max-age=15552000 + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-encoding: + - gzip + content-length: + - '1137' + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:46 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '3' + x-request-id: + - 9ae00958-740e-49d5-a0ad-03d3bc2c51c9 + x-yahoo-request-id: + - dkpdu9dh3m9n3 + y-rid: + - dkpdu9dh3m9n3 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False].yaml b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False].yaml index 31d00c1f28e..76d64986781 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices0-1d-2022-01-01-2022-02-02-Adj Close-False].yaml @@ -16,30 +16,29 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAO1WTW/jNhD9Lzq7LDnDz70Vm8W2hwZBkwItgrRQYyVx17ZSWc5uGvi/941ExZRT - YG899UIPyeF8vHkz8kt1+1B3ffXupeqa3X4N6fql2jR9LUe3+65rtrfP1bvq58uzalHtnjd/tGts - f/t4efEeB80XvN/eN+f1psHx5fkFDlfbXd/tN822v3p+lOMfzs8+/IKLu1W366+6etmc1T0uvjFM - zkXWWi/g/36/rrsf6+5T01+txJ7xNljvTHCL6n7Tt3d3u6aXZ9bKix5Kf7db8fDh7KqI5ipf5Ki+ - 2zTd6rb+9rz5/PuvbfepOnF2gVvoWXZBRb8YIbnomqdVu9+9X7c7uQzeKxMX1aMof7/aIg5aZISG - 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content-length: - - '1142' + - '1140' content-type: - application/json;charset=utf-8 date: - - Thu, 17 Mar 2022 08:55:52 GMT + - Mon, 21 Mar 2022 11:04:33 GMT server: - ATS vary: @@ -71,13 +70,13 @@ interactions: x-envoy-decorator-operation: - finance-chart-api--mtls-production-ir2.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '3' + - '6' x-request-id: - - 390d6990-6112-45b4-8c52-bc940e4ffa83 + - bee76a40-9284-4470-93e3-2173e64eebbe x-yahoo-request-id: - - e3gguplh35tso + - 6trs4nhh3gmu2 y-rid: - - e3gguplh35tso + - 6trs4nhh3gmu2 status: code: 200 message: OK @@ -98,27 +97,27 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAO1WTW8bNxD9LzqrLDnDz9yKOGh9iGG0LpDCcIuttbbVSFp3tUriGvrvebPL1XLj - FDn11gs1JIfDxzfzZvW8uH2o2m7x6nnR1vvDBtb182Jbd5Us3R7att7dPi1eLX795WyxXOyftn82 - G0x/P393/hoL9Sec393XF9W2xvLF+Vssrnf7rj1s61139fQoy+cXZ2/eYeNu3e67q7Za1WdVhw22 - 3hBpvcTl94dN1b6t2vd1d7WWYMbbYL1jl5aL+23X3N3ta8D7zlgrJzo4/dPsJPybs6sCylXeyJB+ - 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+ headers: + Age: + - '1' + Connection: + - keep-alive + Expect-CT: + - max-age=31536000, report-uri="http://csp.yahoo.com/beacon/csp?src=yahoocom-expect-ct-report-only" + Referrer-Policy: + - no-referrer-when-downgrade + Strict-Transport-Security: + - max-age=15552000 + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-length: + - '1324' + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:46 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-canary-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '4' + x-request-id: + - d9602847-4a3b-4a39-aae0-e955b84af24d + x-yahoo-request-id: + - 4ic7seph3m9n3 + y-rid: + - 4ic7seph3m9n3 + status: + code: 200 + message: OK +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, + like Gecko) Chrome/39.0.2171.95 Safari/537.36 + method: GET + uri: https://query2.finance.yahoo.com/v8/finance/chart/%5EDJI?period1=1591416000&period2=1594094400&interval=1wk&includePrePost=False&events=div%2Csplits + response: + body: + string: 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+ headers: + Age: + - '0' + Connection: + - keep-alive + Expect-CT: + - max-age=31536000, report-uri="http://csp.yahoo.com/beacon/csp?src=yahoocom-expect-ct-report-only" + Referrer-Policy: + - no-referrer-when-downgrade + Strict-Transport-Security: + - max-age=15552000 + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-length: + - '1309' + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:47 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '3' + x-request-id: + - 8d9dedbf-996a-4068-aa06-7c5796886586 + x-yahoo-request-id: + - 506rl8ph3m9n3 + y-rid: + - 506rl8ph3m9n3 + status: + code: 200 + message: OK +- request: + body: null + headers: + Accept: + - '*/*' + 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+ headers: + Age: + - '0' + Connection: + - keep-alive + Expect-CT: + - max-age=31536000, report-uri="http://csp.yahoo.com/beacon/csp?src=yahoocom-expect-ct-report-only" + Referrer-Policy: + - no-referrer-when-downgrade + Strict-Transport-Security: + - max-age=15552000 + X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-length: + - '1337' + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:47 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '2' + x-request-id: + - c28f9c10-4a96-44d7-8da3-2d2dde3e7e8c + x-yahoo-request-id: + - 0keb6f9h3m9n3 + y-rid: + - 0keb6f9h3m9n3 + status: + code: 200 + message: OK +version: 1 diff --git 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SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-encoding: + - gzip + content-length: + - '1331' + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:47 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '3' + x-request-id: + - ab20d561-ad50-4b55-b6bf-a4944aa1442c + x-yahoo-request-id: + - 1j0qoo9h3m9n3 + y-rid: + - 1j0qoo9h3m9n3 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices3-3mo-2010-01-01-2016-02-06-High-True].yaml b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices3-3mo-2010-01-01-2016-02-06-High-True].yaml index 070cb1cf5e4..d7dc6c85aac 100644 --- a/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices3-3mo-2010-01-01-2016-02-06-High-True].yaml +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices[indices3-3mo-2010-01-01-2016-02-06-High-True].yaml @@ -16,30 +16,30 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAO1WTW/bRhD9LzoL252d/ZrcisRtcmhgBC5QIHAA1mIctZLoUlQa19B/75slbS6d - AkFvPfRCLYezszPz3pvVw+rmU9MPqxcPq749nnZYvX9Y7duhUdPNqe/bw8396sXq4ud3q/XqeL// - tdvh9cMPL1+/gaH9gv2H2/Zts29hvvxevbaH49Cf9u1huLq/U/Obt68ufsGHj9v+OFz1zaZ91Qz4 - EDm6lL21a5x+e9o1/U9N/3s7XG01GkWfgo3eh/Xqdj90Hz8eW+THUf0HuPzVHTT6y4urKpOr6cOU - 0cWp7+7a7y6bfntcPTvmst/eaBbRkYlpPbbism8/b7vT8eWuO+IjC0fDvF7dqfPr7QEZuPXUmVLL - 9nB72fbbbqMdu+tb/fkqu+NQujyWhIJRQXvYLA3Pajw/ZfvvI7KP/xzxrjsO3ww37f5mOMTbNEPz - Y98cNM3toEThfad9VijwhuXnZrfdvNP3I8i1og1sQR9UPEf/WJ50j4fTR9AHWX3eD+q8b76srs8j - 7sh1f4dY5IJE57R2cjGx5HGpCbMry8xOJgchCpODZOHSMmIbc5Liy+SzGx3YuSiTA9sQoh+XOViK - 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b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices_returns[indices0-1m-2022-03-18-2022-03-21-Adj Close-True-False].yaml new file mode 100644 index 00000000000..8dd374ef98c --- /dev/null +++ b/tests/gamestonk_terminal/economy/cassettes/test_yfinance_view/test_show_indices_returns[indices0-1m-2022-03-18-2022-03-21-Adj Close-True-False].yaml @@ -0,0 +1,630 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, + like Gecko) Chrome/39.0.2171.95 Safari/537.36 + method: GET + uri: https://query2.finance.yahoo.com/v8/finance/chart/%5EGSPC?period1=1647576000&period2=1647835200&interval=1m&includePrePost=False&events=div%2Csplits + response: + body: + string: !!binary | + H4sIAAAAAAAAAJVd25IcuXH9l3lutwEkrnpzaBWyH7yxYa0j7NigHWNytGKI5KyHQ0nrjf13ZwLo + 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X-Content-Type-Options: + - nosniff + X-Frame-Options: + - SAMEORIGIN + X-XSS-Protection: + - 1; mode=block + cache-control: + - public, max-age=10, stale-while-revalidate=20 + content-encoding: + - gzip + content-type: + - application/json;charset=utf-8 + date: + - Wed, 23 Mar 2022 13:55:47 GMT + server: + - ATS + vary: + - Origin,Accept-Encoding + x-envoy-decorator-operation: + - finance-chart-api--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '15' + x-request-id: + - f2fbaab1-e8cf-40a7-aa48-8428db9c4a44 + x-yahoo-request-id: + - 8cej2d9h3m9n4 + y-rid: + - 8cej2d9h3m9n4 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/economy/csv/test_alphavantage_model/test_get_screener_data.csv b/tests/gamestonk_terminal/economy/csv/test_alphavantage_model/test_get_screener_data.csv index 0923ff612de..0b2ac680ad7 100644 --- a/tests/gamestonk_terminal/economy/csv/test_alphavantage_model/test_get_screener_data.csv +++ b/tests/gamestonk_terminal/economy/csv/test_alphavantage_model/test_get_screener_data.csv @@ -1,12 +1,12 @@ ,Rank A: Real-Time Performance,Rank B: Day Performance,Rank C: Day Performance,Rank D: Month Performance,Rank E: Month Performance,Rank F: Year-to-Date (YTD) Performance,Rank G: Year Performance,Rank H: Year Performance,Rank I: Year Performance,Rank J: Year Performance -Consumer Discretionary,0.0335,0.0335,0.043,-0.055,-0.1303,-0.1426,0.0263,0.579,0.9876999999999999,2.9217 -Information Technology,0.0332,0.0332,0.011399999999999999,-0.0519,-0.1328,-0.134,0.1358,1.0647,1.9346,4.3963 -Communication Services,0.029300000000000003,0.029300000000000003,0.0063,-0.047599999999999996,-0.1479,-0.1517,-0.057699999999999994,0.431,0.31989999999999996,0.7041 -Financials,0.0288,0.0288,0.0394,-0.0458,-0.0141,-0.0196,0.1121,0.4401,0.5548,2.0154 -Materials,0.0158,0.0158,0.0199,-0.014199999999999999,-0.0444,-0.07719999999999999,0.0659,0.5134000000000001,0.5939,1.2396 -Industrials,0.0132,0.0132,0.0227,0.0037,-0.0294,-0.0473,0.0361,0.3657,0.5112,1.5984 -Health Care,0.012199999999999999,0.012199999999999999,0.030299999999999997,0.0258,-0.0294,-0.052199999999999996,0.1618,0.46240000000000003,0.7656000000000001,2.6488 -Real Estate,0.0113,0.0113,0.0052,0.024900000000000002,-0.069,-0.1075,0.18280000000000002,0.31670000000000004,0.504, -Consumer Staples,0.0013,0.0013,0.0084,-0.0222,-0.028900000000000002,-0.0475,0.1293,0.3513,0.3513,1.2124 -Utilities,-0.0017000000000000001,-0.0017000000000000001,0.0132,0.0706,0.0054,-0.0125,0.1253,0.2158,0.373,1.0273999999999999 -Energy,-0.0043,-0.0043,-0.0487,0.048499999999999995,0.3012,0.2784,0.3654,0.11470000000000001,0.0489,-0.0197 +Information Technology,0.0219,0.0219,0.0787,0.008199999999999999,-0.0754,-0.10890000000000001,0.16079999999999997,1.1244,2.0106,4.5517 +Consumer Discretionary,0.0218,0.0218,0.09269999999999999,0.0068000000000000005,-0.0696,-0.1076,0.0626,0.6435,1.0659,3.0967000000000002 +Communication Services,0.0144,0.0144,0.0579,0.0068000000000000005,-0.1164,-0.1315,-0.0462,0.46509999999999996,0.3483,0.7448999999999999 +Health Care,0.006500000000000001,0.006500000000000001,0.062400000000000004,0.0647,-0.0064,-0.030699999999999998,0.1935,0.49560000000000004,0.8317,2.7341 +Materials,0.006,0.006,0.0518,0.0218,-0.0171,-0.0536,0.0928,0.552,0.6367,1.2891 +Industrials,0.004,0.004,0.050199999999999995,0.0352,0.004,-0.030699999999999998,0.0576,0.3895,0.5363,1.6462 +Financials,0.0029,0.0029,0.07139999999999999,-0.0073,0.011899999999999999,-0.0046,0.13449999999999998,0.4621,0.5903,2.0526 +Consumer Staples,0.0027,0.0027,0.0382,-0.0246,-0.0091,-0.0392,0.139,0.363,0.36450000000000005,1.2343000000000002 +Real Estate,0.0021,0.0021,0.0269,0.048600000000000004,-0.0553,-0.0934,0.2036,0.33740000000000003,0.5294, +Energy,0.0009,0.0009,-0.0358,0.0788,0.3697,0.32420000000000004,0.44189999999999996,0.15460000000000002,0.09369999999999999,0.0037 +Utilities,-0.009000000000000001,-0.009000000000000001,0.0052,0.0633,0.011399999999999999,-0.0169,0.1376,0.2104,0.3739,1.0237 diff --git a/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[performance].csv b/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[performance].csv index 5eda082a7e7..50004a24760 100644 --- a/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[performance].csv +++ b/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[performance].csv @@ -1,12 +1,12 @@ ,Name,Perf Week,Perf Month,Perf Quart,Perf Half,Perf Year,Perf YTD,Recom,Avg Volume,Rel Volume,Change,Volume -0,Basic Materials,-0.52%,0.024900000000000002,0.0546,0.0385,0.053399999999999996,0.0097,2.21,558280000.0,1.39,0.019,776750000.0 -1,Communication Services,0.71%,-0.0375,-0.1241,-0.1875,-0.0663,-0.1365,1.88,760940000.0,1.39,0.0374,1060000000.0 -2,Consumer Cyclical,3.67%,-0.06,-0.1375,-0.1334,-0.1426,-0.1547,2.05,1330000000.0,1.5,0.0616,1990000000.0 -3,Consumer Defensive,0.93%,-0.021099999999999997,-0.0245,-0.0049,0.0338,-0.053099999999999994,2.23,452890000.0,1.41,0.0078000000000000005,640390000.0 -4,Energy,-4.35%,-0.0025,0.217,0.2659,0.2949,0.1941,2.23,882680000.0,1.03,0.0033,908760000.0 -5,Financial,3.56%,-0.0341,-0.005699999999999999,-0.0089,0.0466,-0.022400000000000003,2.23,1050000000.0,1.24,0.03,1300000000.0 -6,Healthcare,2.11%,0.0209,-0.0432,-0.0825,0.0072,-0.08220000000000001,2.06,1430000000.0,1.13,0.0196,1620000000.0 -7,Industrials,2.40%,0.0176,-0.0294,-0.0155,0.0137,-0.0579,2.26,670520000.0,1.24,0.0202,832930000.0 -8,Real Estate,0.48%,0.0141,-0.0546,-0.0318,0.066,-0.1041,2.22,355720000.0,1.64,0.0152,585120000.0 -9,Technology,0.79%,-0.0403,-0.1344,-0.11259999999999999,0.0335,-0.16010000000000002,1.98,1640000000.0,1.39,0.0418,2280000000.0 -10,Utilities,0.83%,0.0642,0.027000000000000003,0.0431,0.1125,-0.0111,2.32,160030000.0,1.13,0.0024,181120000.0 +0,Basic Materials,3.03%,0.0337,0.0721,0.12119999999999999,0.1058,0.04190000000000001,2.21,564390000.0,1.7,0.006999999999999999,961360000.0 +1,Communication Services,6.37%,-0.0258,-0.10310000000000001,-0.1558,-0.0248,-0.11449999999999999,1.88,764960000.0,1.78,0.0169,1360000000.0 +2,Consumer Cyclical,10.93%,-0.050499999999999996,-0.0915,-0.10220000000000001,-0.0962,-0.1226,2.06,1340000000.0,1.78,0.0256,2390000000.0 +3,Consumer Defensive,4.75%,-0.0126,-0.0218,0.0161,0.0506,-0.0392,2.23,455720000.0,1.71,0.0063,779270000.0 +4,Energy,-1.82%,0.0352,0.24539999999999998,0.3398,0.3865,0.23190000000000002,2.23,887410000.0,1.38,-0.0003,1230000000.0 +5,Financial,6.90%,-0.031,-0.0012,0.0172,0.07769999999999999,-0.0049,2.23,1050000000.0,1.67,0.0059,1750000000.0 +6,Healthcare,6.69%,0.029900000000000003,-0.0388,-0.0592,0.039900000000000005,-0.0565,2.05,1420000000.0,1.66,0.0086,2360000000.0 +7,Industrials,5.41%,0.0143,-0.0165,0.016399999999999998,0.0458,-0.04,2.26,672710000.0,1.56,0.0062,1050000000.0 +8,Real Estate,2.91%,0.0155,-0.051500000000000004,-0.0096,0.1091,-0.0902,2.22,355260000.0,1.98,0.0031,702840000.0 +9,Technology,8.89%,-0.030600000000000002,-0.0989,-0.0704,0.10300000000000001,-0.1294,1.98,1650000000.0,1.91,0.025099999999999997,3160000000.0 +10,Utilities,0.90%,0.060899999999999996,0.006500000000000001,0.0638,0.11449999999999999,-0.012199999999999999,2.32,161370000.0,2.25,-0.0063,362510000.0 diff --git a/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[valuation].csv b/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[valuation].csv index dd2d2082979..8e770398357 100644 --- a/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[valuation].csv +++ b/tests/gamestonk_terminal/economy/csv/test_finviz_model/test_get_valuation_performance_data[valuation].csv @@ -1,12 +1,12 @@ ,Name,Market Cap,P/E,Fwd P/E,PEG,P/S,P/B,P/C,P/FCF,EPS past 5Y,EPS next 5Y,Sales past 5Y,Change,Volume -0,Basic Materials,2191.01B,10.71,13.62,0.72,1.58,2.23,12.75,19.68,0.3018,0.1491,0.12269999999999999,0.019,776750000.0 -1,Communication Services,7234.11B,20.86,14.79,1.03,3.36,3.18,13.84,33.2,0.2041,0.20309999999999997,0.19879999999999998,0.0374,1060000000.0 -2,Consumer Cyclical,7138.80B,24.93,13.83,1.1,1.06,3.43,9.81,40.02,0.27949999999999997,0.226,0.1972,0.0616,1990000000.0 -3,Consumer Defensive,3683.90B,24.47,20.93,2.66,1.37,4.03,20.07,42.58,0.0843,0.09210000000000002,0.0479,0.0078000000000000005,640390000.0 -4,Energy,3381.60B,12.01,9.33,1.08,0.93,1.22,9.87,14.51,0.2803,0.1116,0.0874,0.0033,908760000.0 -5,Financial,9964.12B,12.64,13.46,1.02,2.91,0.01,0.58,12.57,0.188,0.12359999999999999,0.0727,0.03,1300000000.0 -6,Healthcare,7599.72B,24.3,15.09,2.17,2.17,3.56,12.56,28.67,0.1612,0.1119,0.15810000000000002,0.0196,1620000000.0 -7,Industrials,4889.74B,22.91,16.75,1.53,1.98,3.93,13.49,32.16,0.162,0.1495,0.056299999999999996,0.0202,832930000.0 -8,Real Estate,1758.91B,27.56,30.89,2.75,5.55,1.8,22.97,60.58,0.1045,0.1002,0.1005,0.0152,585120000.0 -9,Technology,13592.54B,29.36,21.83,1.71,4.96,4.37,15.75,34.39,0.24600000000000002,0.172,0.1467,0.0418,2280000000.0 -10,Utilities,1447.17B,26.15,3.29,3.71,1.83,1.42,32.5,66.06,0.013600000000000001,0.0706,0.051399999999999994,0.0024,181120000.0 +0,Basic Materials,2260.70B,11.05,14.06,0.74,1.63,2.3,13.15,20.31,0.30219999999999997,0.1501,0.1231,0.006999999999999999,961360000.0 +1,Communication Services,7417.63B,21.39,15.17,1.05,3.45,3.26,14.19,34.05,0.2059,0.20329999999999998,0.2022,0.0169,1360000000.0 +2,Consumer Cyclical,7410.15B,25.88,14.36,1.14,1.1,3.56,10.19,41.54,0.2838,0.22649999999999998,0.20120000000000002,0.0256,2390000000.0 +3,Consumer Defensive,3737.73B,24.83,21.23,2.68,1.39,4.09,20.36,43.2,0.085,0.0925,0.048600000000000004,0.0063,779270000.0 +4,Energy,3488.00B,12.39,9.63,1.11,0.96,1.26,10.18,14.97,0.2792,0.1112,0.08779999999999999,-0.0003,1230000000.0 +5,Financial,10142.30B,12.87,13.7,1.04,2.96,0.01,0.59,12.79,0.1888,0.124,0.07339999999999999,0.0059,1750000000.0 +6,Healthcare,7812.17B,24.97,15.52,2.22,2.23,3.66,12.91,29.47,0.1617,0.11230000000000001,0.1602,0.0086,2360000000.0 +7,Industrials,4981.60B,23.34,17.07,1.56,2.02,4.0,13.74,32.77,0.1624,0.1492,0.056600000000000004,0.0062,1050000000.0 +8,Real Estate,1786.12B,27.99,31.37,2.79,5.64,1.83,23.32,61.52,0.1051,0.1005,0.1014,0.0031,702840000.0 +9,Technology,14089.76B,30.43,22.63,1.76,5.14,4.53,16.32,35.65,0.248,0.17309999999999998,0.1491,0.025099999999999997,3160000000.0 +10,Utilities,1445.62B,26.13,3.29,3.71,1.82,1.42,32.47,65.99,0.0141,0.0704,0.052000000000000005,-0.0063,362510000.0 diff --git a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_data-kwargs_dict1].csv b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_data-kwargs_dict1].csv index de99d109032..d9a49e642ec 100644 --- a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_data-kwargs_dict1].csv +++ b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_data-kwargs_dict1].csv @@ -574,3 +574,5 @@ 2022-03-11,2.0 2022-03-14,2.14 2022-03-15,2.15 +2022-03-16,2.19 +2022-03-17,2.2 diff --git a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_notes-kwargs_dict0].csv b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_notes-kwargs_dict0].csv index 6b3ae25f4cc..6167069e3ef 100644 --- a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_notes-kwargs_dict0].csv +++ b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_check_output[get_series_notes-kwargs_dict0].csv @@ -1,2873 +1,2829 @@ ,id,realtime_start,realtime_end,title,observation_start,observation_end,frequency,frequency_short,units,units_short,seasonal_adjustment,seasonal_adjustment_short,last_updated,popularity,group_popularity,notes -0,SOFR,2022-03-17,2022-03-17,Secured Overnight Financing Rate,2018-04-03,2022-03-15,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:06-05,78,78,No description provided. -1,TERMCBAUTO48NS,2022-03-17,2022-03-17,"Finance Rate on Consumer Installment Loans at Commercial Banks, New Autos 48 Month Loan",1972-02-01,2021-11-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-01-07 14:02:06-06,56,56,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -2,SOFR30DAYAVG,2022-03-17,2022-03-17,30-Day Average SOFR,2018-05-02,2022-03-16,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:15-05,65,65,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) -3,VAPGDPFI,2022-03-17,2022-03-17,"Value Added by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Finance and Insurance as a Percentage of GDP",2005-01-01,2021-07-01,Quarterly,Q,Percent,%,Not Seasonally Adjusted,NSA,2021-12-22 07:31:03-06,42,42,"Value added represents the sum of the costs-incurred and the incomes-earned in production, and consists of compensation of employees, taxes on production and imports, less subsidies, and gross operating surplus." -4,RIFLPBCIANM60NM,2022-03-17,2022-03-17,"Finance Rate on Consumer Installment Loans at Commercial Banks, New Autos 60 Month Loan",2006-08-01,2021-11-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-01-07 14:02:08-06,49,49,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -5,TERMCBPER24NS,2022-03-17,2022-03-17,"Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan",1972-02-01,2021-11-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-01-07 14:02:10-06,45,45,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -6,DTCTLVENANM,2022-03-17,2022-03-17,Average Amount Financed for New Car Loans at Finance Companies,2008-03-01,2021-12-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2022-03-07 14:07:06-06,29,30,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -7,DTCTLVENANQ,2022-03-17,2022-03-17,Average Amount Financed for New Car Loans at Finance Companies,2008-01-01,2021-07-01,Quarterly,Q,Dollars,$,Not Seasonally Adjusted,NSA,2021-12-17 13:09:05-06,6,30,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." -8,SOFRINDEX,2022-03-17,2022-03-17,SOFR Index,2018-04-02,2022-03-16,Daily,D,"Index Apr 2, 2018 = 1","Index Apr 2, 2018 = 1",Not Seasonally Adjusted,NSA,2022-03-16 07:01:16-05,55,55,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) -9,TERMAFCNCNSA,2022-03-17,2022-03-17,New Car Average Finance Rate at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2012-06-26 12:01:23-05,25,25,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -10,RIELPCFANNM,2022-03-17,2022-03-17,"Average Finance Rate of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-03-01,2021-12-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-03-07 14:07:06-06,19,24,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -11,RIELPCFANNQ,2022-03-17,2022-03-17,"Average Finance Rate of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-07-01,Quarterly,Q,Percent,%,Not Seasonally Adjusted,NSA,2021-12-17 13:10:17-06,10,24,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." -12,SOFR1,2022-03-17,2022-03-17,Secured Overnight Financing Rate: 1st Percentile,2018-04-03,2022-03-15,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:07-05,37,37,No description provided. -13,RIFLPCFANNM,2022-03-17,2022-03-17,New Car Average Finance Rate at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 09:59:18-06,2,25,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -14,SOFR90DAYAVG,2022-03-17,2022-03-17,90-Day Average SOFR,2018-07-02,2022-03-16,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:15-05,49,49,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) -15,SOFRVOL,2022-03-17,2022-03-17,Secured Overnight Financing Volume,2018-04-03,2022-03-15,Daily,D,Billions of U.S. Dollars,Bil. of U.S. $,Not Seasonally Adjusted,NSA,2022-03-16 07:01:10-05,34,34,No description provided. -16,DTCTLVEUANQ,2022-03-17,2022-03-17,Average Amount Financed for Used Car Loans at Finance Companies,2008-01-01,2021-07-01,Quarterly,Q,Dollars,$,Not Seasonally Adjusted,NSA,2021-12-17 13:09:02-06,20,20,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." -17,REVEF52311ALLEST,2022-03-17,2022-03-17,"Total Revenue for Investment Banking and Securities Dealing, All Establishments, Employer Firms",1998-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:05-06,23,23,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -18,HSTFC,2022-03-17,2022-03-17,"Houses Sold by Type of Financing, Cash Purchase",1988-01-01,2021-10-01,Quarterly,Q,Thousands of Units,Thous. of Units,Not Seasonally Adjusted,NSA,2022-01-26 09:01:15-06,26,26,No description provided. -19,DTCTLVENMNM,2022-03-17,2022-03-17,"Average Maturity of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-03-01,2021-12-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2022-03-07 14:07:09-06,15,16,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -20,DTCTLVENMNQ,2022-03-17,2022-03-17,"Average Maturity of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-07-01,Quarterly,Q,Months,Months,Not Seasonally Adjusted,NSA,2021-12-17 13:09:09-06,3,16,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." -21,DDDI03USA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for United States,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:03-05,19,19,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -22,DDDI03PHA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Philippines,1996-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:06-05,19,19,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -23,SOFR180DAYAVG,2022-03-17,2022-03-17,180-Day Average SOFR,2018-10-01,2022-03-16,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:14-05,40,40,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) -24,BOGZ1LM503062003Q,2022-03-17,2022-03-17,"Other Financial Business; Municipal Securities Held by COVID-19 Municipal Liquidity Facility (MLF); Asset, Market Value Levels",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:32:07-06,17,17,"Source ID: LM503062003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM503062003&t=) provided by the source." -25,RIELPCFAUNQ,2022-03-17,2022-03-17,"Average Finance Rate of Used Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-07-01,Quarterly,Q,Percent,%,Not Seasonally Adjusted,NSA,2021-12-17 13:09:04-06,11,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." -26,BOGZ1LM503062003A,2022-03-17,2022-03-17,"Other Financial Business; Municipal Securities Held by COVID-19 Municipal Liquidity Facility (MLF); Asset, Market Value Levels",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:55:34-06,1,17,"Source ID: LM503062003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM503062003&t=) provided by the source." -27,FBTFASQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Total Financial Assets, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:05-06,15,16,"Source ID: FL794090005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794090005&t=) provided by the source." -28,USSTHPI,2022-03-17,2022-03-17,All-Transactions House Price Index for the United States,1975-01-01,2021-10-01,Quarterly,Q,Index 1980:Q1=100,Index 1980 Q1=100,Not Seasonally Adjusted,NSA,2022-02-22 09:03:02-06,76,76,Estimated using sales prices and appraisal data. -29,DTTHFM,2022-03-17,2022-03-17,"Total Loans and Leases Outstanding at Domestic Finance Companies, Level",1970-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-02-17 14:03:01-06,13,13,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -30,FBTFASA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Total Financial Assets, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:03-06,3,16,"Source ID: FL794090005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794090005&t=) provided by the source." -31,QFRTCASHINFUSNO,2022-03-17,2022-03-17,Quarterly Financial Report: U.S. Corporations: All Information: Total Cash on Hand and in U.S. Banks,2009-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-07 09:06:02-06,37,37,"Further information related to the Quarterly Financial Report survey can be found at https://www.census.gov/econ/qfr/about.html -Methodology details can be found at https://www.census.gov/econ/qfr/documents/QFR_Methodology.pdf" -32,DTTHFNM,2022-03-17,2022-03-17,"Total Loans and Leases Outstanding at Domestic Finance Companies, Level",1943-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:14-06,1,13,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -33,REVEF52221ALLEST,2022-03-17,2022-03-17,"Total Revenue for Credit Card Issuing, All Establishments, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:06-06,14,14,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -34,DTCOLNVHFNM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Owned by Finance Companies, Level",1943-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:03-06,11,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -35,DTRTM,2022-03-17,2022-03-17,"Total Real Estate Loans Owned and Securitized by Finance Companies, Level",1970-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-02-17 14:03:04-06,10,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -36,A454RC1Q027SBEA,2022-03-17,2022-03-17,Gross value added of financial corporate business,1947-01-01,2021-07-01,Quarterly,Q,Billions of Dollars,Bil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-02-24 07:57:31-06,13,13,"BEA Account Code: A454RC +0,SOFR,2022-03-21,2022-03-21,Secured Overnight Financing Rate,2018-04-03,2022-03-17,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 07:01:16-05,76,76,No description provided. +1,TERMCBAUTO48NS,2022-03-21,2022-03-21,"Finance Rate on Consumer Installment Loans at Commercial Banks, New Autos 48 Month Loan",1972-02-01,2021-11-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-01-07 14:02:06-06,55,55,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +2,SOFR30DAYAVG,2022-03-21,2022-03-21,30-Day Average SOFR,2018-05-02,2022-03-18,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 07:01:05-05,63,63,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) +3,RIFLPBCIANM60NM,2022-03-21,2022-03-21,"Finance Rate on Consumer Installment Loans at Commercial Banks, New Autos 60 Month Loan",2006-08-01,2021-11-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-01-07 14:02:08-06,49,49,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +4,VAPGDPFI,2022-03-21,2022-03-21,"Value Added by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Finance and Insurance as a Percentage of GDP",2005-01-01,2021-07-01,Quarterly,Q,Percent,%,Not Seasonally Adjusted,NSA,2021-12-22 07:31:03-06,40,40,"Value added represents the sum of the costs-incurred and the incomes-earned in production, and consists of compensation of employees, taxes on production and imports, less subsidies, and gross operating surplus." +5,DTCTLVENANM,2022-03-21,2022-03-21,Average Amount Financed for New Car Loans at Finance Companies,2008-03-01,2021-12-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2022-03-07 14:07:06-06,34,35,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +6,DTCTLVENANQ,2022-03-21,2022-03-21,Average Amount Financed for New Car Loans at Finance Companies,2008-01-01,2021-10-01,Quarterly,Q,Dollars,$,Not Seasonally Adjusted,NSA,2022-03-18 14:48:11-05,8,35,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." +7,TERMCBPER24NS,2022-03-21,2022-03-21,"Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan",1972-02-01,2021-11-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-01-07 14:02:10-06,43,43,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +8,SOFRINDEX,2022-03-21,2022-03-21,SOFR Index,2018-04-02,2022-03-18,Daily,D,"Index Apr 2, 2018 = 1","Index Apr 2, 2018 = 1",Not Seasonally Adjusted,NSA,2022-03-18 07:01:02-05,52,52,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) +9,SOFR1,2022-03-21,2022-03-21,Secured Overnight Financing Rate: 1st Percentile,2018-04-03,2022-03-17,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 07:01:15-05,36,36,No description provided. +10,TERMAFCNCNSA,2022-03-21,2022-03-21,New Car Average Finance Rate at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2012-06-26 12:01:23-05,22,22,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +11,DTCTLVEUANQ,2022-03-21,2022-03-21,Average Amount Financed for Used Car Loans at Finance Companies,2008-01-01,2021-10-01,Quarterly,Q,Dollars,$,Not Seasonally Adjusted,NSA,2022-03-18 14:48:02-05,20,20,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." +12,RIFLPCFANNM,2022-03-21,2022-03-21,New Car Average Finance Rate at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 09:59:18-06,2,22,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +13,RIELPCFANNM,2022-03-21,2022-03-21,"Average Finance Rate of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-03-01,2021-12-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2022-03-07 14:07:06-06,18,19,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +14,SOFR90DAYAVG,2022-03-21,2022-03-21,90-Day Average SOFR,2018-07-02,2022-03-18,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 07:01:04-05,46,46,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) +15,RIELPCFANNQ,2022-03-21,2022-03-21,"Average Finance Rate of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-10-01,Quarterly,Q,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 14:48:04-05,4,19,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." +16,SOFRVOL,2022-03-21,2022-03-21,Secured Overnight Financing Volume,2018-04-03,2022-03-17,Daily,D,Billions of U.S. Dollars,Bil. of U.S. $,Not Seasonally Adjusted,NSA,2022-03-18 07:01:12-05,32,32,No description provided. +17,A454RC1Q027SBEA,2022-03-21,2022-03-21,Gross value added of financial corporate business,1947-01-01,2021-07-01,Quarterly,Q,Billions of Dollars,Bil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-02-24 07:57:31-06,13,20,"BEA Account Code: A454RC For more information about this series, please see http://www.bea.gov/national/." -37,DDAI05HNA156NWDB,2022-03-17,2022-03-17,People 15 Years and Over with Account at a Formal Financial Institution for Honduras,2011-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:26:57-05,13,13,"The percentage of respondents with an account (self or together with someone else) at a bank, credit union, another financial institution (e.g., cooperative, microfinance institution), or the post office (if applicable) including respondents who reported having a debit card (% age 15+). +18,A454RC1A027NBEA,2022-03-21,2022-03-21,Gross value added of financial corporate business,1929-01-01,2020-01-01,Annual,A,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2021-07-29 07:52:21-05,9,20,"BEA Account Code: A454RC + +For more information about this series, please see http://www.bea.gov/national/." +19,REVEF52311ALLEST,2022-03-21,2022-03-21,"Total Revenue for Investment Banking and Securities Dealing, All Establishments, Employer Firms",1998-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:05-06,18,18,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +20,HSTFC,2022-03-21,2022-03-21,"Houses Sold by Type of Financing, Cash Purchase",1988-01-01,2021-10-01,Quarterly,Q,Thousands of Units,Thous. of Units,Not Seasonally Adjusted,NSA,2022-01-26 09:01:15-06,21,21,No description provided. +21,DTCTLVENMNM,2022-03-21,2022-03-21,"Average Maturity of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-03-01,2021-12-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2022-03-07 14:07:09-06,12,13,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +22,DTCTLVENMNQ,2022-03-21,2022-03-21,"Average Maturity of New Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-10-01,Quarterly,Q,Months,Months,Not Seasonally Adjusted,NSA,2022-03-18 14:48:08-05,2,13,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." +23,FBTFASQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Financial Assets, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:05-06,16,17,"Source ID: FL794090005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794090005&t=) provided by the source." +24,USSTHPI,2022-03-21,2022-03-21,All-Transactions House Price Index for the United States,1975-01-01,2021-10-01,Quarterly,Q,Index 1980:Q1=100,Index 1980 Q1=100,Not Seasonally Adjusted,NSA,2022-02-22 09:03:02-06,76,76,Estimated using sales prices and appraisal data. +25,RIELPCFAUNQ,2022-03-21,2022-03-21,"Average Finance Rate of Used Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-10-01,Quarterly,Q,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 14:48:04-05,11,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." +26,FBTFASA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Financial Assets, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:03-06,2,17,"Source ID: FL794090005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794090005&t=) provided by the source." +27,BOGZ1LM503062003Q,2022-03-21,2022-03-21,"Other Financial Business; Municipal Securities Held by COVID-19 Municipal Liquidity Facility (MLF); Asset, Market Value Levels",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:32:07-06,15,16,"Source ID: LM503062003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM503062003&t=) provided by the source." +28,DTTHFM,2022-03-21,2022-03-21,"Total Loans and Leases Outstanding at Domestic Finance Companies, Level",1970-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-03-18 14:48:03-05,11,13,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +29,BOGZ1LM503062003A,2022-03-21,2022-03-21,"Other Financial Business; Municipal Securities Held by COVID-19 Municipal Liquidity Facility (MLF); Asset, Market Value Levels",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:55:34-06,1,16,"Source ID: LM503062003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM503062003&t=) provided by the source." +30,DDDI03USA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for United States,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:03-05,15,15,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +31,REVEF52221ALLEST,2022-03-21,2022-03-21,"Total Revenue for Credit Card Issuing, All Establishments, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:06-06,15,15,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +32,DTTHFNM,2022-03-21,2022-03-21,"Total Loans and Leases Outstanding at Domestic Finance Companies, Level",1943-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:50-05,2,13,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +33,QFRTCASHINFUSNO,2022-03-21,2022-03-21,Quarterly Financial Report: U.S. Corporations: All Information: Total Cash on Hand and in U.S. Banks,2009-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-07 09:06:02-06,36,36,"Further information related to the Quarterly Financial Report survey can be found at https://www.census.gov/econ/qfr/about.html +Methodology details can be found at https://www.census.gov/econ/qfr/documents/QFR_Methodology.pdf" +34,DTCOLNVHFNM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Owned by Finance Companies, Level",1943-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:04-05,11,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +35,SOFR180DAYAVG,2022-03-21,2022-03-21,180-Day Average SOFR,2018-10-01,2022-03-18,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 07:01:05-05,35,35,As an extension of the additional documentation about the Treasury Repo Reference Rates. (https://www.newyorkfed.org/markets/treasury-repo-reference-rates-information) +36,DDDI03PHA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Philippines,1996-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:06-05,13,13,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +37,DDAI05HNA156NWDB,2022-03-21,2022-03-21,People 15 Years and Over with Account at a Formal Financial Institution for Honduras,2011-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:26:57-05,13,13,"The percentage of respondents with an account (self or together with someone else) at a bank, credit union, another financial institution (e.g., cooperative, microfinance institution), or the post office (if applicable) including respondents who reported having a debit card (% age 15+). Source Code: GFDD.AI.05" -38,DDDI12CNA156NWDB,2022-03-17,2022-03-17,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for China,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:11-05,13,13,"Private credit by deposit money banks and other financial institutions to GDP. +38,TERMFCLVRNCNS,2022-03-21,2022-03-21,Loan-to-Value Ratio of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2012-06-26 12:03:12-05,9,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +39,DTCTLVNLNM,2022-03-21,2022-03-21,Loan-to-Value Ratio of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 10:03:20-06,2,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +40,DDDI03ETA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Ethiopia,1998-01-01,2006-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:52:30-05,12,12,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +41,BOGZ1FL614090610Q,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:12-05,8,10,"Source ID: FL614090610.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614090610&t=) provided by the source." +42,BOGZ1FL614090610A,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,3,10,"Source ID: FL614090610.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614090610&t=) provided by the source." +43,CASTHPI,2022-03-21,2022-03-21,All-Transactions House Price Index for California,1975-01-01,2021-10-01,Quarterly,Q,Index 1980:Q1=100,Index 1980 Q1=100,Not Seasonally Adjusted,NSA,2022-02-22 09:03:02-06,71,71,Estimated using sales prices and appraisal data. +44,DTCORVHFNM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Owned by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:03-05,9,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +45,DTRTM,2022-03-21,2022-03-21,"Total Real Estate Loans Owned and Securitized by Finance Companies, Level",1970-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-03-18 14:48:05-05,6,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +46,DTRTNM,2022-03-21,2022-03-21,"Total Real Estate Loans Owned and Securitized by Finance Companies, Level",1970-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:11-05,3,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +47,REVEF52312ALLEST,2022-03-21,2022-03-21,"Total Revenue for Securities Brokerage, All Establishments, Employer Firms",1998-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:06-06,11,11,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +48,FBDSILA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Debt Securities; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:37:05-06,7,11,"Source ID: FL794122005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794122005&t=) provided by the source." +49,DTCTLVEUMNQ,2022-03-21,2022-03-21,"Average Maturity of Used Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-10-01,Quarterly,Q,Months,Months,Not Seasonally Adjusted,NSA,2022-03-18 14:48:04-05,6,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." +50,FBDSILQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Debt Securities; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:08-06,5,11,"Source ID: FL794122005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794122005&t=) provided by the source." +51,DTCTHFNM,2022-03-21,2022-03-21,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Level",1943-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:08-05,6,8,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +52,DTCTHFM,2022-03-21,2022-03-21,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Level",1985-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-03-18 14:48:51-05,3,8,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +53,REVEF52231ALLEST,2022-03-21,2022-03-21,"Total Revenue for Mortgage and Nonmortgage Loan Brokers, All Establishments, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:32-06,10,10,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +54,DDDI12CNA156NWDB,2022-03-21,2022-03-21,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for China,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:11-05,10,10,"Private credit by deposit money banks and other financial institutions to GDP. Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics) Source Code: GFDD.DI.12" -39,DTRTNM,2022-03-17,2022-03-17,"Total Real Estate Loans Owned and Securitized by Finance Companies, Level",1970-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:07-06,2,11,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -40,A454RC1A027NBEA,2022-03-17,2022-03-17,Gross value added of financial corporate business,1929-01-01,2020-01-01,Annual,A,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2021-07-29 07:52:21-05,2,13,"BEA Account Code: A454RC +55,FBCELLQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Corporate Equities; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:06-06,9,10,"Source ID: FL793164105.Q -For more information about this series, please see http://www.bea.gov/national/." -41,DTCORVHFNM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Owned by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:03-06,10,10,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -42,DTCTHFNM,2022-03-17,2022-03-17,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Level",1943-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:04-06,8,10,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -43,DDDI03ETA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Ethiopia,1998-01-01,2006-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:52:30-05,12,12,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793164105&t=) provided by the source." +56,DRFAPGACBN,2022-03-21,2022-03-21,"Delinquency Rate on Loans to Finance Agricultural Production, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Not Seasonally Adjusted,NSA,2022-02-23 15:04:14-06,16,19,No description provided. +57,RIFLPCFAUNM,2022-03-21,2022-03-21,Used Car Average Finance Rate at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 09:59:15-06,6,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +58,FBCELLA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Corporate Equities; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:06-06,1,10,"Source ID: FL793164105.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793164105&t=) provided by the source." +59,DRFAPGACBS,2022-03-21,2022-03-21,"Delinquency Rate on Loans to Finance Agricultural Production, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Seasonally Adjusted,SA,2022-02-23 15:04:23-06,7,19,No description provided. +60,DDDI03EGA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Egypt,1996-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:47-05,9,9,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -44,TERMFCLVRNCNS,2022-03-17,2022-03-17,Loan-to-Value Ratio of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2012-06-26 12:03:12-05,9,10,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -45,DTCTLVNLNM,2022-03-17,2022-03-17,Loan-to-Value Ratio of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 10:03:20-06,3,10,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -46,DTCTHFM,2022-03-17,2022-03-17,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Level",1985-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-02-17 14:03:29-06,3,10,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -47,FBCELLQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Corporate Equities; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:06-06,11,12,"Source ID: FL793164105.Q +61,DRLFRACBS,2022-03-21,2022-03-21,"Delinquency Rate on Lease Financing Receivables, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Seasonally Adjusted,SA,2022-02-23 15:04:14-06,16,18,No description provided. +62,DTCTLVNANM,2022-03-21,2022-03-21,Average Amount Financed for New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2014-03-05 10:03:19-06,3,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +63,DRLFRACBN,2022-03-21,2022-03-21,"Delinquency Rate on Lease Financing Receivables, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Not Seasonally Adjusted,NSA,2022-02-23 15:05:17-06,4,18,No description provided. +64,DTBTNM,2022-03-21,2022-03-21,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:11-05,5,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +65,DTBTM,2022-03-21,2022-03-21,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Level",1985-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-03-18 14:48:55-05,2,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +66,DDDI031WA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for World (DISCONTINUED),1960-01-01,2015-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2017-08-30 08:06:30-05,8,8,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793164105&t=) provided by the source." -48,DRFAPGACBN,2022-03-17,2022-03-17,"Delinquency Rate on Loans to Finance Agricultural Production, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Not Seasonally Adjusted,NSA,2022-02-23 15:04:14-06,19,21,No description provided. -49,FBCELLA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Corporate Equities; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:06-06,1,12,"Source ID: FL793164105.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793164105&t=) provided by the source." -50,DTRTXDFBAA,2022-03-17,2022-03-17,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-02-17 14:03:17-06,7,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -51,DRFAPGACBS,2022-03-17,2022-03-17,"Delinquency Rate on Loans to Finance Agricultural Production, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Seasonally Adjusted,SA,2022-02-23 15:04:23-06,8,21,No description provided. -52,DTRTXDFBANM,2022-03-17,2022-03-17,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:15-06,3,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -53,REVEF52312ALLEST,2022-03-17,2022-03-17,"Total Revenue for Securities Brokerage, All Establishments, Employer Firms",1998-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:06-06,11,11,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -54,DTRTXDFBAM,2022-03-17,2022-03-17,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-02-17 14:03:16-06,1,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -55,DTRTXDFBANA,2022-03-17,2022-03-17,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:16-06,1,9,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -56,DTCTLVULNM,2022-03-17,2022-03-17,Loan-to-Value Ratio of Used Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 10:03:27-06,8,8,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -57,DTBTNM,2022-03-17,2022-03-17,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:06-06,6,8,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -58,DTBTM,2022-03-17,2022-03-17,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Level",1985-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2022-02-17 14:03:09-06,3,8,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -59,CASTHPI,2022-03-17,2022-03-17,All-Transactions House Price Index for California,1975-01-01,2021-10-01,Quarterly,Q,Index 1980:Q1=100,Index 1980 Q1=100,Not Seasonally Adjusted,NSA,2022-02-22 09:03:02-06,70,70,Estimated using sales prices and appraisal data. -60,DTCTLVEUMNQ,2022-03-17,2022-03-17,"Average Maturity of Used Car Loans at Finance Companies, Amount of Finance Weighted",2008-01-01,2021-07-01,Quarterly,Q,Months,Months,Not Seasonally Adjusted,NSA,2021-12-17 13:09:08-06,5,5,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/." -61,STFAFCXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Cash and Cash Equivalents, Flow",2011-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:18-06,5,7,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -62,STFAFCXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Cash and Cash Equivalents, Flow",2011-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:17-06,3,7,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -63,TOTALFC,2022-03-17,2022-03-17,Total Consumer Credit Owned by Finance Companies,1943-01-01,2022-01-01,Monthly,M,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2022-03-07 14:07:12-06,7,7,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -64,BOGZ1FL614090610Q,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:12-05,6,7,"Source ID: FL614090610.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614090610&t=) provided by the source." -65,BOGZ1FL613066223Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:13-05,6,7,"Source ID: FL613066223.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066223&t=) provided by the source." -66,FBDSILQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Debt Securities; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:08-06,8,9,"Source ID: FL794122005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794122005&t=) provided by the source." -67,DRLFRACBS,2022-03-17,2022-03-17,"Delinquency Rate on Lease Financing Receivables, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Seasonally Adjusted,SA,2022-02-23 15:04:14-06,18,18,No description provided. -68,BOGZ1FL614090610A,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,2,7,"Source ID: FL614090610.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614090610&t=) provided by the source." -69,BOGZ1FL613066223A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:45-05,1,7,"Source ID: FL613066223.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066223&t=) provided by the source." -70,RIFLPCFAUNM,2022-03-17,2022-03-17,Used Car Average Finance Rate at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 09:59:15-06,5,5,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -71,MSPTFC,2022-03-17,2022-03-17,"Median Sales Price of Houses Sold by Type of Financing, Cash Purchase",1988-01-01,2021-10-01,Quarterly,Q,Dollars,$,Not Seasonally Adjusted,NSA,2022-01-26 09:01:25-06,12,12,No description provided. -72,HSTFCM,2022-03-17,2022-03-17,"Houses Sold by Type of Financing, Conventional",1988-01-01,2021-10-01,Quarterly,Q,Thousands of Units,Thous. of Units,Not Seasonally Adjusted,NSA,2022-01-26 09:01:22-06,12,12,No description provided. -73,JTU5200QUR,2022-03-17,2022-03-17,Quits: Finance and Insurance,2000-12-01,2022-01-01,Monthly,M,Rate,Rate,Not Seasonally Adjusted,NSA,2022-03-09 09:10:06-06,13,18,No description provided. -74,FBDSILA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Debt Securities; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:37:05-06,2,9,"Source ID: FL794122005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794122005&t=) provided by the source." -75,JTU5200QUL,2022-03-17,2022-03-17,Quits: Finance and Insurance,2000-12-01,2022-01-01,Monthly,M,Level in Thousands,Level in Thous.,Not Seasonally Adjusted,NSA,2022-03-09 09:10:04-06,8,18,No description provided. -76,STFAFCNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Cash and Cash Equivalents, Level",2010-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:09:07-06,6,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -77,DTCOLRHFXDFBANM,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:08-06,3,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -78,TEDRATE,2022-03-17,2022-03-17,TED Spread (DISCONTINUED),1986-01-02,2022-01-21,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-01-28 16:01:02-06,80,80,"Series is calculated as the spread between 3-Month LIBOR based on US dollars (USD3MTD156N) and 3-Month Treasury Bill (DTB3 (https://fred.stlouisfed.org/series/DTB3)). +Source Code: GFDD.DI.03" +67,TEDRATE,2022-03-21,2022-03-21,TED Spread (DISCONTINUED),1986-01-02,2022-01-21,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-01-28 16:01:02-06,79,79,"Series is calculated as the spread between 3-Month LIBOR based on US dollars (USD3MTD156N) and 3-Month Treasury Bill (DTB3 (https://fred.stlouisfed.org/series/DTB3)). Starting with the update on June 21, 2019, the Treasury bond data used in calculating interest rate spreads is obtained directly from the U.S. Treasury Department (https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield). The 3-Month LIBOR based on US Dollars has been removed from FRED (https://news.research.stlouisfed.org/2022/01/ice-benchmark-administration-ltd-iba-data-to-be-removed-from-fred/) as of January 31, 2022, so this calculated series has been discontinued and will no longer be updated. Users interested in calculating a similar credit risk can use the Secured Overnight Financing Rate (SOFR), which has been identified as the rate that represents best practice for use in certain new U.S. Dollar derivatives and other financial contracts. For more details, see the article Transition from LIBOR (https://www.newyorkfed.org/arrc/sofr-transition) from the Alternative Reference Rates Committee (AARC)." -79,DTCOLRHFXDFBANA,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:32-06,1,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -80,DRLFRACBN,2022-03-17,2022-03-17,"Delinquency Rate on Lease Financing Receivables, All Commercial Banks",1987-01-01,2021-10-01,"Quarterly, End of Period",Q,Percent,%,Not Seasonally Adjusted,NSA,2022-02-23 15:05:17-06,1,18,No description provided. -81,REVEF52231ALLEST,2022-03-17,2022-03-17,"Total Revenue for Mortgage and Nonmortgage Loan Brokers, All Establishments, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:32-06,8,8,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -82,DDDI12AUA156NWDB,2022-03-17,2022-03-17,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for Australia,1960-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:54:42-05,8,8,"Private credit by deposit money banks and other financial institutions to GDP. +68,TOTALFC,2022-03-21,2022-03-21,Total Consumer Credit Owned by Finance Companies,1943-01-01,2022-01-01,Monthly,M,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2022-03-07 14:07:12-06,6,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +69,SOFR99,2022-03-21,2022-03-21,Secured Overnight Financing Rate: 99th Percentile,2018-04-03,2022-03-17,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-18 07:01:13-05,17,17,No description provided. +70,B1134C1A027NBEA,2022-03-21,2022-03-21,Monetary interest received: Domestic: Corporate business: Financial,1946-01-01,2020-01-01,Annual,A,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2021-08-06 14:40:26-05,8,8,"BEA Account Code: B1134C -Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics) - -Source Code: GFDD.DI.12" -83,FLREVOLNFCA,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Billions of Dollars, Annual Rate","Bil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:15-06,3,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -84,SOFR99,2022-03-17,2022-03-17,Secured Overnight Financing Rate: 99th Percentile,2018-04-03,2022-03-15,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:09-05,17,17,No description provided. -85,FLREVOLNFC,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Billions of Dollars, Monthly Rate","Bil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:34-06,2,6,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -86,DTCTLVNMNM,2022-03-17,2022-03-17,Weighted-Average Maturity of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2014-03-05 10:03:23-06,4,5,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -87,DDDI03BHA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Bahrain,1991-01-01,2015-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:22:03-05,7,7,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -88,BOGZ1FL713068905Q,2022-03-17,2022-03-17,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:19-05,6,7,"Source ID: FL713068905.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL713068905&t=) provided by the source." -89,CES5552000001,2022-03-17,2022-03-17,"All Employees, Finance and Insurance",1990-01-01,2022-02-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-04 07:44:41-06,16,16,"The series comes from the 'Current Employment Statistics (Establishment Survey).' - -The source code is: CES5552000001" -90,TERMFCWAMNCLNS,2022-03-17,2022-03-17,Weighted-Average Maturity of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2012-06-26 12:01:34-05,1,5,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -91,STFLFDNNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Non-Recourse Debt Associated with Financing Activities, Level",2010-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:32-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -92,BOGZ1FL713068905A,2022-03-17,2022-03-17,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:03-05,1,7,"Source ID: FL713068905.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL713068905&t=) provided by the source." -93,DTCTLNVHFNM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Owned and Securitized by Finance Companies, Level",1989-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:09-06,4,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -94,STFLFLNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Bank Loans",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:09:04-06,4,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -95,DTBOEXDFBANM,2022-03-17,2022-03-17,"Business Equipment Loans and Leases Owned by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:06-06,3,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -96,DTBOEXDFBANA,2022-03-17,2022-03-17,"Business Equipment Loans and Leases Owned by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:56-06,2,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -97,CEU5552000001,2022-03-17,2022-03-17,"All Employees, Finance and Insurance",1990-01-01,2022-02-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-04 07:48:59-06,1,16,"The series comes from the 'Current Employment Statistics (Establishment Survey).' - -The source code is: CEU5552000001" -98,DDDI031WA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for World (DISCONTINUED),1960-01-01,2015-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2017-08-30 08:06:30-05,6,6,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -99,DDDI03THA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Thailand,2007-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:53:15-05,6,6,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -100,DDDI03ZWA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Zimbabwe,1975-01-01,2006-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 13:51:02-05,6,6,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -101,DDDI03MYA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Malaysia (DISCONTINUED),1964-01-01,1994-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2013-05-23 14:17:30-05,6,6,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -102,FINCDSL,2022-03-17,2022-03-17,"Finance Companies; Debt Securities; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:50:11-06,4,4,"Source ID: FL614122005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614122005&t=) provided by the source." -103,FBTALDQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Total Assets (Does Not Include Land), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:08-06,6,6,"Source ID: FL792000095.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792000095&t=) provided by the source." -104,DBNLTRIAOFCNO,2022-03-17,2022-03-17,"Amount Outstanding of Domestic Bonds and Notes in Financial Corporations Sector, Long-Term at Original Maturity, Residence of Issuer in Norway",1995-10-01,2021-07-01,Quarterly,Q,Millions of US Dollars,Mil. of US $,Not Seasonally Adjusted,NSA,2022-03-07 15:54:34-06,6,6,"This series appears in Table 17b. -Source Code: Q:NO:3P:B:1:A:A:A:TO1:K:A:A:A:A:I - -Nationality refers to the ultimate obligor, as opposed to the immediate borrower on a residence basis, and is linked to the consolidation of assets and liabilities for related entities. Information on a nationality basis is useful to analyse potential support that might be available from the parent company and to understand links between borrowers in different countries and sectors. -For example, the debts of a Cayman Islands subsidiary of a Brazilian bank may be guaranteed by the parent bank. Consistent with the approach taken in the international banking statistics, the BIS bases the nationality of an issuer on the residency of its controlling parent, regardless of any intermediate owners. (December 2012, BIS Quarterly Review, https://www.bis.org/publ/qtrpdf/r_qt1212h.pdf) - -Copyright, 2016, Bank for International Settlements (BIS). Terms and conditions of use are available at http://www.bis.org/terms_conditions.htm#Copyright_and_Permissions." -105,DDAI06JPA156NWDB,2022-03-17,2022-03-17,People 15 Years and Over Saving at a Financial Institution in the Past Year for Japan,2011-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:21:33-05,6,6,"The percentage of respondents who report saving or setting aside any money by using an account at a formal financial institution such as a bank, credit union, microfinance institution, or cooperative in the past 12 months (% age 15+). - -Source Code: GFDD.AI.06" -106,REVFPIEF5231YALLEST,2022-03-17,2022-03-17,"Sources of Revenue: Financial Planning and Investment Management Services for Individuals for Investment Banking and Securities Dealing and Brokerage, All Establishments, Employer Firms",2013-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:11-06,6,6,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -107,DDDI12ETA156NWDB,2022-03-17,2022-03-17,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for Ethiopia,1998-01-01,2008-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:52:16-05,6,6,"Private credit by deposit money banks and other financial institutions to GDP. - -Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics) - -Source Code: GFDD.DI.12" -108,FBNWMTA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Net Worth (IMA), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:04-06,5,6,"Source ID: FL792090095.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792090095&t=) provided by the source." -109,BOGZ1FL614122005A,2022-03-17,2022-03-17,"Finance Companies; Debt Securities; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,4,"Source ID: FL614122005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614122005&t=) provided by the source." -110,ALFAPGT100,2022-03-17,2022-03-17,"Loans to Finance Agricultural Production, Banks Ranked 1st to 100th Largest in Size by Assets",1985-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-23 15:04:22-06,15,15,The 100 largest banks are measured by consolidated foreign and domestic assets. -111,FBNWMTQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Net Worth (IMA), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:13-06,2,6,"Source ID: FL792090095.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792090095&t=) provided by the source." -112,DTCTLVNANM,2022-03-17,2022-03-17,Average Amount Financed for New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2014-03-05 10:03:19-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -113,DTCTLVUANM,2022-03-17,2022-03-17,Average Amount Financed for Used Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2014-03-05 10:03:25-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -114,STFLFDNXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Non-Recourse Debt Associated with Financing Activities, Flow",2011-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:33-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -115,STFLFDNXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Non-Recourse Debt Associated with Financing Activities, Flow",2011-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:33-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -116,FBTALDA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Total Assets (Does Not Include Land), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:03-06,1,6,"Source ID: FL792000095.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792000095&t=) provided by the source." -117,REVEF52222ALLEST,2022-03-17,2022-03-17,"Total Revenue for Sales Financing, All Establishments, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:25:42-06,2,2,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -118,DTBTELNM,2022-03-17,2022-03-17,"Business Equipment Loans Owned and Securitized by Finance Companies, Level",1992-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:13-06,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -119,DTROSXDFBANA,2022-03-17,2022-03-17,"One to Four Family Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:19-06,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -120,DTBNELXDFBANA,2022-03-17,2022-03-17,"Business Equipment Loans Securitized by Finance Companies, Flow",1992-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:04-06,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -121,DTRTSNM,2022-03-17,2022-03-17,"One to Four Family Real Estate Owned and Securitized by Finance Companies, Level",1996-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:04-06,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -122,DTROSXDFBANM,2022-03-17,2022-03-17,"One to Four Family Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:18-06,1,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -123,DTBNELXDFBANM,2022-03-17,2022-03-17,"Business Equipment Loans Securitized by Finance Companies, Flow",1992-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:04-06,1,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -124,DDDI03KEA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Kenya,1996-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:22-05,5,5,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -125,DDDI03SGA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Singapore,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:22-05,5,5,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -126,DDDI03IDA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Indonesia,2009-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:53:13-05,5,5,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -127,BOGZ1FL614190005Q,2022-03-17,2022-03-17,"Finance Companies; Total Liabilities, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:15-06,3,3,"Source ID: FL614190005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614190005&t=) provided by the source." -128,BOGZ1FR613066223Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:57-05,3,3,"Source ID: FR613066223.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066223&t=) provided by the source." -129,REVOLNFC,2022-03-17,2022-03-17,Revolving Consumer Credit Owned by Finance Companies,1984-12-01,2022-01-01,Monthly,M,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2022-03-07 14:07:18-06,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -130,FCCFBA,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:50:11-06,3,3,"Source ID: FL613063003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063003&t=) provided by the source." -131,DBNLTRIAOFCZA,2022-03-17,2022-03-17,"Amount Outstanding of Domestic Bonds and Notes in Financial Corporations Sector, Long-Term at Original Maturity, Residence of Issuer in South Africa",2001-01-01,2021-07-01,Quarterly,Q,Millions of US Dollars,Mil. of US $,Not Seasonally Adjusted,NSA,2022-03-07 15:54:33-06,5,5,"This series appears in Table 17b. -Source Code: Q:ZA:3P:B:1:A:A:A:TO1:K:A:A:A:A:I - -Nationality refers to the ultimate obligor, as opposed to the immediate borrower on a residence basis, and is linked to the consolidation of assets and liabilities for related entities. Information on a nationality basis is useful to analyse potential support that might be available from the parent company and to understand links between borrowers in different countries and sectors. -For example, the debts of a Cayman Islands subsidiary of a Brazilian bank may be guaranteed by the parent bank. Consistent with the approach taken in the international banking statistics, the BIS bases the nationality of an issuer on the residency of its controlling parent, regardless of any intermediate owners. (December 2012, BIS Quarterly Review, https://www.bis.org/publ/qtrpdf/r_qt1212h.pdf) - -Copyright, 2016, Bank for International Settlements (BIS). Terms and conditions of use are available at http://www.bis.org/terms_conditions.htm#Copyright_and_Permissions." -132,DDDI12INA156NWDB,2022-03-17,2022-03-17,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for India,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:52:16-05,5,5,"Private credit by deposit money banks and other financial institutions to GDP. - -Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics) - -Source Code: GFDD.DI.12" -133,BOGZ1FL614190005A,2022-03-17,2022-03-17,"Finance Companies; Total Liabilities, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,3,"Source ID: FL614190005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614190005&t=) provided by the source." -134,BOGZ1FR613066223A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,3,"Source ID: FR613066223.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066223&t=) provided by the source." -135,FBCELIQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Corporate Equities; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:35:52-06,4,5,"Source ID: FA793164105.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA793164105&t=) provided by the source." -136,BOGZ1FL613063003A,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:41:10-06,0,3,"Source ID: FL613063003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063003&t=) provided by the source." -137,FBCELIA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Corporate Equities; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:02:15-06,2,5,"Source ID: FA793164105.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA793164105&t=) provided by the source." -138,FLSTHPI,2022-03-17,2022-03-17,All-Transactions House Price Index for Florida,1975-01-01,2021-10-01,Quarterly,Q,Index 1980:Q1=100,Index 1980 Q1=100,Not Seasonally Adjusted,NSA,2022-02-22 09:03:04-06,65,65,Estimated using sales prices and appraisal data. -139,HSTFFHAI,2022-03-17,2022-03-17,"Houses Sold by Type of Financing, FHA Insured",1988-01-01,2021-10-01,Quarterly,Q,Thousands of Units,Thous. of Units,Not Seasonally Adjusted,NSA,2022-01-26 09:01:24-06,8,8,No description provided. -140,TERMFCAAFNCNS,2022-03-17,2022-03-17,Average Amount Financed for New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2012-06-26 12:03:29-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -141,BOGZ1FL613066000Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613066000.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066000&t=) provided by the source." -142,BOGZ1FL613066000A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613066000.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066000&t=) provided by the source." -143,REVEF52222TAXABL,2022-03-17,2022-03-17,"Total Revenue for Sales Financing, Establishments Subject to Federal Income Tax, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:25:42-06,1,1,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -144,BOGZ1FA613066000Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613066000.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066000&t=) provided by the source." -145,BOGZ1FL613066020A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613066020.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066020&t=) provided by the source." -146,EXPEF52222ALLEST,2022-03-17,2022-03-17,"Total Expense for Sales Financing, All Establishments, Employer Firms (DISCONTINUED)",2009-01-01,2012-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2018-12-26 15:52:51-06,1,1,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -147,EXPEF52222TAXABL,2022-03-17,2022-03-17,"Total Expense for Sales Financing, Establishments Subject to Federal Income Tax, Employer Firms (DISCONTINUED)",2009-01-01,2012-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2018-12-26 15:54:38-06,1,1,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -148,BOGZ1FR613066020A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613066020.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066020&t=) provided by the source." -149,BOGZ1FA613066020A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613066020.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066020&t=) provided by the source." -150,BOGZ1FA613066000A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:22:35-05,0,1,"Source ID: FA613066000.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066000&t=) provided by the source." -151,BOGZ1FU613066000A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:11:11-05,0,1,"Source ID: FU613066000.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066000&t=) provided by the source." -152,BOGZ1FU613066000Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,0,1,"Source ID: FU613066000.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066000&t=) provided by the source." -153,BOGZ1FL613066020Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:19:30-05,0,1,"Source ID: FL613066020.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066020&t=) provided by the source." -154,BOGZ1FR613066020Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:15:34-05,0,1,"Source ID: FR613066020.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066020&t=) provided by the source." -155,BOGZ1FA613066020Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:22:35-05,0,1,"Source ID: FA613066020.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066020&t=) provided by the source." -156,BOGZ1FU613066020A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:11:10-05,0,1,"Source ID: FU613066020.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066020&t=) provided by the source." -157,BOGZ1FU613066020Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:11:10-05,0,1,"Source ID: FU613066020.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066020&t=) provided by the source." -158,DTCTLNVHFXDFBANM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Owned and Securitized by Finance Companies, Flow",1989-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:08-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -159,DTBNEXDFBANA,2022-03-17,2022-03-17,"Business Equipment Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:05-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -160,DTBNEXDFBANM,2022-03-17,2022-03-17,"Business Equipment Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:05-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -161,DTTHFABAM,2022-03-17,2022-03-17,Total Owned and Managed Receivables Outstanding Held by Finance Companies,1970-07-01,2021-12-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-02-17 14:03:07-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -162,DTCOLNVHFXDFBANA,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Owned by Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:34-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -163,STFCFXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Equity Capital, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:09:10-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -164,DTBTERXDFBANM,2022-03-17,2022-03-17,"Business Equipment Leases Owned and Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:15-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -165,DTCNLNVHFNM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Securitized by Finance Companies, Level",1989-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:43-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -166,DTROOXDFBANM,2022-03-17,2022-03-17,"Other Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:20-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -167,DTCOLRHFNM,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned by Finance Companies, Level",1984-12-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:32-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -168,DTCNLNVHFXDFBANM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Securitized by Finance Companies, Flow",1989-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:41-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -169,DTRTSXDFBANM,2022-03-17,2022-03-17,"One to Four Family Real Estate Owned and Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:03-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -170,DTCOLNVHFXDFBANM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Owned by Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:33-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -171,STFCFXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Equity Capital, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:32-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -172,DTBOVRXDFBANM,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:49-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -173,DTBOVRXDFBANA,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:49-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -174,DTROOXDFBANA,2022-03-17,2022-03-17,"Other Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:20-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -175,DTCNLNVHFXDFBANA,2022-03-17,2022-03-17,"Consumer Motor Vehicle Loans Securitized by Finance Companies, Flow",1989-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:42-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -176,DTCNRVHFXDFBANA,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:38-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -177,DTCNRVHFXDFBANM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:37-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -178,DDDI03SAA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Saudi Arabia,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:11-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -179,DDDI03KRA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Republic of Korea,1996-01-01,1998-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:47-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -180,DDDI03EGA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Egypt,1996-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:47-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -181,DDDI03BNA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Brunei Darussalam,2006-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:53:40-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. - -Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) - -Source Code: GFDD.DI.03" -182,SMU36935615552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York City, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:34:53-05,2,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36935615552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" - -New York City, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -183,BOGZ1FL613065183Q,2022-03-17,2022-03-17,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:00-05,2,2,"Source ID: FL613065183.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065183&t=) provided by the source." -184,NREVNFC,2022-03-17,2022-03-17,Nonrevolving Consumer Credit Owned by Finance Companies,1943-01-01,2022-01-01,Monthly,M,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2022-03-07 14:07:26-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -185,BOGZ1FA613063045Q,2022-03-17,2022-03-17,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Flow",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-12-16 12:22:29-06,2,2,"Source ID: FA613063045.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613063045&t=) provided by the source." -186,SMS08000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Colorado,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:12-05,2,2,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -187,FLTOTALFC,2022-03-17,2022-03-17,"Total Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Monthly Rate","Bil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:31-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -188,SMS36000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New York,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:38-05,2,2,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -189,SMU36935615552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York City, NY",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:55-05,1,2,"New York City, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -190,SMU36935615552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York City, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:32:20-05,1,2,"New York City, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -191,BOGZ1FL613065183A,2022-03-17,2022-03-17,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,2,"Source ID: FL613065183.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065183&t=) provided by the source." -192,BOGZ1FL614104005A,2022-03-17,2022-03-17,"Finance Companies; Debt Securities and Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,1,2,"Source ID: FL614104005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614104005&t=) provided by the source." -193,BOGZ1FA613063045A,2022-03-17,2022-03-17,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Flow",1946-01-01,2019-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-12-16 12:26:28-06,1,2,"Source ID: FA613063045.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613063045&t=) provided by the source." -194,SMU08000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Colorado,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:35-05,1,2,No description provided. -195,SMU12331005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Miami-Fort Lauderdale-West Palm Beach, FL (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:27-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12331005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -196,SMU12331005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Miami-Fort Lauderdale-West Palm Beach, FL (MSA)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:03-05,1,2,No description provided. -197,SMU12331005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Miami-Fort Lauderdale-West Palm Beach, FL (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:58:16-05,1,2,No description provided. -198,BOGZ1FA613070103Q,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,2,"Source ID: FA613072003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613072003&t=) provided by the source." -199,BOGZ1FA613070103A,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:33-05,1,2,"Source ID: FA613072003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613072003&t=) provided by the source." -200,BOGZ1FU613070103A,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:13-05,1,2,"Source ID: FU613072003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613072003&t=) provided by the source." -201,BOGZ1FU613070103Q,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:48:01-06,1,2,"Source ID: FU613072003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613072003&t=) provided by the source." -202,SMU06417405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Diego-Carlsbad, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:23-05,1,2,No description provided. -203,SMU06417405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Diego-Carlsbad, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:12:10-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06417405552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -204,SMU06417405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Diego-Carlsbad, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:32-05,1,2,No description provided. -205,SMU32298205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Las Vegas-Henderson-Paradise, NV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:41:11-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU32298205552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -206,SMU32298205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Las Vegas-Henderson-Paradise, NV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:20-05,1,2,No description provided. -207,SMU32298205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Las Vegas-Henderson-Paradise, NV (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:57-05,1,2,No description provided. -208,SMU06401405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Riverside-San Bernardino-Ontario, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:05-05,1,2,No description provided. -209,SMU06401405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Riverside-San Bernardino-Ontario, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:43-05,1,2,No description provided. -210,SMU06401405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Riverside-San Bernardino-Ontario, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:13:44-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06401405552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -211,SMU02112605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Anchorage, AK (MSA)",2000-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:19:12-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU02112605552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -212,SMU02112605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Anchorage, AK (MSA)",2000-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:16-05,1,2,No description provided. -213,SMU02112605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Anchorage, AK (MSA)",2000-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:59-05,1,2,No description provided. -214,FLTOTALFCA,2022-03-17,2022-03-17,"Total Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Annual Rate","Bil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:19-06,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -215,SMU25000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Massachusetts,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:36:26-05,1,2,No description provided. -216,SMS25000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Massachusetts,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:30-05,1,2,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -217,SMU25000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Massachusetts,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:40-05,1,2,No description provided. -218,SMU48191005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dallas-Fort Worth-Arlington, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:18:51-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48191005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -219,SMU48191005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dallas-Fort Worth-Arlington, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:19:49-05,1,2,No description provided. -220,SMU48191005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dallas-Fort Worth-Arlington, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:51-05,1,2,No description provided. -221,SMU36000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New York,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:32-05,1,2,No description provided. -222,SMU36000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New York,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:33:34-05,1,2,No description provided. -223,BOGZ1FA703130015Q,2022-03-17,2022-03-17,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:00:49-05,4,4,"Source ID: FA703130015.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA703130015&t=) provided by the source." -224,DMMISTRIAOFCCA,2022-03-17,2022-03-17,"Amount Outstanding of Domestic Money Market instruments in Financial Corporations Sector, Short-Term at Original Maturity, Residence of Issuer in Canada",1989-01-01,2021-07-01,Quarterly,Q,Millions of US Dollars,Mil. of US $,Not Seasonally Adjusted,NSA,2022-03-07 15:51:09-06,4,4,"This series appears in Table 17a. +For more information about this series, please see http://www.bea.gov/national/." +71,DMMISTRIAOFCCA,2022-03-21,2022-03-21,"Amount Outstanding of Domestic Money Market instruments in Financial Corporations Sector, Short-Term at Original Maturity, Residence of Issuer in Canada",1989-01-01,2021-07-01,Quarterly,Q,Millions of US Dollars,Mil. of US $,Not Seasonally Adjusted,NSA,2022-03-07 15:51:09-06,8,8,"This series appears in Table 17a. Source Code: Q:CA:3P:B:1:A:A:A:TO1:C:A:A:A:A:I Nationality refers to the ultimate obligor, as opposed to the immediate borrower on a residence basis, and is linked to the consolidation of assets and liabilities for related entities. Information on a nationality basis is useful to analyse potential support that might be available from the parent company and to understand links between borrowers in different countries and sectors. For example, the debts of a Cayman Islands subsidiary of a Brazilian bank may be guaranteed by the parent bank. Consistent with the approach taken in the international banking statistics, the BIS bases the nationality of an issuer on the residency of its controlling parent, regardless of any intermediate owners. (December 2012, BIS Quarterly Review, https://www.bis.org/publ/qtrpdf/r_qt1212h.pdf) Copyright, 2016, Bank for International Settlements (BIS). Terms and conditions of use are available at http://www.bis.org/terms_conditions.htm#Copyright_and_Permissions." -225,REVICSEF5231YALLEST,2022-03-17,2022-03-17,"Sources of Revenue: Brokering and Dealing Investment Company Securities for Investment Banking and Securities Dealing and Brokerage, All Establishments, Employer Firms",2013-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:21-06,4,4,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." -226,FBLA,2022-03-17,2022-03-17,"Domestic Financial Sectors; Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:38:11-06,4,4,"Source ID: FL794023005.Q +72,REVFPIEF5231YALLEST,2022-03-21,2022-03-21,"Sources of Revenue: Financial Planning and Investment Management Services for Individuals for Investment Banking and Securities Dealing and Brokerage, All Establishments, Employer Firms",2013-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:11-06,8,8,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +73,TERMFCAAFNCNS,2022-03-21,2022-03-21,Average Amount Financed for New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2012-06-26 12:03:29-05,1,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +74,BOGZ1FL613066223Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:13-05,5,5,"Source ID: FL613066223.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794023005&t=) provided by the source." -227,FBGVADA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Gross Value Added (IMA), Transactions",1959-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2021-09-23 11:37:26-05,4,4,"Source ID: FA796902505.A +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066223&t=) provided by the source." +75,FINCDSL,2022-03-21,2022-03-21,"Finance Companies; Debt Securities; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:50:11-06,5,5,"Source ID: FL614122005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA796902505&t=) provided by the source." -228,BOGZ1FL503069003Q,2022-03-17,2022-03-17,"Other Financial Business; Loans to Hedge Funds and Private Domestic Equity Funds from the Term Asset-Backed Securities Loan Facility LLC (TALF II); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:35:45-06,4,4,"Source ID: FL503069003.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614122005&t=) provided by the source." +76,A1100C1A027NBEA,2022-03-21,2022-03-21,Monetary interest paid: Domestic: Corporate business: Financial,1946-01-01,2020-01-01,Annual,A,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2021-08-06 14:41:34-05,7,7,"BEA Account Code: A1100C + +For more information about this series, please see http://www.bea.gov/national/." +77,DDDI12AUA156NWDB,2022-03-21,2022-03-21,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for Australia,1960-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:54:42-05,7,7,"Private credit by deposit money banks and other financial institutions to GDP. + +Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics) + +Source Code: GFDD.DI.12" +78,BOGZ1FL613066223A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:45-05,2,5,"Source ID: FL613066223.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL503069003&t=) provided by the source." -229,BOGZ1FL503063013Q,2022-03-17,2022-03-17,"Other Financial Business; Corporate Bonds and Corporate Bond Focused Exchange-Traded Fund Shares Held by COVID-19 Corporate Credit Facilities (CCF); Asset, Level",1945-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-20 09:35:04-06,4,4,"Source ID: FL503063013.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066223&t=) provided by the source." +79,BOGZ1FL614122005A,2022-03-21,2022-03-21,"Finance Companies; Debt Securities; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,5,"Source ID: FL614122005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614122005&t=) provided by the source." +80,MSPTFC,2022-03-21,2022-03-21,"Median Sales Price of Houses Sold by Type of Financing, Cash Purchase",1988-01-01,2021-10-01,Quarterly,Q,Dollars,$,Not Seasonally Adjusted,NSA,2022-01-26 09:01:25-06,10,10,No description provided. +81,JTU5200QUR,2022-03-21,2022-03-21,Quits: Finance and Insurance,2000-12-01,2022-01-01,Monthly,M,Rate,Rate,Not Seasonally Adjusted,NSA,2022-03-09 09:10:06-06,10,16,No description provided. +82,STFLFDNNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Non-Recourse Debt Associated with Financing Activities, Level",2010-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +83,JTU5200QUL,2022-03-21,2022-03-21,Quits: Finance and Insurance,2000-12-01,2022-01-01,Monthly,M,Level in Thousands,Level in Thous.,Not Seasonally Adjusted,NSA,2022-03-09 09:10:04-06,8,16,No description provided. +84,FLSTHPI,2022-03-21,2022-03-21,All-Transactions House Price Index for Florida,1975-01-01,2021-10-01,Quarterly,Q,Index 1980:Q1=100,Index 1980 Q1=100,Not Seasonally Adjusted,NSA,2022-02-22 09:03:04-06,66,66,Estimated using sales prices and appraisal data. +85,DTCTLNVHFNM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Owned and Securitized by Finance Companies, Level",1989-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,4,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +86,DTCTLVNMNM,2022-03-21,2022-03-21,Weighted-Average Maturity of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2014-03-05 10:03:23-06,4,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +87,DTCTLVULNM,2022-03-21,2022-03-21,Loan-to-Value Ratio of Used Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Percent,%,Not Seasonally Adjusted,NSA,2014-03-05 10:03:27-06,4,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +88,DTRTXDFBAA,2022-03-21,2022-03-21,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-03-18 14:48:05-05,3,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +89,DTRTXDFBANM,2022-03-21,2022-03-21,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:50-05,2,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +90,DTCOLRHFXDFBANM,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:51-05,2,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +91,DTRTXDFBAM,2022-03-21,2022-03-21,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-03-18 14:48:50-05,1,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +92,DTRTXDFBANA,2022-03-21,2022-03-21,"Total Real Estate Loans Owned and Securitized by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:50-05,1,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +93,DTCOLRHFXDFBANA,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:51-05,1,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +94,A09024USA144NNBR,2022-03-21,2022-03-21,"Corporate Earnings, Finance for United States",1916-01-01,1940-01-01,Annual,A,Millions of Dollars,Mil. Of $,Not Seasonally Adjusted,NSA,2012-08-17 15:01:30-05,15,15,"The Data For 1916 Were Computed By NBER By Adding Figures For ""Grand Total Banks And Insurance Companies"" And For ""Real Estate,"" See Crum, ""Corporate Earning Power,"" P. 327. For 1936-1940, The Data Were Computed By NBER From Data In Statistics Of Income (See Notes). The 1938 Data Were Adjusted By NBER To Conform To 1937 Industrial Classification (See Notes). Data For 1939-1940 Are Based On 1938 Standard Industrial Classification. Source: Bureau Of Internal Revenue: Statistics Of Income, 1916 And Successive Years. + +This NBER data series a09024 appears on the NBER website in Chapter 9 at http://www.nber.org/databases/macrohistory/contents/chapter09.html. + +NBER Indicator: a09024" +95,FBTPSPA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Taxes on Production and Imports Less Subsidies, Payable (IMA), Transactions",1959-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:43:26-06,6,6,"Source ID: FA796240101.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA796240101&t=) provided by the source." +96,REVLBGEF522ALLEST,2022-03-21,2022-03-21,"Sources of Revenue: Loan Income from Businesses and Governments for Credit Intermediation and Related Activities, All Establishments, Employer Firms",2013-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:21-06,6,6,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +97,BOGZ1FL713068905Q,2022-03-21,2022-03-21,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:19-05,6,6,"Source ID: FL713068905.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL713068905&t=) provided by the source." +98,FLREVOLNFCA,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Billions of Dollars, Annual Rate","Bil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:15-06,2,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +99,TERMFCWAMNCLNS,2022-03-21,2022-03-21,Weighted-Average Maturity of New Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2012-06-26 12:01:34-05,1,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +100,FLREVOLNFC,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Billions of Dollars, Monthly Rate","Bil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:34-06,1,4,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +101,BOGZ1FL713068905A,2022-03-21,2022-03-21,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:03-05,1,6,"Source ID: FL713068905.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL713068905&t=) provided by the source." +102,DTCTLVUANM,2022-03-21,2022-03-21,Average Amount Financed for Used Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Dollars,$,Not Seasonally Adjusted,NSA,2014-03-05 10:03:25-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +103,STFLFDNXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Non-Recourse Debt Associated with Financing Activities, Flow",2011-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +104,STFLFDNXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Non-Recourse Debt Associated with Financing Activities, Flow",2011-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +105,FBTPSPQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Taxes on Production and Imports Less Subsidies, Payable (IMA), Transactions",1959-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:52:31-06,0,6,"Source ID: FA796240101.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA796240101&t=) provided by the source." +106,REVEF52222ALLEST,2022-03-21,2022-03-21,"Total Revenue for Sales Financing, All Establishments, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:25:42-06,2,2,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +107,STFTFXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Liabilities and Equity Capital, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:16-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +108,STFAFCNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Cash and Cash Equivalents, Level",2010-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:11-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +109,DTCTLNOHFNM,2022-03-21,2022-03-21,"Other Consumer Loans Owned and Securitized by Finance Companies, Level",1989-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +110,STFLFLNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Bank Loans",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:08-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +111,DTBTELNM,2022-03-21,2022-03-21,"Business Equipment Loans Owned and Securitized by Finance Companies, Level",1992-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +112,STFAFXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 17:01:50-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +113,DTCTLRHFXDFBANM,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned and Securitized by Finance Companies, Flow",1985-01-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +114,DTCTRVHFNM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Owned and Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,3,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +115,STFAFCXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Cash and Cash Equivalents, Flow",2011-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:16-05,2,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +116,STFAFCXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Cash and Cash Equivalents, Flow",2011-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,2,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +117,STFTFXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Liabilities and Equity Capital, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +118,STFAFXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,3,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +119,DDDI03MYA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Malaysia (DISCONTINUED),1964-01-01,1994-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2013-05-23 14:17:30-05,5,5,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +120,FBTFATA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Financial Assets, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:02:12-06,5,5,"Source ID: FA794090005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA794090005&t=) provided by the source." +121,REVTDCEF5231YALLEST,2022-03-21,2022-03-21,"Sources of Revenue: Trading Derivative Contracts on Own Account - Net Gains (Losses) for Investment Banking and Securities Dealing and Brokerage, All Establishments, Employer Firms",2013-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:23:25-06,5,5,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +122,FBLIEQQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Liabilities and Equity, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:22-06,5,5,"Source ID: FL794194005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794194005&t=) provided by the source." +123,FBNWMTA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Net Worth (IMA), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:04-06,3,5,"Source ID: FL792090095.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792090095&t=) provided by the source." +124,FBNWMTQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Net Worth (IMA), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:13-06,3,5,"Source ID: FL792090095.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792090095&t=) provided by the source." +125,FBTFATQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Financial Assets, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:52:32-06,1,5,"Source ID: FA794090005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA794090005&t=) provided by the source." +126,FBLIEQA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Liabilities and Equity, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:05-06,1,5,"Source ID: FL794194005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794194005&t=) provided by the source." +127,HSTFCM,2022-03-21,2022-03-21,"Houses Sold by Type of Financing, Conventional",1988-01-01,2021-10-01,Quarterly,Q,Thousands of Units,Thous. of Units,Not Seasonally Adjusted,NSA,2022-01-26 09:01:22-06,8,8,No description provided. +128,BOGZ1FL613066000Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613066000.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066000&t=) provided by the source." +129,BOGZ1FL613066000A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613066000.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066000&t=) provided by the source." +130,REVEF52222TAXABL,2022-03-21,2022-03-21,"Total Revenue for Sales Financing, Establishments Subject to Federal Income Tax, Employer Firms",2009-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-11-24 10:25:42-06,1,1,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +131,BOGZ1FA613066000Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613066000.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066000&t=) provided by the source." +132,BOGZ1FL613066020A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613066020.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066020&t=) provided by the source." +133,EXPEF52222ALLEST,2022-03-21,2022-03-21,"Total Expense for Sales Financing, All Establishments, Employer Firms (DISCONTINUED)",2009-01-01,2012-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2018-12-26 15:52:51-06,1,1,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +134,EXPEF52222TAXABL,2022-03-21,2022-03-21,"Total Expense for Sales Financing, Establishments Subject to Federal Income Tax, Employer Firms (DISCONTINUED)",2009-01-01,2012-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2018-12-26 15:54:38-06,1,1,"For further information, please refer to the US Census Bureau's Annual Services release, online at http://www.census.gov/services/." +135,BOGZ1FR613066020A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613066020.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066020&t=) provided by the source." +136,BOGZ1FA613066020A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613066020.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066020&t=) provided by the source." +137,BOGZ1FA613066000A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:22:35-05,0,1,"Source ID: FA613066000.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066000&t=) provided by the source." +138,BOGZ1FU613066000A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:11:11-05,0,1,"Source ID: FU613066000.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066000&t=) provided by the source." +139,BOGZ1FU613066000Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,0,1,"Source ID: FU613066000.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066000&t=) provided by the source." +140,BOGZ1FL613066020Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:19:30-05,0,1,"Source ID: FL613066020.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066020&t=) provided by the source." +141,BOGZ1FR613066020Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:15:34-05,0,1,"Source ID: FR613066020.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066020&t=) provided by the source." +142,BOGZ1FA613066020Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:22:35-05,0,1,"Source ID: FA613066020.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066020&t=) provided by the source." +143,BOGZ1FU613066020A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:11:10-05,0,1,"Source ID: FU613066020.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066020&t=) provided by the source." +144,BOGZ1FU613066020Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Before 1993); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:11:10-05,0,1,"Source ID: FU613066020.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066020&t=) provided by the source." +145,DTBOEXDFBANM,2022-03-21,2022-03-21,"Business Equipment Loans and Leases Owned by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:17-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +146,DTCNLNVHFNM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Securitized by Finance Companies, Level",1989-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:17-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +147,DTCTLNVHFXDFBANM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Owned and Securitized by Finance Companies, Flow",1989-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +148,STFAFNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Assets",1943-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:16-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +149,DTCOLNVHFXDFBANA,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:17-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +150,STFCFXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Equity Capital, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:06-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +151,DTROSXDFBANA,2022-03-21,2022-03-21,"One to Four Family Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:02-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +152,DTTHFABAM,2022-03-21,2022-03-21,Total Owned and Managed Receivables Outstanding Held by Finance Companies,1970-07-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-03-18 14:48:50-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +153,DTCOLNOHFNM,2022-03-21,2022-03-21,"Other Consumer Loans Owned by Finance Companies, Level",1943-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +154,STFAFGRXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Real Estate Accounts Receivable Excluding Pools of Securitized Real Estate Assets, Flow",1970-07-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 17:01:50-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +155,DTCTLRHFNM,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned and Securitized by Finance Companies, Level",1984-12-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +156,DTRNONM,2022-03-21,2022-03-21,"Other Real Estate Loans Securitized by Finance Companies, Level",1996-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +157,DTBTVRNM,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Owned and Securitized by Finance Companies, Level",1996-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +158,DTRTSNM,2022-03-21,2022-03-21,"One to Four Family Real Estate Owned and Securitized by Finance Companies, Level",1996-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:01-05,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +159,DTBOEXDFBANA,2022-03-21,2022-03-21,"Business Equipment Loans and Leases Owned by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +160,DTCOLNVHFXDFBANM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +161,STFCFXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Total Equity Capital, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +162,DTROSXDFBANM,2022-03-21,2022-03-21,"One to Four Family Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:02-05,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +163,STFAFGRXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Real Estate Accounts Receivable Excluding Pools of Securitized Real Estate Assets, Flow",1970-07-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +164,DDDI03SAA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Saudi Arabia,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:11-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +165,DDDI03THA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Thailand,2007-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:53:15-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +166,DDDI03ZWA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Zimbabwe,1975-01-01,2006-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 13:51:02-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +167,DDDI03KEA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Kenya,1996-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:22-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +168,DDDI03RWA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Rwanda (DISCONTINUED),1969-01-01,2003-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2013-06-04 13:08:33-05,4,4,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. + +Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) + +Source Code: GFDD.DI.03" +169,BOGZ1FL614190005Q,2022-03-21,2022-03-21,"Finance Companies; Total Liabilities, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:15-06,2,2,"Source ID: FL614190005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614190005&t=) provided by the source." +170,REVOLNFC,2022-03-21,2022-03-21,Revolving Consumer Credit Owned by Finance Companies,1984-12-01,2022-01-01,Monthly,M,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2022-03-07 14:07:18-06,2,2,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +171,BOGZ1FR613066223Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:57-05,2,2,"Source ID: FR613066223.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066223&t=) provided by the source." +172,SMS36000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New York,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:38-05,2,2,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +173,FCCFBA,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:50:11-06,2,2,"Source ID: FL613063003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063003&t=) provided by the source." +174,BOGZ1FL614190005A,2022-03-21,2022-03-21,"Finance Companies; Total Liabilities, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,2,"Source ID: FL614190005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614190005&t=) provided by the source." +175,BOGZ1FR613066223A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,2,"Source ID: FR613066223.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066223&t=) provided by the source." +176,SMU36000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New York,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:32-05,1,2,No description provided. +177,SMU36000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New York,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:33:34-05,1,2,No description provided. +178,SMU36935615552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York City, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:34:53-05,1,2,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36935615552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" + +New York City, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +179,SMU36935615552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York City, NY",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:55-05,1,2,"New York City, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +180,SMU36935615552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York City, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:32:20-05,1,2,"New York City, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +181,BOGZ1FL614104005A,2022-03-21,2022-03-21,"Finance Companies; Debt Securities and Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,1,2,"Source ID: FL614104005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614104005&t=) provided by the source." +182,BOGZ1FL613065183Q,2022-03-21,2022-03-21,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:00-05,1,2,"Source ID: FL613065183.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065183&t=) provided by the source." +183,BOGZ1FL613065183A,2022-03-21,2022-03-21,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,2,"Source ID: FL613065183.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065183&t=) provided by the source." +184,USPCEFININS,2022-03-21,2022-03-21,Personal Consumption Expenditures: Services: Financial Services and Insurance for United States,1997-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-10-08 11:11:04-05,4,4,"A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. + +Spending on financial services and insurance. Financial services consist of financial services furnished without payment, and financial service charges, fees, and commissions. Insurance consists of life insurance, net household insurance, net health insurance, net motor vehicle and other transportation insurance.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm." +185,FBTALDQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Assets (Does Not Include Land), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:08-06,4,4,"Source ID: FL792000095.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792000095&t=) provided by the source." +186,BOGZ1FR854090085Q,2022-03-21,2022-03-21,"Other Financial Corporations and Money Market Funds; Revaluation of Debt Securities and Equity and Investment Fund Shares; Asset (IMA), Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:13-06,4,4,"Source ID: FR854090085.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR854090085&t=) provided by the source." +187,FBLSRAQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Loans Including Security Repurchase Agreements; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:41-06,4,4,"Source ID: FL794135005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794135005&t=) provided by the source." +188,BOGZ1FL503063013Q,2022-03-21,2022-03-21,"Other Financial Business; Corporate Bonds and Corporate Bond Focused Exchange-Traded Fund Shares Held by COVID-19 Corporate Credit Facilities (CCF); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 13:02:04-05,4,4,"Source ID: FL503063013.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL503063013&t=) provided by the source." -230,FBFDILQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Foreign Direct Investment in U.S.; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:00:54-05,4,4,"Source ID: FA793192005.Q +189,BOGZ1FL613063003A,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:41:10-06,0,2,"Source ID: FL613063003.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA793192005&t=) provided by the source." -231,DDDI12USA156NWDB,2022-03-17,2022-03-17,Private Credit by Deposit Money Banks and Other Financial Institutions to GDP for United States,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:04-05,4,4,"Private credit by deposit money banks and other financial institutions to GDP. - -Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics) - -Source Code: GFDD.DI.12" -232,BOGZ1FA713068905Q,2022-03-17,2022-03-17,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:05-05,4,4,"Source ID: FA713068905.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA713068905&t=) provided by the source." -233,A1100C1A027NBEA,2022-03-17,2022-03-17,Monetary interest paid: Domestic: Corporate business: Financial,1946-01-01,2020-01-01,Annual,A,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2021-08-06 14:41:34-05,4,4,"BEA Account Code: A1100C - -For more information about this series, please see http://www.bea.gov/national/." -234,USPCEFININS,2022-03-17,2022-03-17,Personal Consumption Expenditures: Services: Financial Services and Insurance for United States,1997-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-10-08 11:11:04-05,4,4,"A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. - -Spending on financial services and insurance. Financial services consist of financial services furnished without payment, and financial service charges, fees, and commissions. Insurance consists of life insurance, net household insurance, net health insurance, net motor vehicle and other transportation insurance.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm." -235,BOGZ1FU613063045A,2022-03-17,2022-03-17,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Flow",1946-01-01,2019-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:23:20-06,0,2,"Source ID: FU613063045.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613063045&t=) provided by the source." -236,BOGZ1FU613063045Q,2022-03-17,2022-03-17,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Flow",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:23:20-06,0,2,"Source ID: FU613063045.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613063045&t=) provided by the source." -237,SMU08000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Colorado,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:55:20-05,0,2,No description provided. -238,FBDSTSQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Debt Securities; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:23-06,3,4,"Source ID: FL794022005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794022005&t=) provided by the source." -239,FBDILNECA,2022-03-17,2022-03-17,"Domestic Financial Sectors; Depository Institution Loans N.E.C.; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:42-06,3,4,"Source ID: FL793068005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793068005&t=) provided by the source." -240,SOFR25,2022-03-17,2022-03-17,Secured Overnight Financing Rate: 25th Percentile,2018-04-03,2022-03-15,Daily,D,Percent,%,Not Seasonally Adjusted,NSA,2022-03-16 07:01:08-05,13,13,No description provided. -241,BOGZ1FA703130015A,2022-03-17,2022-03-17,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:00-05,2,4,"Source ID: FA703130015.A +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063003&t=) provided by the source." +190,BOGZ1FA703130015Q,2022-03-21,2022-03-21,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:00:49-05,3,4,"Source ID: FA703130015.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA703130015&t=) provided by the source." -242,BOGZ1FU703130015Q,2022-03-17,2022-03-17,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:57-05,1,4,"Source ID: FU703130015.Q +191,FBCELIQ027S,2022-03-21,2022-03-21,"Domestic Financial Sectors; Corporate Equities; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:35:52-06,3,4,"Source ID: FA793164105.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA793164105&t=) provided by the source." +192,FBTALDA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Assets (Does Not Include Land), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:03-06,1,4,"Source ID: FL792000095.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL792000095&t=) provided by the source." +193,BOGZ1FR854090085A,2022-03-21,2022-03-21,"Other Financial Corporations and Money Market Funds; Revaluation of Debt Securities and Equity and Investment Fund Shares; Asset (IMA), Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:16-06,1,4,"Source ID: FR854090085.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR854090085&t=) provided by the source." +194,FBLSRAA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Total Loans Including Security Repurchase Agreements; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:04-06,1,4,"Source ID: FL794135005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794135005&t=) provided by the source." +195,BOGZ1FA703130015A,2022-03-21,2022-03-21,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:00-05,1,4,"Source ID: FA703130015.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA703130015&t=) provided by the source." +196,BOGZ1FU703130015Q,2022-03-21,2022-03-21,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:57-05,1,4,"Source ID: FU703130015.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU703130015&t=) provided by the source." -243,BOGZ1FU703130015A,2022-03-17,2022-03-17,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:57-05,1,4,"Source ID: FU703130015.A +197,BOGZ1FU703130015A,2022-03-21,2022-03-21,"Private Depository Institutions; Time and Savings Deposits Due to Other Financial Corporations, State and Local Governments, Nonfinancial Businesses, and Households and Nonprofit Organizations; Liability (SRF), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:57-05,1,4,"Source ID: FU703130015.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU703130015&t=) provided by the source." -244,FBUGAVQ027S,2022-03-17,2022-03-17,"Domestic Financial Sectors; Gross Value Added (IMA), Transactions",1959-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:52:31-06,1,4,"Source ID: FA796902505.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA796902505&t=) provided by the source." -245,BOGZ1FL503069003A,2022-03-17,2022-03-17,"Other Financial Business; Loans to Hedge Funds and Private Domestic Equity Funds from the Term Asset-Backed Securities Loan Facility LLC (TALF II); Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:53-06,1,4,"Source ID: FL503069003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL503069003&t=) provided by the source." -246,BOGZ1FL503063013A,2022-03-17,2022-03-17,"Other Financial Business; Corporate Bonds and Corporate Bond Focused Exchange-Traded Fund Shares Held by COVID-19 Corporate Credit Facilities (CCF); Asset, Level",1945-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-03-19 14:08:39-05,1,4,"Source ID: FL503063013.A +198,BOGZ1FL503063013A,2022-03-21,2022-03-21,"Other Financial Business; Corporate Bonds and Corporate Bond Focused Exchange-Traded Fund Shares Held by COVID-19 Corporate Credit Facilities (CCF); Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 13:02:36-05,1,4,"Source ID: FL503063013.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL503063013&t=) provided by the source." -247,FBFDILA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Foreign Direct Investment in U.S.; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:48-05,1,4,"Source ID: FA793192005.A +199,FBCELIA027N,2022-03-21,2022-03-21,"Domestic Financial Sectors; Corporate Equities; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:02:15-06,1,4,"Source ID: FA793164105.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA793192005&t=) provided by the source." -248,BOGZ1FA713068905A,2022-03-17,2022-03-17,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Transactions",1946-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2021-03-19 14:05:04-05,1,4,"Source ID: FA713068905.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA713068905&t=) provided by the source." -249,BOGZ1FU713068905Q,2022-03-17,2022-03-17,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:56-05,1,4,"Source ID: FU713068905.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU713068905&t=) provided by the source." -250,FBDSTSA027N,2022-03-17,2022-03-17,"Domestic Financial Sectors; Debt Securities; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:05-06,1,4,"Source ID: FL794022005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL794022005&t=) provided by the source." -251,BOGZ1FU713068905A,2022-03-17,2022-03-17,"Monetary Authority; Depository Institution Loans N.E.C. to Other Financial Business COVID-19 Funding, Credit, and Liquidity Facilities; Asset, Transactions",1946-01-01,2020-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-03-19 14:17:48-05,0,4,"Source ID: FU713068905.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU713068905&t=) provided by the source." -252,BOGZ1FR613066000A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:15:34-05,0,0,"Source ID: FR613066000.A +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA793164105&t=) provided by the source." +200,BOGZ1FR613066000A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:15:34-05,0,0,"Source ID: FR613066000.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066000&t=) provided by the source." -253,BOGZ1FR613066000Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:15:34-05,0,0,"Source ID: FR613066000.Q +201,BOGZ1FR613066000Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Including Captive Retail Finance Companies (Since 1992); Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:15:34-05,0,0,"Source ID: FR613066000.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613066000&t=) provided by the source." -254,DTCTRVHFNM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Owned and Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:07-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -255,DTBOVLWNM,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Owned by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:52-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -256,DTBNERNM,2022-03-17,2022-03-17,"Business Equipment Leases Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:04-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -257,DTCTLNOHFNM,2022-03-17,2022-03-17,"Other Consumer Loans Owned and Securitized by Finance Companies, Level",1989-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:09-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -258,DTBNELNM,2022-03-17,2022-03-17,"Business Equipment Loans Securitized by Finance Companies, Level",1992-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:09-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -259,DTRTABAM,2022-03-17,2022-03-17,Total Real Estate Owned and Managed Receivables Outstanding Held by Finance Companies,1970-07-01,2021-12-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-02-17 14:03:18-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -260,DTBOVNM,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Owned by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:50-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -261,STFAFNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Assets",1943-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:25-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -262,DTBOVRNM,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Owned by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:50-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -263,DTBNVLWNM,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Securitized by Finance Companies, Level",1990-08-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:03-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -264,DTRNONM,2022-03-17,2022-03-17,"Other Real Estate Loans Securitized by Finance Companies, Level",1996-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:25-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -265,DTCNRVHFNM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:38-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -266,DTCTHFABAM,2022-03-17,2022-03-17,Total Consumer Owned and Managed Receivables Outstanding Held by Finance Companies,1985-07-01,2021-12-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-02-17 14:03:30-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -267,DTBNVNM,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:03-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -268,DTRTOXDFBANM,2022-03-17,2022-03-17,"Other Real Estate Loans Owned and Securitized by Finance Companies, Flow",2006-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -269,DTCTLRHFXDFBANM,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned and Securitized by Finance Companies, Flow",1985-01-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:08-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -270,STFAFSNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Securities, Level",2010-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:28-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -271,DTBTEXDFBANM,2022-03-17,2022-03-17,"Business Equipment Owned and Securitized by Finance Companies, Flow",2006-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:15-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -272,DTBTVLWXDFBANM,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Owned and Securitized by Finance Companies, Flow",1990-09-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:11-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -273,DTRTONM,2022-03-17,2022-03-17,"Other Real Estate Loans Owned and Securitized by Finance Companies, Level",2006-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -274,DTCTLRHFNM,2022-03-17,2022-03-17,"Revolving Consumer Credit Owned and Securitized by Finance Companies, Level",1984-12-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:08-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -275,DTBTVLRNM,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Owned and Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:12-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -276,DTBOERXDFBANA,2022-03-17,2022-03-17,"Business Equipment Leases Owned by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:57-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -277,DTBOERXDFBANM,2022-03-17,2022-03-17,"Business Equipment Leases Owned by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:57-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -278,STFLFDMXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Notes, Bonds, and Debentures, Flow",1985-07-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:01-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -279,DTBTXDFBAA,2022-03-17,2022-03-17,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-02-17 14:03:47-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -280,DTBTXDFBANM,2022-03-17,2022-03-17,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:45-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -281,DTBTXDFBAM,2022-03-17,2022-03-17,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-02-17 14:03:46-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -282,DTBTXDFBANA,2022-03-17,2022-03-17,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:45-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -283,DTBTELXDFBANM,2022-03-17,2022-03-17,"Business Equipment Loans Owned and Securitized by Finance Companies, Flow",1992-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:14-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -284,DTBTVRNM,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Owned and Securitized by Finance Companies, Level",1996-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:11-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -285,DTBTVLRXDFBANM,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Owned and Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:12-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -286,DTBNVLRNM,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:06-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -287,DTBOENM,2022-03-17,2022-03-17,"Business Equipment Loans and Leases Owned by Finance Companies, Level",1985-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:59-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -288,DTBTVLWNM,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Owned and Securitized by Finance Companies, Level",1990-08-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:12-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -289,DTBNVLRXDFBANA,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:04-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -290,DTBNVLRXDFBANM,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:04-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -291,DTBTVRXDFBANM,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Owned and Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:10-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -292,DTBTVNM,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Owned and Securitized by Finance Companies, Level",2006-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:11-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -293,STFTFXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Liabilities and Equity Capital, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:39-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -294,STFTFXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Liabilities and Equity Capital, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:39-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -295,DTCORVHFXDFBANM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:30-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -296,DTCORVHFXDFBANA,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:31-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -297,DTBNERXDFBANM,2022-03-17,2022-03-17,"Business Equipment Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -298,DTBNERXDFBANA,2022-03-17,2022-03-17,"Business Equipment Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:04-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -299,DTTHFXDFBAA,2022-03-17,2022-03-17,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-02-17 14:03:13-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -300,DTTHFXDFBANM,2022-03-17,2022-03-17,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:10-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -301,DTTHFXDFBAM,2022-03-17,2022-03-17,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1970-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-02-17 14:03:12-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -302,DTTHFXDFBANA,2022-03-17,2022-03-17,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:11-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -303,DTBNOXDFBANA,2022-03-17,2022-03-17,"Other Business Receivables Securitized by Finance Companies, Flow",1991-01-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -304,DTBNOXDFBANM,2022-03-17,2022-03-17,"Other Business Receivables Securitized by Finance Companies, Flow",1991-01-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -305,DTBNENM,2022-03-17,2022-03-17,"Business Equipment Loans and Leases Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -306,STFLFPXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Commercial Paper, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:38-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -307,STFLFPXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Commercial Paper, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:38-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -308,STFAFOXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, All Other Assets and Accounts and Notes Receivable, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:27-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -309,STFCFNQ,2022-03-17,2022-03-17,"Domestic Finance Companies; Capital, Surplus, and Undivided Profits",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:31-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -310,DTCNLRHFNM,2022-03-17,2022-03-17,"Revolving Consumer Credit Securitized by Finance Companies, Level",1997-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:41-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -311,DTBTOXDFBANM,2022-03-17,2022-03-17,"Other Business Loans Owned and Securitized by Finance Companies, Flow",1991-01-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:13-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -312,DTBTONM,2022-03-17,2022-03-17,"Other Business Loans Owned and Securitized by Finance Companies, Level",1990-12-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:15-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -313,DTBTERNM,2022-03-17,2022-03-17,"Business Equipment Leases Owned and Securitized by Finance Companies, Level",1990-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:14-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -314,DTCNLNOHFXDFBANA,2022-03-17,2022-03-17,"Other Consumer Loans Securitized by Finance Companies, Flow",1989-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:44-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -315,DTCNLNOHFXDFBANM,2022-03-17,2022-03-17,"Other Consumer Loans Securitized by Finance Companies, Flow",1989-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:43-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -316,DTBOERNM,2022-03-17,2022-03-17,"Business Equipment Leases Owned by Finance Companies, Level",1985-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:58-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -317,DTCTHFXDFBAA,2022-03-17,2022-03-17,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-02-17 14:03:28-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -318,DTCTHFXDFBANA,2022-03-17,2022-03-17,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:27-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -319,DTCTHFXDFBAM,2022-03-17,2022-03-17,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-02-17 14:03:27-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -320,DTCTHFXDFBANM,2022-03-17,2022-03-17,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:26-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -321,DTCTRVHFXDFBANM,2022-03-17,2022-03-17,"Consumer Motor Vehicle Leases Owned and Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:06-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -322,STFAFUXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Reserves for Unearned Income, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:30-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -323,STFAFUXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Reserves for Unearned Income, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:29-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -324,STFAFGXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:23-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -325,STFAFGXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:23-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -326,DTBTENM,2022-03-17,2022-03-17,"Business Equipment Owned and Securitized by Finance Companies, Level",2006-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-28 14:36:14-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -327,DTCTLNOHFXDFBANM,2022-03-17,2022-03-17,"Other Consumer Loans Owned and Securitized by Finance Companies, Flow",1989-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:09-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -328,DTBTVXDFBANM,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Owned and Securitized by Finance Companies, Flow",2006-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-28 14:36:10-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." -329,DTBOELNM,2022-03-17,2022-03-17,"Business Equipment Loans Owned by Finance Companies, Level",1985-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:01-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -330,DTROSNM,2022-03-17,2022-03-17,"One to Four Family Real Estate Loans Owned by Finance Companies, Level",1970-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:19-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -331,DTRNSXDFBANM,2022-03-17,2022-03-17,"One to Four Family Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:22-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -332,DTRNSXDFBANA,2022-03-17,2022-03-17,"One to Four Family Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:22-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -333,DTRNSNM,2022-03-17,2022-03-17,"One to Four Family Real Estate Loans Securitized by Finance Companies, Level",1996-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:23-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -334,DTBOVLWXDFBANA,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:52-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -335,DTBOVLWXDFBANM,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:51-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -336,DTBNVLWXDFBANM,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-09-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:03-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -337,DTBNVLWXDFBANA,2022-03-17,2022-03-17,"Business Wholesale Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-09-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:03-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -338,DTBNVRXDFBANM,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:02-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -339,DTBNVRXDFBANA,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:02-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -340,DTBOVXDFBANA,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:48-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -341,DTBOVXDFBANM,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:48-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -342,DTCTLVUMNM,2022-03-17,2022-03-17,Weighted-Average Maturity of Used Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2014-03-05 10:03:28-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -343,STFTFNQ,2022-03-17,2022-03-17,Domestic Finance Companies; Total Liabilities and Capital,1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:38-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -344,STFAFXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:30-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -345,DTBTABAM,2022-03-17,2022-03-17,Total Business Owned and Managed Receivables Outstanding Held by Finance Companies,1985-07-01,2021-12-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-02-17 14:03:47-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -346,DTBOVLRNM,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Owned by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:54-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -347,DTBOVLRXDFBANA,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:54-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -348,DTBOVLRXDFBANM,2022-03-17,2022-03-17,"Business Retail Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:53-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -349,DTBNVXDFBANA,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:01-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -350,DTBNVXDFBANM,2022-03-17,2022-03-17,"Business Motor Vehicle Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:01-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -351,DTBNVRNM,2022-03-17,2022-03-17,"Business Motor Vehicle Leases Securitized by Finance Companies, Level",1996-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:03-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -352,DTROONM,2022-03-17,2022-03-17,"Other Real Estate Loans Owned by Finance Companies, Level",1970-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:21-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -353,STFLFDPXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Debt Due to Parent Company, Flow",1988-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:34-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -354,STFLFDPXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Debt Due to Parent Company, Flow",1988-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:35-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -355,STFLFLXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Bank Loans, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:35-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -356,DTCOLNOHFNM,2022-03-17,2022-03-17,"Other Consumer Loans Owned by Finance Companies, Level",1943-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:36-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -357,STFAFGRXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Real Estate Accounts Receivable Excluding Pools of Securitized Real Estate Assets, Flow",1970-07-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:22-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -358,STFLFDONQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Notes, Bonds, and Debentures, Outstanding (DISCONTINUED)",1985-04-01,2014-01-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2014-05-21 11:51:04-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -359,STFLFDPNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Debt Due to Parent",1987-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:34-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -360,STFLFPNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Commercial Paper",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:37-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -361,DTCNLRHFXDFBANA,2022-03-17,2022-03-17,"Revolving Consumer Credit Securitized by Finance Companies, Flow",1997-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:40-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -362,DTCNLRHFXDFBANM,2022-03-17,2022-03-17,"Revolving Consumer Credit Securitized by Finance Companies, Flow",1997-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:39-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -363,DTBOONM,2022-03-17,2022-03-17,"Other Business Receivables Owned by Finance Companies, Level",1980-06-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:56-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -364,DTRNOXDFBANA,2022-03-17,2022-03-17,"Other Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:24-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -365,DTRNOXDFBANM,2022-03-17,2022-03-17,"Other Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:24-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -366,STFLFOXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, All Other Liabilities, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:36-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -367,STFLFOXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, All Other Liabilities, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:37-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -368,STFAFGRNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Real Estate Accounts Receivable Excluding Pools of Securitized Real Estate Assets",1970-04-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:21-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -369,DTBNONM,2022-03-17,2022-03-17,"Other Business Receivables Securitized by Finance Companies, Level",1990-12-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:04:05-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -370,STFAFGNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Accounts Receivable Excluding Pools of Securitized Assets",1943-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:09:08-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -371,DTBOELXDFBANA,2022-03-17,2022-03-17,"Business Equipment Loans Owned by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:04:00-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -372,DTBOELXDFBANM,2022-03-17,2022-03-17,"Business Equipment Loans Owned by Finance Companies, Flow",1985-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:59-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -373,STFAFSXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Securities, Flow",2011-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:56-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -374,STFAFSXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Securities, Flow",2011-01-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:28-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -375,DTCNLNOHFNM,2022-03-17,2022-03-17,"Other Consumer Loans Securitized by Finance Companies, Level",1989-01-01,2021-12-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-02-17 14:03:44-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -376,DTCOLNOHFXDFBANM,2022-03-17,2022-03-17,"Other Consumer Loans Owned by Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:35-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -377,DTCOLNOHFXDFBANA,2022-03-17,2022-03-17,"Other Consumer Loans Owned by Finance Companies, Flow",1943-02-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:35-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -378,STFAFLNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Reserves for Losses",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:24-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -379,STFAFNNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Net Accounts Receivable Excluding Pools of Securitized Assets",1943-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:25-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -380,DTBOOXDFBANA,2022-03-17,2022-03-17,"Other Business Receivables Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:55-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -381,DTBOOXDFBANM,2022-03-17,2022-03-17,"Other Business Receivables Owned by Finance Companies, Flow",1980-07-01,2021-12-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-02-17 14:03:55-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -382,STFAFLXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Reserves for Losses, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:24-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -383,STFAFLXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Reserves for Losses, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:25-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -384,STFLFDOXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Notes, Bonds, and Debentures, Flow (DISCONTINUED)",1985-07-01,2014-01-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2014-05-21 11:51:04-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -385,STFAFGCXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Consumer Accounts Receivable Excluding Pools of Securitized Consumer Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:20-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -386,STFAFGCXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Consumer Accounts Receivable Excluding Pools of Securitized Consumer Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:21-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -387,STFAFGBNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Business Accounts Receivable Excluding Pools of Securitized Business Assets",1980-04-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:18-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -388,STFAFNXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Net Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:26-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -389,STFAFNXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Net Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:26-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -390,STFAFONQ,2022-03-17,2022-03-17,"Domestic Finance Companies, All Other Assets",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:27-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -391,STFAFGBXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Business Accounts Receivable Excluding Pools of Securitized Business Assets, Flow",1980-07-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:19-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -392,STFAFGBXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Business Accounts Receivable Excluding Pools of Securitized Business Assets, Flow",1980-07-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:19-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -393,STFAFUNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Reserves for Unearned Income",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:29-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -394,STFAFGCNQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Consumer Accounts Receivable Excluding Pools of Securitized Consumer Assets",1943-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:20-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -395,STFLFONQ,2022-03-17,2022-03-17,"Domestic Finance Companies, All Other Liabilities",1984-01-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-17 13:10:36-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -396,STFAFOXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, All Other Assets and Accounts and Notes Receivable, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:27-06,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -397,STFAFXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Total Assets, Flow",1943-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:31-06,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -398,STFLFLXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Bank Loans, Flow",1984-04-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:56-06,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -399,STFAFGRXDFBANA,2022-03-17,2022-03-17,"Domestic Finance Companies, Gross Real Estate Accounts Receivable Excluding Pools of Securitized Real Estate Assets, Flow",1970-07-01,2021-07-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2021-12-17 13:10:56-06,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -400,STFLFDOXDFBANQ,2022-03-17,2022-03-17,"Domestic Finance Companies, Notes, Bonds, and Debentures, Flow (DISCONTINUED)",1985-07-01,2014-01-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2014-05-21 11:51:04-05,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." -401,DDDI03BRA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Brazil,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:30-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +202,DTBTERXDFBANM,2022-03-21,2022-03-21,"Business Equipment Leases Owned and Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +203,DTBTVXDFBANM,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Owned and Securitized by Finance Companies, Flow",2006-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +204,DTCNLNVHFXDFBANM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Securitized by Finance Companies, Flow",1989-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +205,DTCNLNVHFXDFBANA,2022-03-21,2022-03-21,"Consumer Motor Vehicle Loans Securitized by Finance Companies, Flow",1989-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +206,DTCOLRHFNM,2022-03-21,2022-03-21,"Revolving Consumer Credit Owned by Finance Companies, Level",1984-12-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +207,DTBOVRXDFBANM,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +208,DTBOVRXDFBANA,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +209,DTRTSXDFBANM,2022-03-21,2022-03-21,"One to Four Family Real Estate Owned and Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:50-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +210,DTBNEXDFBANA,2022-03-21,2022-03-21,"Business Equipment Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +211,DTBNEXDFBANM,2022-03-21,2022-03-21,"Business Equipment Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +212,DTBOVLWNM,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Owned by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +213,DTCNRVHFXDFBANA,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +214,DTCNRVHFXDFBANM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +215,DTBNELXDFBANA,2022-03-21,2022-03-21,"Business Equipment Loans Securitized by Finance Companies, Flow",1992-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:01-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +216,DTBNELXDFBANM,2022-03-21,2022-03-21,"Business Equipment Loans Securitized by Finance Companies, Flow",1992-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +217,DTBOVNM,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Owned by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +218,DTBNERNM,2022-03-21,2022-03-21,"Business Equipment Leases Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +219,DTBNELNM,2022-03-21,2022-03-21,"Business Equipment Loans Securitized by Finance Companies, Level",1992-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:01-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +220,DTRTABAM,2022-03-21,2022-03-21,Total Real Estate Owned and Managed Receivables Outstanding Held by Finance Companies,1970-07-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-03-18 14:49:02-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +221,DTROOXDFBANM,2022-03-21,2022-03-21,"Other Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +222,DTROOXDFBANA,2022-03-21,2022-03-21,"Other Real Estate Loans Owned by Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +223,DTBNVLWNM,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Securitized by Finance Companies, Level",1990-08-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +224,DTCNRVHFNM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +225,DTCTHFABAM,2022-03-21,2022-03-21,Total Consumer Owned and Managed Receivables Outstanding Held by Finance Companies,1985-07-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-03-18 14:48:51-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +226,DTBTXDFBAA,2022-03-21,2022-03-21,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +227,DTBTXDFBANM,2022-03-21,2022-03-21,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +228,DTBTXDFBAM,2022-03-21,2022-03-21,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +229,DTBTXDFBANA,2022-03-21,2022-03-21,"Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +230,DTBNVNM,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +231,DTBOVRNM,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Owned by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +232,DTRTOXDFBANM,2022-03-21,2022-03-21,"Other Real Estate Loans Owned and Securitized by Finance Companies, Flow",2006-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:01-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +233,DTRTONM,2022-03-21,2022-03-21,"Other Real Estate Loans Owned and Securitized by Finance Companies, Level",2006-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:01-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +234,DTBTVLRNM,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Owned and Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +235,DTBTEXDFBANM,2022-03-21,2022-03-21,"Business Equipment Owned and Securitized by Finance Companies, Flow",2006-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +236,DTBTVLWXDFBANM,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Owned and Securitized by Finance Companies, Flow",1990-09-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +237,STFLFDMXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Notes, Bonds, and Debentures, Flow",1985-07-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +238,DTBOENM,2022-03-21,2022-03-21,"Business Equipment Loans and Leases Owned by Finance Companies, Level",1985-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +239,DTBOERXDFBANA,2022-03-21,2022-03-21,"Business Equipment Leases Owned by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +240,DTBOERXDFBANM,2022-03-21,2022-03-21,"Business Equipment Leases Owned by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +241,DTBTELXDFBANM,2022-03-21,2022-03-21,"Business Equipment Loans Owned and Securitized by Finance Companies, Flow",1992-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +242,DTBTVLRXDFBANM,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Owned and Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +243,DTBNVLRNM,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +244,STFAFSNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Securities, Level",2010-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +245,DTTHFXDFBAA,2022-03-21,2022-03-21,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-03-18 14:48:50-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +246,DTTHFXDFBANM,2022-03-21,2022-03-21,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +247,DTTHFXDFBAM,2022-03-21,2022-03-21,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1970-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-03-18 14:48:50-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +248,DTTHFXDFBANA,2022-03-21,2022-03-21,"Total Loans and Leases Outstanding at Domestic Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:50-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +249,DTBNVLRXDFBANA,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +250,DTBNVLRXDFBANM,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +251,DTBTVLWNM,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Owned and Securitized by Finance Companies, Level",1990-08-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +252,DTBTVRXDFBANM,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Owned and Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +253,DTBTVNM,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Owned and Securitized by Finance Companies, Level",2006-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:54-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +254,DTCORVHFXDFBANM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:51-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +255,DTCORVHFXDFBANA,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:51-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +256,DTBNERXDFBANM,2022-03-21,2022-03-21,"Business Equipment Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +257,DTBNERXDFBANA,2022-03-21,2022-03-21,"Business Equipment Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +258,DTBNENM,2022-03-21,2022-03-21,"Business Equipment Loans and Leases Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +259,STFCFNQ,2022-03-21,2022-03-21,"Domestic Finance Companies; Capital, Surplus, and Undivided Profits",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +260,STFAFOXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, All Other Assets and Accounts and Notes Receivable, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:11-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +261,DTBNOXDFBANA,2022-03-21,2022-03-21,"Other Business Receivables Securitized by Finance Companies, Flow",1991-01-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +262,DTBNOXDFBANM,2022-03-21,2022-03-21,"Other Business Receivables Securitized by Finance Companies, Flow",1991-01-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +263,STFLFPXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Commercial Paper, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +264,STFLFPXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Commercial Paper, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +265,DTCTHFXDFBAA,2022-03-21,2022-03-21,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Seasonally Adjusted,SA,2022-03-18 14:48:51-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +266,DTCTHFXDFBANA,2022-03-21,2022-03-21,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:51-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +267,DTCTHFXDFBAM,2022-03-21,2022-03-21,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Seasonally Adjusted,SA,2022-03-18 14:48:51-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +268,DTCTHFXDFBANM,2022-03-21,2022-03-21,"Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:50-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +269,DTBOERNM,2022-03-21,2022-03-21,"Business Equipment Leases Owned by Finance Companies, Level",1985-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +270,DTCNLRHFNM,2022-03-21,2022-03-21,"Revolving Consumer Credit Securitized by Finance Companies, Level",1997-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +271,DTBTOXDFBANM,2022-03-21,2022-03-21,"Other Business Loans Owned and Securitized by Finance Companies, Flow",1991-01-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +272,DTBTONM,2022-03-21,2022-03-21,"Other Business Loans Owned and Securitized by Finance Companies, Level",1990-12-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +273,DTBTERNM,2022-03-21,2022-03-21,"Business Equipment Leases Owned and Securitized by Finance Companies, Level",1990-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +274,DTCNLNOHFXDFBANA,2022-03-21,2022-03-21,"Other Consumer Loans Securitized by Finance Companies, Flow",1989-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +275,DTCNLNOHFXDFBANM,2022-03-21,2022-03-21,"Other Consumer Loans Securitized by Finance Companies, Flow",1989-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +276,STFAFUXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Reserves for Unearned Income, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +277,STFAFUXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Reserves for Unearned Income, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +278,STFAFGXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +279,STFAFGXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +280,DTCTRVHFXDFBANM,2022-03-21,2022-03-21,"Consumer Motor Vehicle Leases Owned and Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +281,DTBOELNM,2022-03-21,2022-03-21,"Business Equipment Loans Owned by Finance Companies, Level",1985-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +282,DTROSNM,2022-03-21,2022-03-21,"One to Four Family Real Estate Loans Owned by Finance Companies, Level",1970-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +283,DTRNSXDFBANM,2022-03-21,2022-03-21,"One to Four Family Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +284,DTRNSXDFBANA,2022-03-21,2022-03-21,"One to Four Family Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +285,DTRNSNM,2022-03-21,2022-03-21,"One to Four Family Real Estate Loans Securitized by Finance Companies, Level",1996-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +286,DTBOVLWXDFBANA,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +287,DTBOVLWXDFBANM,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +288,DTBTENM,2022-03-21,2022-03-21,"Business Equipment Owned and Securitized by Finance Companies, Level",2006-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:55-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +289,DTCTLNOHFXDFBANM,2022-03-21,2022-03-21,"Other Consumer Loans Owned and Securitized by Finance Companies, Flow",1989-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release (http://www.federalreserve.gov/releases/g20/)." +290,DTBNVLWXDFBANM,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-09-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +291,DTBNVLWXDFBANA,2022-03-21,2022-03-21,"Business Wholesale Motor Vehicle Loans Securitized by Finance Companies, Flow",1990-09-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +292,DTBNVRXDFBANM,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +293,DTBNVRXDFBANA,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +294,DTBOVXDFBANA,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +295,DTBOVXDFBANM,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +296,DTCTLVUMNM,2022-03-21,2022-03-21,Weighted-Average Maturity of Used Car Loans at Auto Finance Companies (DISCONTINUED),1971-06-01,2011-01-01,Monthly,M,Months,Months,Not Seasonally Adjusted,NSA,2014-03-05 10:03:28-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +297,DTBTABAM,2022-03-21,2022-03-21,Total Business Owned and Managed Receivables Outstanding Held by Finance Companies,1985-07-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted Annual Rate,SAAR,2022-03-18 14:48:56-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +298,DTBOVLRNM,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Owned by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +299,STFTFNQ,2022-03-21,2022-03-21,Domestic Finance Companies; Total Liabilities and Capital,1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +300,DTBOVLRXDFBANA,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +301,DTBOVLRXDFBANM,2022-03-21,2022-03-21,"Business Retail Motor Vehicle Loans Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +302,DTBNVXDFBANA,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +303,DTBNVXDFBANM,2022-03-21,2022-03-21,"Business Motor Vehicle Loans and Leases Securitized by Finance Companies, Flow",1990-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +304,DTBNVRNM,2022-03-21,2022-03-21,"Business Motor Vehicle Leases Securitized by Finance Companies, Level",1996-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:59-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +305,DTROONM,2022-03-21,2022-03-21,"Other Real Estate Loans Owned by Finance Companies, Level",1970-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +306,STFLFDPXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Debt Due to Parent Company, Flow",1988-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +307,STFLFDPXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Debt Due to Parent Company, Flow",1988-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +308,STFLFLXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Bank Loans, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +309,STFLFDONQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Notes, Bonds, and Debentures, Outstanding (DISCONTINUED)",1985-04-01,2014-01-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2014-05-21 11:51:04-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +310,STFLFDPNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Debt Due to Parent",1987-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:46-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +311,STFLFPNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Commercial Paper",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +312,DTCNLRHFXDFBANA,2022-03-21,2022-03-21,"Revolving Consumer Credit Securitized by Finance Companies, Flow",1997-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +313,DTCNLRHFXDFBANM,2022-03-21,2022-03-21,"Revolving Consumer Credit Securitized by Finance Companies, Flow",1997-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +314,DTBOONM,2022-03-21,2022-03-21,"Other Business Receivables Owned by Finance Companies, Level",1980-06-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +315,DTRNOXDFBANA,2022-03-21,2022-03-21,"Other Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:20-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +316,DTRNOXDFBANM,2022-03-21,2022-03-21,"Other Real Estate Loans Securitized by Finance Companies, Flow",1996-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:49:19-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +317,STFLFOXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, All Other Liabilities, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +318,STFLFOXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, All Other Liabilities, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +319,DTBNONM,2022-03-21,2022-03-21,"Other Business Receivables Securitized by Finance Companies, Level",1990-12-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:49:00-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +320,STFAFGRNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Real Estate Accounts Receivable Excluding Pools of Securitized Real Estate Assets",1970-04-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +321,STFAFGNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Accounts Receivable Excluding Pools of Securitized Assets",1943-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +322,DTBOELXDFBANA,2022-03-21,2022-03-21,"Business Equipment Loans Owned by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +323,DTBOELXDFBANM,2022-03-21,2022-03-21,"Business Equipment Loans Owned by Finance Companies, Flow",1985-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:58-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +324,DTCNLNOHFNM,2022-03-21,2022-03-21,"Other Consumer Loans Securitized by Finance Companies, Level",1989-01-01,2022-01-01,Monthly,M,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:53-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +325,STFAFSXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Securities, Flow",2011-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +326,STFAFSXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Securities, Flow",2011-01-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +327,DTCOLNOHFXDFBANM,2022-03-21,2022-03-21,"Other Consumer Loans Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +328,DTCOLNOHFXDFBANA,2022-03-21,2022-03-21,"Other Consumer Loans Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:52-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +329,STFAFLNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Reserves for Losses",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +330,DTBOOXDFBANA,2022-03-21,2022-03-21,"Other Business Receivables Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +331,DTBOOXDFBANM,2022-03-21,2022-03-21,"Other Business Receivables Owned by Finance Companies, Flow",1980-07-01,2022-01-01,Monthly,M,"Millions of Dollars, Monthly Rate","Mil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:57-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +332,STFAFNNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Net Accounts Receivable Excluding Pools of Securitized Assets",1943-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +333,STFAFLXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Reserves for Losses, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +334,STFAFLXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Reserves for Losses, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:48-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +335,STFLFDOXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Notes, Bonds, and Debentures, Flow (DISCONTINUED)",1985-07-01,2014-01-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2014-05-21 11:51:04-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +336,STFAFGCXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Consumer Accounts Receivable Excluding Pools of Securitized Consumer Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +337,STFAFGCXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Consumer Accounts Receivable Excluding Pools of Securitized Consumer Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +338,STFAFGBNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Business Accounts Receivable Excluding Pools of Securitized Business Assets",1980-04-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +339,STFAFNXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Net Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +340,STFAFNXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Net Accounts Receivable Excluding Pools of Securitized Assets, Flow",1943-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +341,STFAFGBXDFBANA,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Business Accounts Receivable Excluding Pools of Securitized Business Assets, Flow",1980-07-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Annual Rate","Mil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +342,STFAFGBXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Business Accounts Receivable Excluding Pools of Securitized Business Assets, Flow",1980-07-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +343,STFAFONQ,2022-03-21,2022-03-21,"Domestic Finance Companies, All Other Assets",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +344,STFAFUNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Reserves for Unearned Income",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:47-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +345,STFAFGCNQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Gross Consumer Accounts Receivable Excluding Pools of Securitized Consumer Assets",1943-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:49-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +346,STFLFONQ,2022-03-21,2022-03-21,"Domestic Finance Companies, All Other Liabilities",1984-01-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 14:48:45-05,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +347,STFAFOXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, All Other Assets and Accounts and Notes Receivable, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 17:01:50-05,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +348,STFLFLXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Bank Loans, Flow",1984-04-01,2021-10-01,Quarterly,Q,"Millions of Dollars, Quarterly Rate","Mil. of $, Quarterly Rate",Not Seasonally Adjusted,NSA,2022-03-18 17:01:50-05,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +349,STFLFDOXDFBANQ,2022-03-21,2022-03-21,"Domestic Finance Companies, Notes, Bonds, and Debentures, Flow (DISCONTINUED)",1985-07-01,2014-01-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2014-05-21 11:51:04-05,0,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.20 release, online at http://www.federalreserve.gov/releases/g20/ ." +350,DDDI03IDA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Indonesia,2009-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 13:53:13-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -402,DDDI03SEA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Sweden,1996-01-01,1999-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:52:30-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +351,DDDI03ROA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Romania (DISCONTINUED),2008-01-01,2014-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2017-08-29 11:34:14-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -403,DDDI03RWA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Rwanda (DISCONTINUED),1969-01-01,2003-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2013-06-04 13:08:33-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +352,DDDI03EZA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Euro Area (DISCONTINUED),1961-01-01,2007-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2017-08-30 08:06:28-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -404,DDDI03ZAA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for South Africa,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:18-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +353,DDDI03BHA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Bahrain,1991-01-01,2015-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:22:03-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -405,DDDI03JPA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Japan,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:22-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +354,DDDI03SGA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Singapore,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:22-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -406,DDDI03PGA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for Papua New Guinea,2009-01-01,2016-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:21:32-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +355,DDDI03PGA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Papua New Guinea,2009-01-01,2016-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:21:32-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -407,DDDI03FRA156NWDB,2022-03-17,2022-03-17,Non-Bank Financial Institutions' Assets to GDP for France,1961-01-01,1984-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2018-09-21 11:22:36-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. +356,DDDI03BRA156NWDB,2022-03-21,2022-03-21,Non-Bank Financial Institutions' Assets to GDP for Brazil,1996-01-01,2017-01-01,Annual,A,Percent,%,Not Seasonally Adjusted,NSA,2019-10-21 14:50:30-05,3,3,"Total assets held by financial institutions that do not accept transferable deposits but that perform financial intermediation by accepting other types of deposits or by issuing securities or other liabilities that are close substitutes for deposits as a share of GDP. It covers institutions such as saving and mortgage loan institutions, post-office savings institution, building and loan associations, finance companies that accept deposits or deposit substitutes, development banks, and offshore banking institutions. Assets include claims on domestic real nonfinancial sector such as central-, state- and local government, nonfinancial public enterprises and private sector. Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Non-bank financial institutions assets (IFS lines 42, a-d and h); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and annual CPI (IFS line 64..ZF). (International Monetary Fund, International Financial Statistics, and World Bank GDP estimates) Source Code: GFDD.DI.03" -408,SMS06000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in California,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:48-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -409,SMU06000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in California,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:04-05,1,1,No description provided. -410,SMU06000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in California,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:33-05,1,1,No description provided. -411,SMS01000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Alabama,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:11-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -412,SMU01000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Alabama,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:05:04-05,1,1,No description provided. -413,SMU01000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Alabama,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:48:17-05,1,1,No description provided. -414,BOGZ1FA613065123A,2022-03-17,2022-03-17,"Finance Companies; Home Equity Loans; Asset, Transactions",1971-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065123.A +357,NREVNFC,2022-03-21,2022-03-21,Nonrevolving Consumer Credit Owned by Finance Companies,1943-01-01,2022-01-01,Monthly,M,Billions of Dollars,Bil. of $,Not Seasonally Adjusted,NSA,2022-03-07 14:07:26-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +358,SMU12331005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Miami-Fort Lauderdale-West Palm Beach, FL (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:27-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12331005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +359,SMU12331005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Miami-Fort Lauderdale-West Palm Beach, FL (MSA)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:03-05,1,1,No description provided. +360,SMU12331005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Miami-Fort Lauderdale-West Palm Beach, FL (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:58:16-05,1,1,No description provided. +361,FLTOTALFC,2022-03-21,2022-03-21,"Total Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Monthly Rate","Bil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:31-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +362,FLTOTALFCA,2022-03-21,2022-03-21,"Total Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Annual Rate","Bil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:19-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +363,BOGZ1FA613063045Q,2022-03-21,2022-03-21,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Flow",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-12-16 12:22:29-06,1,1,"Source ID: FA613063045.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613063045&t=) provided by the source." +364,BOGZ1FA613063045A,2022-03-21,2022-03-21,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Flow",1946-01-01,2019-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-12-16 12:26:28-06,1,1,"Source ID: FA613063045.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613063045&t=) provided by the source." +365,SMU32298205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Las Vegas-Henderson-Paradise, NV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:41:11-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU32298205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +366,SMU32298205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Las Vegas-Henderson-Paradise, NV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:20-05,1,1,No description provided. +367,SMU32298205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Las Vegas-Henderson-Paradise, NV (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:57-05,1,1,No description provided. +368,BOGZ1FA613070103Q,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613072003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613072003&t=) provided by the source." +369,BOGZ1FA613070103A,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:33-05,1,1,"Source ID: FA613072003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613072003&t=) provided by the source." +370,BOGZ1FU613070103A,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:01:13-05,1,1,"Source ID: FU613072003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613072003&t=) provided by the source." +371,BOGZ1FU613070103Q,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:48:01-06,1,1,"Source ID: FU613072003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613072003&t=) provided by the source." +372,SMU48191005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dallas-Fort Worth-Arlington, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:18:51-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48191005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +373,SMU48191005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dallas-Fort Worth-Arlington, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:19:49-05,1,1,No description provided. +374,SMU48191005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dallas-Fort Worth-Arlington, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:51-05,1,1,No description provided. +375,SMU06417405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Diego-Carlsbad, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:23-05,1,1,No description provided. +376,SMU06417405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Diego-Carlsbad, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:12:10-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06417405552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +377,SMU06417405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Diego-Carlsbad, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:32-05,1,1,No description provided. +378,SMS08000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Colorado,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:12-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +379,SMU08000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Colorado,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:35-05,1,1,No description provided. +380,BOGZ1FA613065123Q,2022-03-21,2022-03-21,"Finance Companies; Home Equity Loans; Asset, Transactions",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:36:19-06,1,1,"Source ID: FA613065123.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065123&t=) provided by the source." -415,BOGZ1FA613065123Q,2022-03-17,2022-03-17,"Finance Companies; Home Equity Loans; Asset, Transactions",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:36:19-06,1,1,"Source ID: FA613065123.Q +381,BOGZ1FA613065123A,2022-03-21,2022-03-21,"Finance Companies; Home Equity Loans; Asset, Transactions",1971-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065123.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065123&t=) provided by the source." -416,BOGZ1FU613065123Q,2022-03-17,2022-03-17,"Finance Companies; Home Equity Loans; Asset, Transactions",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:48:01-06,1,1,"Source ID: FU613065123.Q +382,BOGZ1FU613065123Q,2022-03-21,2022-03-21,"Finance Companies; Home Equity Loans; Asset, Transactions",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:48:01-06,1,1,"Source ID: FU613065123.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613065123&t=) provided by the source." -417,FCLA,2022-03-17,2022-03-17,"Finance Companies; Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:02:12-06,1,1,"Source ID: FL614023005.Q +383,FCLA,2022-03-21,2022-03-21,"Finance Companies; Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:02:12-06,1,1,"Source ID: FL614023005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614023005&t=) provided by the source." -418,BOGZ1FL614023005A,2022-03-17,2022-03-17,"Finance Companies; Loans; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL614023005.A +384,BOGZ1FL614023005A,2022-03-21,2022-03-21,"Finance Companies; Loans; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL614023005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614023005&t=) provided by the source." -419,BOGZ1FL613070103Q,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613072003.Q +385,SMU06401405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Riverside-San Bernardino-Ontario, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:05-05,1,1,No description provided. +386,SMU06401405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Riverside-San Bernardino-Ontario, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:43-05,1,1,No description provided. +387,SMU06401405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Riverside-San Bernardino-Ontario, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:13:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613072003&t=) provided by the source." -420,BOGZ1FL613070103A,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:07-05,1,1,"Source ID: FL613072003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613072003&t=) provided by the source." -421,BOGZ1LM613063003Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds; Asset, Market Value Levels",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:46:50-06,1,1,"Source ID: LM613063003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613063003&t=) provided by the source." -422,BOGZ1LM613063003A,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds; Asset, Market Value Levels",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:55:53-06,1,1,"Source ID: LM613063003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613063003&t=) provided by the source." -423,SMU44772005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Providence-Warwick, RI-MA (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:23:43-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU44772005552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06401405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -424,SMU44772005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Providence-Warwick, RI-MA (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:28-05,1,1,No description provided. -425,BOGZ1FL613063045Q,2022-03-17,2022-03-17,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Level",1945-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:22:09-06,1,1,"Source ID: FL613063045.Q +388,SMU02112605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Anchorage, AK (MSA)",2000-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:19:12-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU02112605552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +389,SMU02112605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Anchorage, AK (MSA)",2000-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:16-05,1,1,No description provided. +390,SMU02112605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Anchorage, AK (MSA)",2000-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:59-05,1,1,No description provided. +391,SMS06000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in California,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:48-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +392,SMU06000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in California,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:04-05,1,1,No description provided. +393,SMU06000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in California,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:33-05,1,1,No description provided. +394,NCBFSCQ027S,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Equity Investment in Finance Company Subsidiaries; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:52:06-06,1,1,"Source ID: FL103094105.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063045&t=) provided by the source." -426,BOGZ1FL613063045A,2022-03-17,2022-03-17,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Level",1945-01-01,2019-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:24:46-06,1,1,"Source ID: FL613063045.A +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103094105&t=) provided by the source." +395,NCBFSCA027N,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Equity Investment in Finance Company Subsidiaries; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:01:58-06,1,1,"Source ID: FL103094105.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063045&t=) provided by the source." -427,SMS21000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Kentucky,2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:41-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -428,SMU21000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Kentucky,2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:51:08-05,1,1,No description provided. -429,SMU21000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Kentucky,2001-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:23-05,1,1,No description provided. -430,BOGZ1FL614090005Q,2022-03-17,2022-03-17,"Finance Companies; Total Financial Assets, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:50-06,1,1,"Source ID: FL614090005.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103094105&t=) provided by the source." +396,SMU25000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Massachusetts,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:36:26-05,1,1,No description provided. +397,SMS25000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Massachusetts,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:30-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +398,SMU25000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Massachusetts,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:40-05,1,1,No description provided. +399,BOGZ1FA613066223Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:00:21-05,1,1,"Source ID: FA613066223.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066223&t=) provided by the source." +400,BOGZ1FA613066223A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:22:35-05,1,1,"Source ID: FA613066223.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066223&t=) provided by the source." +401,BOGZ1FU613066223Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:56-05,1,1,"Source ID: FU613066223.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066223&t=) provided by the source." +402,BOGZ1FU613066223A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU613066223.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066223&t=) provided by the source." +403,BOGZ1FL614090005Q,2022-03-21,2022-03-21,"Finance Companies; Total Financial Assets, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:50-06,1,1,"Source ID: FL614090005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614090005&t=) provided by the source." -431,BOGZ1FA616000105Q,2022-03-17,2022-03-17,"Finance Companies; Gross Saving Including Foreign Earnings Retained Abroad and CCAdj Less Net Capital Transfers Paid, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA616000105.Q +404,BOGZ1LM613063003Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds; Asset, Market Value Levels",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:46:50-06,1,1,"Source ID: LM613063003.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616000105&t=) provided by the source." -432,BOGZ1FU616000105Q,2022-03-17,2022-03-17,"Finance Companies; Gross Saving Including Foreign Earnings Retained Abroad and CCAdj Less Net Capital Transfers Paid, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:24:31-06,1,1,"Source ID: FU616000105.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613063003&t=) provided by the source." +405,BOGZ1LM613063003A,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds; Asset, Market Value Levels",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:55:53-06,1,1,"Source ID: LM613063003.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU616000105&t=) provided by the source." -433,NCBFSCQ027S,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Equity Investment in Finance Company Subsidiaries; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:52:06-06,1,1,"Source ID: FL103094105.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613063003&t=) provided by the source." +406,SMU44772005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Providence-Warwick, RI-MA (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:23:43-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103094105&t=) provided by the source." -434,NCBFSCA027N,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Equity Investment in Finance Company Subsidiaries; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:01:58-06,1,1,"Source ID: FL103094105.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103094105&t=) provided by the source." -435,BOGZ1FA613066223Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:00:21-05,1,1,"Source ID: FA613066223.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066223&t=) provided by the source." -436,BOGZ1FA613066223A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:22:35-05,1,1,"Source ID: FA613066223.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613066223&t=) provided by the source." -437,BOGZ1FU613066223Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:00:56-05,1,1,"Source ID: FU613066223.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066223&t=) provided by the source." -438,BOGZ1FU613066223A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit, Student Loans Held by Sallie Mae; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU613066223.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613066223&t=) provided by the source." -439,SMU08178205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Colorado Springs, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:10:12-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU08178205552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU44772005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -440,SMU08178205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Colorado Springs, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:07-05,1,1,No description provided. -441,SMU08178205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Colorado Springs, CO (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:07-05,1,1,No description provided. -442,SMU37167405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Charlotte-Concord-Gastonia, NC-SC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:33:46-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +407,SMU44772005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Providence-Warwick, RI-MA (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:28-05,1,1,No description provided. +408,BOGZ1FL613070103Q,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613072003.Q -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU37167405552000001). +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -443,SMU37167405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Charlotte-Concord-Gastonia, NC-SC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:12-05,1,1,No description provided. -444,SMU37167405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Charlotte-Concord-Gastonia, NC-SC (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:06-05,1,1,No description provided. -445,SMU06310805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Anaheim, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:18-05,1,1,No description provided. -446,SMU06310805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Anaheim, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:52-05,1,1,No description provided. -447,SMU06310805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Anaheim, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06310805552000001). +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613072003&t=) provided by the source." +409,BOGZ1FL613070103A,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:07-05,1,1,"Source ID: FL613072003.A -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -448,BOGZ1FL613065105Q,2022-03-17,2022-03-17,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:10-06,1,1,"Source ID: FL613065105.Q +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613072003&t=) provided by the source." +410,BOGZ1FL613065105Q,2022-03-21,2022-03-21,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:34:10-06,1,1,"Source ID: FL613065105.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065105&t=) provided by the source." -449,BOGZ1FL613065105A,2022-03-17,2022-03-17,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:37:08-06,1,1,"Source ID: FL613065105.A +411,BOGZ1FL613065105A,2022-03-21,2022-03-21,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:37:08-06,1,1,"Source ID: FL613065105.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065105&t=) provided by the source." -450,BOGZ1FL613163083A,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163083.A +412,BOGZ1FL613063045Q,2022-03-21,2022-03-21,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Level",1945-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:22:09-06,1,1,"Source ID: FL613063045.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163083&t=) provided by the source." -451,BOGZ1FL613163083Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163083.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063045&t=) provided by the source." +413,BOGZ1FL613063045A,2022-03-21,2022-03-21,"Finance Companies; Corporate Bonds Issued by Nonfinancial Corporate Businesses; Asset, Level",1945-01-01,2019-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:24:46-06,1,1,"Source ID: FL613063045.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163083&t=) provided by the source." -452,BOGZ1FU613065000Q,2022-03-17,2022-03-17,"Finance Companies; Total Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:32:15-06,1,1,"Source ID: FU613065000.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613063045&t=) provided by the source." +414,SMS01000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Alabama,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:11-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +415,SMU01000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Alabama,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:05:04-05,1,1,No description provided. +416,SMU01000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Alabama,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:48:17-05,1,1,No description provided. +417,BOGZ1FU613065000Q,2022-03-21,2022-03-21,"Finance Companies; Total Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:32:15-06,1,1,"Source ID: FU613065000.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613065000&t=) provided by the source." -453,BOGZ1FA613065000Q,2022-03-17,2022-03-17,"Finance Companies; Total Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:15-06,1,1,"Source ID: FA613065000.Q +418,BOGZ1FA613065000Q,2022-03-21,2022-03-21,"Finance Companies; Total Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:15-06,1,1,"Source ID: FA613065000.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065000&t=) provided by the source." -454,BOGZ1FA613065000A,2022-03-17,2022-03-17,"Finance Companies; Total Mortgages; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065000.A +419,BOGZ1FA613065000A,2022-03-21,2022-03-21,"Finance Companies; Total Mortgages; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065000.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065000&t=) provided by the source." -455,SMU53426445552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Seattle-Bellevue-Everett, WA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:12:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +420,SMU37167405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Charlotte-Concord-Gastonia, NC-SC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:33:46-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU37167405552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +421,SMU37167405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Charlotte-Concord-Gastonia, NC-SC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:12-05,1,1,No description provided. +422,SMU37167405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Charlotte-Concord-Gastonia, NC-SC (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:06-05,1,1,No description provided. +423,BOGZ1FL613163083A,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163083.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163083&t=) provided by the source." +424,BOGZ1FL613163083Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163083.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163083&t=) provided by the source." +425,SMS21000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Kentucky,2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:41-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +426,SMU21000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Kentucky,2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:51:08-05,1,1,No description provided. +427,SMU21000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Kentucky,2001-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:23-05,1,1,No description provided. +428,SMU53426445552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Seattle-Bellevue-Everett, WA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:12:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU53426445552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -456,SMU53426445552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Seattle-Bellevue-Everett, WA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:27-05,1,1,No description provided. -457,SMU53426445552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Seattle-Bellevue-Everett, WA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:56-05,1,1,No description provided. -458,FLNREVNFCA,2022-03-17,2022-03-17,"Nonrevolving Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Annual Rate","Bil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:20-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -459,FLNREVNFC,2022-03-17,2022-03-17,"Nonrevolving Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Monthly Rate","Bil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:39-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." -460,BOGZ1FU615013103Q,2022-03-17,2022-03-17,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:07-05,1,1,"Source ID: FU615013103.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU615013103&t=) provided by the source." -461,BOGZ1FA615013103Q,2022-03-17,2022-03-17,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:11-05,1,1,"Source ID: FA615013103.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615013103&t=) provided by the source." -462,BOGZ1FU615013103A,2022-03-17,2022-03-17,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:11-05,1,1,"Source ID: FU615013103.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU615013103&t=) provided by the source." -463,BOGZ1FA615013103A,2022-03-17,2022-03-17,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:11-05,1,1,"Source ID: FA615013103.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615013103&t=) provided by the source." -464,BOGZ1FL613069500Q,2022-03-17,2022-03-17,"Finance Companies; Nonfinancial Business Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613069500.Q +429,SMU53426445552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Seattle-Bellevue-Everett, WA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:27-05,1,1,No description provided. +430,SMU53426445552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Seattle-Bellevue-Everett, WA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:56-05,1,1,No description provided. +431,FLNREVNFCA,2022-03-21,2022-03-21,"Nonrevolving Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Annual Rate","Bil. of $, Annual Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:20-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +432,FLNREVNFC,2022-03-21,2022-03-21,"Nonrevolving Consumer Credit Owned by Finance Companies, Flow",1943-02-01,2022-01-01,Monthly,M,"Billions of Dollars, Monthly Rate","Bil. of $, Monthly Rate",Not Seasonally Adjusted,NSA,2022-03-07 14:07:39-06,1,1,"For further information, please refer to the Board of Governors of the Federal Reserve System's G.19 release, online at http://www.federalreserve.gov/releases/g19/." +433,BOGZ1FL613069500Q,2022-03-21,2022-03-21,"Finance Companies; Nonfinancial Business Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613069500.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613069500&t=) provided by the source." -465,BOGZ1FL615080003A,2022-03-17,2022-03-17,"Finance Companies; Equity Capital, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:19:27-05,1,1,"Source ID: FL615080003.A +434,BOGZ1FA616000105Q,2022-03-21,2022-03-21,"Finance Companies; Gross Saving Including Foreign Earnings Retained Abroad and CCAdj Less Net Capital Transfers Paid, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA616000105.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL615080003&t=) provided by the source." -466,BOGZ1FL615080003Q,2022-03-17,2022-03-17,"Finance Companies; Equity Capital, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,1,1,"Source ID: FL615080003.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616000105&t=) provided by the source." +435,BOGZ1FU616000105Q,2022-03-21,2022-03-21,"Finance Companies; Gross Saving Including Foreign Earnings Retained Abroad and CCAdj Less Net Capital Transfers Paid, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:24:31-06,1,1,"Source ID: FU616000105.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL615080003&t=) provided by the source." -467,BOGZ1FL613065000Q,2022-03-17,2022-03-17,"Finance Companies; Total Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613065000.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU616000105&t=) provided by the source." +436,BOGZ1FL613065000Q,2022-03-21,2022-03-21,"Finance Companies; Total Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613065000.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065000&t=) provided by the source." -468,SMU22000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Louisiana,1996-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:53:45-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +437,SMU22000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Louisiana,1996-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:53:45-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU22000005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -469,SMU22000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Louisiana,1996-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:37:52-05,1,1,No description provided. -470,SMU22000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Louisiana,1996-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:42-05,1,1,No description provided. -471,SMS72000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Puerto Rico,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:21-05,1,1,No description provided. -472,SMU72000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Puerto Rico,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:11:29-05,1,1,No description provided. -473,SMU72000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Puerto Rico,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:20:07-05,1,1,No description provided. -474,BOGZ1FA103169535A,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:46:41-06,1,1,"Source ID: FA103169535.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA103169535&t=) provided by the source." -475,BOGZ1FA103169535Q,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:57:56-06,1,1,"Source ID: FA103169535.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA103169535&t=) provided by the source." -476,BOGZ1FA613093043A,2022-03-17,2022-03-17,"Finance Companies; Unidentified Miscellaneous Assets Issued by Nonfinancial Corporate Businesses; Asset, Flow",1945-01-01,1945-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-05-11 14:22:19-05,1,1,"Source ID: FA613093043.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613093043&t=) provided by the source." -477,BOGZ1FU614090610A,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU614090610.A +438,SMU22000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Louisiana,1996-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:37:52-05,1,1,No description provided. +439,SMU22000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Louisiana,1996-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:42-05,1,1,No description provided. +440,BOGZ1FU614090610A,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU614090610.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU614090610&t=) provided by the source." -478,BOGZ1FA614090610Q,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA614090610.Q +441,BOGZ1FA614090610Q,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA614090610.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA614090610&t=) provided by the source." -479,BOGZ1FA614090610A,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA614090610.A +442,BOGZ1FA614090610A,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA614090610.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA614090610&t=) provided by the source." -480,BOGZ1FU614090610Q,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU614090610.Q +443,BOGZ1FU614090610Q,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU614090610.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU614090610&t=) provided by the source." -481,BOGZ1FL613169100Q,2022-03-17,2022-03-17,"Finance Companies; Commercial Paper; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613169100.Q +444,SMU06310805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Anaheim, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:18-05,1,1,No description provided. +445,SMU06310805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Anaheim, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:52-05,1,1,No description provided. +446,SMU06310805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Anaheim, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06310805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +447,BOGZ1FU615013103Q,2022-03-21,2022-03-21,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:07-05,1,1,"Source ID: FU615013103.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU615013103&t=) provided by the source." +448,BOGZ1FA615013103Q,2022-03-21,2022-03-21,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:11-05,1,1,"Source ID: FA615013103.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615013103&t=) provided by the source." +449,BOGZ1FU615013103A,2022-03-21,2022-03-21,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:11-05,1,1,"Source ID: FU615013103.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU615013103&t=) provided by the source." +450,BOGZ1FA615013103A,2022-03-21,2022-03-21,"Finance Companies; Fixed Assets, Book Value, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:11-05,1,1,"Source ID: FA615013103.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615013103&t=) provided by the source." +451,BOGZ1FL613169100Q,2022-03-21,2022-03-21,"Finance Companies; Commercial Paper; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613169100.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613169100&t=) provided by the source." -482,BOGZ1FL613169100A,2022-03-17,2022-03-17,"Finance Companies; Commercial Paper; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:04-06,1,1,"Source ID: FL613169100.A +452,BOGZ1FL613169100A,2022-03-21,2022-03-21,"Finance Companies; Commercial Paper; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:04-06,1,1,"Source ID: FL613169100.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613169100&t=) provided by the source." -483,BOGZ1FL613065200Q,2022-03-17,2022-03-17,"Finance Companies; Home Equity Lines of Credit; Asset, Level",1970-04-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613065200.Q +453,SMU08178205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Colorado Springs, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:10:12-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU08178205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +454,SMU08178205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Colorado Springs, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:07-05,1,1,No description provided. +455,SMU08178205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Colorado Springs, CO (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:07-05,1,1,No description provided. +456,SMS72000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Puerto Rico,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:21-05,1,1,No description provided. +457,SMU72000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Puerto Rico,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:11:29-05,1,1,No description provided. +458,SMU72000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Puerto Rico,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:20:07-05,1,1,No description provided. +459,SMU53000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Washington,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:22:20-05,1,1,No description provided. +460,SMS53000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Washington,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:45-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +461,SMU53000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Washington,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:37-05,1,1,No description provided. +462,BOGZ1FL615080003A,2022-03-21,2022-03-21,"Finance Companies; Equity Capital, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:19:27-05,1,1,"Source ID: FL615080003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL615080003&t=) provided by the source." +463,BOGZ1FL615080003Q,2022-03-21,2022-03-21,"Finance Companies; Equity Capital, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,1,1,"Source ID: FL615080003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL615080003&t=) provided by the source." +464,BOGZ1FL613065200Q,2022-03-21,2022-03-21,"Finance Companies; Home Equity Lines of Credit; Asset, Level",1970-04-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613065200.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065200&t=) provided by the source." -484,BOGZ1FL613065200A,2022-03-17,2022-03-17,"Finance Companies; Home Equity Lines of Credit; Asset, Level",1970-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613065200.A +465,BOGZ1FL613065200A,2022-03-21,2022-03-21,"Finance Companies; Home Equity Lines of Credit; Asset, Level",1970-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613065200.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065200&t=) provided by the source." -485,SMU53000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Washington,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:22:20-05,1,1,No description provided. -486,SMS53000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Washington,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:45-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -487,SMU53000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Washington,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:37-05,1,1,No description provided. -488,SMU17404205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Rockford, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:15-05,1,1,No description provided. -489,SMU17404205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Rockford, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:15-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +466,BOGZ1FA103169535A,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:46:41-06,1,1,"Source ID: FA103169535.A -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17404205552000001). +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -490,SMU17404205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Rockford, IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:46-05,1,1,No description provided. -491,SMU72419805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Juan-Carolina-Caguas, PR (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:10:32-05,1,1,No description provided. -492,SMU72419805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Juan-Carolina-Caguas, PR (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU72419805552000001). +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA103169535&t=) provided by the source." +467,BOGZ1FA103169535Q,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:57:56-06,1,1,"Source ID: FA103169535.Q -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -493,SMU72419805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Juan-Carolina-Caguas, PR (MSA)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:19:55-05,1,1,No description provided. -494,BOGZ1FA616006063Q,2022-03-17,2022-03-17,"Finance Companies; Foreign Earnings Retained Abroad, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA616006063.Q +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA103169535&t=) provided by the source." +468,BOGZ1FA616006063Q,2022-03-21,2022-03-21,"Finance Companies; Foreign Earnings Retained Abroad, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA616006063.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616006063&t=) provided by the source." -495,BOGZ1FU616006063Q,2022-03-17,2022-03-17,"Finance Companies; Foreign Earnings Retained Abroad, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:07-05,1,1,"Source ID: FU616006063.Q +469,BOGZ1FU616006063Q,2022-03-21,2022-03-21,"Finance Companies; Foreign Earnings Retained Abroad, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:07-05,1,1,"Source ID: FU616006063.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU616006063&t=) provided by the source." -496,BOGZ1FL613168005Q,2022-03-17,2022-03-17,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613168005.Q +470,BOGZ1FA613093043A,2022-03-21,2022-03-21,"Finance Companies; Unidentified Miscellaneous Assets Issued by Nonfinancial Corporate Businesses; Asset, Flow",1945-01-01,1945-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-05-11 14:22:19-05,1,1,"Source ID: FA613093043.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613093043&t=) provided by the source." +471,BOGZ1FL103169505Q,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Finance Companies and ABS Issuers Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:05:33-05,1,1,"Source ID: FL103169505.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169505&t=) provided by the source." +472,BOGZ1FL103169505A,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Finance Companies and ABS Issuers Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:05:33-05,1,1,"Source ID: FL103169505.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169505&t=) provided by the source." +473,BOGZ1FL613163093Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163093.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163093&t=) provided by the source." +474,BOGZ1FL613163093A,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163093.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163093&t=) provided by the source." +475,SMU17404205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Rockford, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:15-05,1,1,No description provided. +476,SMU17404205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Rockford, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:15-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17404205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +477,SMU17404205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Rockford, IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:46-05,1,1,No description provided. +478,SMU12367405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Orlando-Kissimmee-Sanford, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:57:11-05,1,1,No description provided. +479,SMU12367405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Orlando-Kissimmee-Sanford, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:02-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12367405552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +480,SMU12367405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Orlando-Kissimmee-Sanford, FL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:40-05,1,1,No description provided. +481,SMU72419805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Juan-Carolina-Caguas, PR (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:10:32-05,1,1,No description provided. +482,SMU72419805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Juan-Carolina-Caguas, PR (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU72419805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +483,SMU72419805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Juan-Carolina-Caguas, PR (MSA)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:19:55-05,1,1,No description provided. +484,SMU17169745552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Chicago-Naperville-Arlington Heights, IL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17169745552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +485,SMU17169745552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Chicago-Naperville-Arlington Heights, IL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:22-05,1,1,No description provided. +486,SMU17169745552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Chicago-Naperville-Arlington Heights, IL (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:03-05,1,1,No description provided. +487,BOGZ1FL613065123Q,2022-03-21,2022-03-21,"Finance Companies; Home Equity Loans; Asset, Level",1970-04-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613065123.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065123&t=) provided by the source." +488,BOGZ1FL613065123A,2022-03-21,2022-03-21,"Finance Companies; Home Equity Loans; Asset, Level",1970-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:38-06,1,1,"Source ID: FL613065123.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065123&t=) provided by the source." +489,SMU36356205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:28:33-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +490,SMU36356205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:28:33-06,1,1,No description provided. +491,SMU36356205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:33:10-05,1,1,No description provided. +492,SMU06418605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Oakland-Fremont, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 13:56:56-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +493,SMU06418605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Oakland-Fremont, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 13:56:56-06,1,1,No description provided. +494,BOGZ1FL103169535Q,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:53:39-06,1,1,"Source ID: FL103169535.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169535&t=) provided by the source." +495,BOGZ1FL103169535A,2022-03-21,2022-03-21,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:04:16-06,1,1,"Source ID: FL103169535.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169535&t=) provided by the source." +496,SMU48264205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:18:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48264205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +497,SMU48264205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:29-05,1,1,No description provided. +498,SMU48264205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:19:17-05,1,1,No description provided. +499,BOGZ1FL613168005Q,2022-03-21,2022-03-21,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613168005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613168005&t=) provided by the source." -497,BOGZ1FL613168005A,2022-03-17,2022-03-17,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:04-06,1,1,"Source ID: FL613168005.A +500,BOGZ1FL613168005A,2022-03-21,2022-03-21,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:04-06,1,1,"Source ID: FL613168005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613168005&t=) provided by the source." -498,SMU36356205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:28:33-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -499,SMU36356205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:28:33-06,1,1,No description provided. -500,SMU36356205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:33:10-05,1,1,No description provided. -501,BOGZ1FA613069500A,2022-03-17,2022-03-17,"Finance Companies; Nonfinancial Business Loans; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613069500.A +501,FINCLL,2022-03-21,2022-03-21,"Finance Companies; Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:50:11-06,1,1,"Source ID: FL614123005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614123005&t=) provided by the source." +502,BOGZ1FL614123005A,2022-03-21,2022-03-21,"Finance Companies; Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL614123005.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614123005&t=) provided by the source." +503,BOGZ1FA613069500A,2022-03-21,2022-03-21,"Finance Companies; Nonfinancial Business Loans; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613069500.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613069500&t=) provided by the source." -502,BOGZ1FL613163093Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163093.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163093&t=) provided by the source." -503,BOGZ1FL613163093A,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:16-05,1,1,"Source ID: FL613163093.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163093&t=) provided by the source." -504,BOGZ1FL103169505A,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Finance Companies and ABS Issuers Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:05:33-05,1,1,"Source ID: FL103169505.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169505&t=) provided by the source." -505,BOGZ1FL103169505Q,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Finance Companies and ABS Issuers Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:05:33-05,1,1,"Source ID: FL103169505.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169505&t=) provided by the source." -506,SMU06418605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Oakland-Fremont, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 13:56:56-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -507,SMU06418605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Oakland-Fremont, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 13:56:56-06,1,1,No description provided. -508,SMU12367405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Orlando-Kissimmee-Sanford, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:57:11-05,1,1,No description provided. -509,SMU12367405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Orlando-Kissimmee-Sanford, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:02-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12367405552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -510,SMU12367405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Orlando-Kissimmee-Sanford, FL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:40-05,1,1,No description provided. -511,SMU17169745552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Chicago-Naperville-Arlington Heights, IL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17169745552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -512,SMU17169745552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Chicago-Naperville-Arlington Heights, IL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:22-05,1,1,No description provided. -513,SMU17169745552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Chicago-Naperville-Arlington Heights, IL (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:03-05,1,1,No description provided. -514,BOGZ1FL613065123Q,2022-03-17,2022-03-17,"Finance Companies; Home Equity Loans; Asset, Level",1970-04-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613065123.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065123&t=) provided by the source." -515,BOGZ1FL613065123A,2022-03-17,2022-03-17,"Finance Companies; Home Equity Loans; Asset, Level",1970-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:38-06,1,1,"Source ID: FL613065123.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065123&t=) provided by the source." -516,SMU48264205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:18:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48264205552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -517,SMU48264205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:29-05,1,1,No description provided. -518,SMU48264205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:19:17-05,1,1,No description provided. -519,BOGZ1FL103169535Q,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:53:39-06,1,1,"Source ID: FL103169535.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169535&t=) provided by the source." -520,BOGZ1FL103169535A,2022-03-17,2022-03-17,"Nonfinancial Corporate Business; Finance Companies Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:04:16-06,1,1,"Source ID: FL103169535.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL103169535&t=) provided by the source." -521,FINCLL,2022-03-17,2022-03-17,"Finance Companies; Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:50:11-06,1,1,"Source ID: FL614123005.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614123005&t=) provided by the source." -522,BOGZ1FL614123005A,2022-03-17,2022-03-17,"Finance Companies; Loans; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL614123005.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614123005&t=) provided by the source." -523,SMU11000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in District of Columbia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:11-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU11000005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -524,SMU11000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in District of Columbia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:34-05,1,1,No description provided. -525,SMU12272605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Jacksonville, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:58:21-05,1,1,No description provided. -526,SMU12272605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Jacksonville, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:05:08-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +504,SMU12272605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Jacksonville, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:58:21-05,1,1,No description provided. +505,SMU12272605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Jacksonville, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:05:08-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12272605552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -527,SMU12272605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Jacksonville, FL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:25-05,1,1,No description provided. -528,SMS54000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in West Virginia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:42-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -529,BOGZ1FL613065115Q,2022-03-17,2022-03-17,"Finance Companies; One-to-Four-Family Residential Mortgages Issued by Households; Asset, Level",1945-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:22:09-06,1,1,"Source ID: FL613065115.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065115&t=) provided by the source." -530,SMU51947835552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Northern Virginia, VA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:14:05-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU51947835552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" - -Northern Virginia, VA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -531,SMU51947835552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Northern Virginia, VA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:14:34-05,1,1,"Northern Virginia, VA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -532,SMU51947835552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Northern Virginia, VA",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:36-05,1,1,"Northern Virginia, VA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -533,SMU18269005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Indianapolis-Carmel-Anderson, IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:51:49-05,1,1,No description provided. -534,SMU18269005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Indianapolis-Carmel-Anderson, IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:56:43-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU18269005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -535,SMU18269005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Indianapolis-Carmel-Anderson, IN (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:48-05,1,1,No description provided. -536,BOGZ1FA616330023Q,2022-03-17,2022-03-17,"Finance Companies; Consumption of Fixed Capital, Nonresidential Equipment, Current Cost Basis, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:06-05,1,1,"Source ID: FA616330023.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616330023&t=) provided by the source." -537,BOGZ1FA616006403Q,2022-03-17,2022-03-17,"Finance Companies; Undistributed Corporate Profits Excluding CCAdj, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA616006403.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616006403&t=) provided by the source." -538,BOGZ1FA616006403A,2022-03-17,2022-03-17,"Finance Companies; Undistributed Corporate Profits Excluding CCAdj, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA616006403.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616006403&t=) provided by the source." -539,SMU36356145552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-Jersey City-White Plains, NY-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:33:01-05,1,1,No description provided. -540,SMU36356145552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-Jersey City-White Plains, NY-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:36:25-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +506,SMU12272605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Jacksonville, FL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:25-05,1,1,No description provided. +507,SMU36356145552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-Jersey City-White Plains, NY-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:33:01-05,1,1,No description provided. +508,SMU36356145552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-Jersey City-White Plains, NY-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:36:25-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36356145552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -541,SMU36356145552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-Jersey City-White Plains, NY-NJ (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:25-05,1,1,No description provided. -542,SMU04380605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Phoenix-Mesa-Scottsdale, AZ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:18:38-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +509,SMU36356145552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-Jersey City-White Plains, NY-NJ (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:25-05,1,1,No description provided. +510,SMU18269005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Indianapolis-Carmel-Anderson, IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:51:49-05,1,1,No description provided. +511,SMU18269005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Indianapolis-Carmel-Anderson, IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:56:43-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU18269005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +512,SMU18269005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Indianapolis-Carmel-Anderson, IN (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:48-05,1,1,No description provided. +513,BOGZ1FL613192305Q,2022-03-21,2022-03-21,"Finance Companies; Foreign Direct Investment in U.S.: Intercompany Debt; Liability (Market Value), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613192305.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613192305&t=) provided by the source." +514,BOGZ1FL613192305A,2022-03-21,2022-03-21,"Finance Companies; Foreign Direct Investment in U.S.: Intercompany Debt; Liability (Market Value), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL613192305.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613192305&t=) provided by the source." +515,SMS17000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Illinois,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:23-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +516,SMU17000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Illinois,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:21-05,1,1,No description provided. +517,SMU17000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Illinois,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:54-05,1,1,No description provided. +518,SMU11000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in District of Columbia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:11-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU11000005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +519,SMU11000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in District of Columbia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:34-05,1,1,No description provided. +520,BOGZ1FA616006403Q,2022-03-21,2022-03-21,"Finance Companies; Undistributed Corporate Profits Excluding CCAdj, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA616006403.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616006403&t=) provided by the source." +521,BOGZ1FA616006403A,2022-03-21,2022-03-21,"Finance Companies; Undistributed Corporate Profits Excluding CCAdj, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA616006403.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616006403&t=) provided by the source." +522,SMU04380605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Phoenix-Mesa-Scottsdale, AZ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:18:38-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU04380605552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -543,SMU04380605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Phoenix-Mesa-Scottsdale, AZ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:59-05,1,1,No description provided. -544,SMU04380605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Phoenix-Mesa-Scottsdale, AZ (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:42-05,1,1,No description provided. -545,BOGZ1FL613192305Q,2022-03-17,2022-03-17,"Finance Companies; Foreign Direct Investment in U.S.: Intercompany Debt; Liability (Market Value), Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613192305.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613192305&t=) provided by the source." -546,BOGZ1FL613192305A,2022-03-17,2022-03-17,"Finance Companies; Foreign Direct Investment in U.S.: Intercompany Debt; Liability (Market Value), Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL613192305.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613192305&t=) provided by the source." -547,BOGZ1FL614004005A,2022-03-17,2022-03-17,"Finance Companies; Debt Securities and Loans; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,1,1,"Source ID: FL614004005.A +523,SMU04380605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Phoenix-Mesa-Scottsdale, AZ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:59-05,1,1,No description provided. +524,SMU04380605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Phoenix-Mesa-Scottsdale, AZ (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:42-05,1,1,No description provided. +525,BOGZ1FL614004005A,2022-03-21,2022-03-21,"Finance Companies; Debt Securities and Loans; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:15-05,1,1,"Source ID: FL614004005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL614004005&t=) provided by the source." -548,SMS17000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Illinois,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:23-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -549,SMU17000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Illinois,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:21-05,1,1,No description provided. -550,SMU17000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Illinois,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:54-05,1,1,No description provided. -551,SMS02000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Alaska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:09-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -552,SMU02000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Alaska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:33-05,1,1,No description provided. -553,SMU02000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Alaska,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:44-05,1,1,No description provided. -554,SMU12331245552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Miami-Miami Beach-Kendall, FL (MD)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:58:11-05,1,1,No description provided. -555,SMU12331245552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Miami-Miami Beach-Kendall, FL (MD)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:20-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +526,BOGZ1FL613065115Q,2022-03-21,2022-03-21,"Finance Companies; One-to-Four-Family Residential Mortgages Issued by Households; Asset, Level",1945-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:22:09-06,1,1,"Source ID: FL613065115.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065115&t=) provided by the source." +527,SMU51947835552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Northern Virginia, VA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:14:05-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU51947835552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" + +Northern Virginia, VA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +528,SMU51947835552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Northern Virginia, VA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:14:34-05,1,1,"Northern Virginia, VA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +529,SMU51947835552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Northern Virginia, VA",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:36-05,1,1,"Northern Virginia, VA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +530,SMS54000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in West Virginia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:42-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +531,BOGZ1FA616330023Q,2022-03-21,2022-03-21,"Finance Companies; Consumption of Fixed Capital, Nonresidential Equipment, Current Cost Basis, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:06-05,1,1,"Source ID: FA616330023.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA616330023&t=) provided by the source." +532,SMS02000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Alaska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:09-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +533,SMU02000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Alaska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:33-05,1,1,No description provided. +534,SMU02000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Alaska,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:44-05,1,1,No description provided. +535,SMU12331245552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Miami-Miami Beach-Kendall, FL (MD)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:58:11-05,1,1,No description provided. +536,SMU12331245552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Miami-Miami Beach-Kendall, FL (MD)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:20-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12331245552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -556,SMU12331245552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Miami-Miami Beach-Kendall, FL (MD)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:59-05,1,1,No description provided. -557,SMU11479005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:50-05,1,1,No description provided. -558,SMU11479005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:01-05,1,1,No description provided. -559,SMU11479005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:06:51-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU11479005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -560,SMU42383005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Pittsburgh, PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:24:09-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42383005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -561,SMU42383005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Pittsburgh, PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:24:19-05,1,1,No description provided. -562,SMU42383005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Pittsburgh, PA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:06-05,1,1,No description provided. -563,SMU27334605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:42:00-05,1,1,No description provided. -564,SMU27334605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:44:29-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27334605552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -565,SMU27334605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:49-05,1,1,No description provided. -566,SMU12453005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Tampa-St. Petersburg-Clearwater, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:56:45-05,1,1,No description provided. -567,SMU12453005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Tampa-St. Petersburg-Clearwater, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:03:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12453005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -568,SMU12453005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Tampa-St. Petersburg-Clearwater, FL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:59-05,1,1,No description provided. -569,SMU48124205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Austin-Round Rock, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:20:04-05,1,1,No description provided. -570,SMU48124205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Austin-Round Rock, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:20:06-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48124205552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -571,SMU25716545552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Newton, MA (NECTA Division)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:45:18-05,1,1,No description provided. -572,SMU25716545552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Newton, MA (NECTA Division)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:36:42-05,1,1,No description provided. -573,SMU25716545552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Newton, MA (NECTA Division)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:50:26-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +537,SMU12331245552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Miami-Miami Beach-Kendall, FL (MD)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:59-05,1,1,No description provided. +538,SMU25716545552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Newton, MA (NECTA Division)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:45:18-05,1,1,No description provided. +539,SMU25716545552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Newton, MA (NECTA Division)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:36:42-05,1,1,No description provided. +540,SMU25716545552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Newton, MA (NECTA Division)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:50:26-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU25716545552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -574,SMU36153805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Buffalo-Cheektowaga-Niagara Falls, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:34:34-05,1,1,No description provided. -575,SMU36153805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Buffalo-Cheektowaga-Niagara Falls, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:36:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +541,SMU27334605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:42:00-05,1,1,No description provided. +542,SMU27334605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:44:29-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36153805552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27334605552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -576,SMU36153805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Buffalo-Cheektowaga-Niagara Falls, NY (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:08-05,1,1,No description provided. -577,SMU06419405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Jose-Sunnyvale-Santa Clara, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:10:47-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +543,SMU27334605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:49-05,1,1,No description provided. +544,SMU42383005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Pittsburgh, PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:24:09-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42383005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +545,SMU42383005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Pittsburgh, PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:24:19-05,1,1,No description provided. +546,SMU42383005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Pittsburgh, PA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:06-05,1,1,No description provided. +547,SMU06419405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Jose-Sunnyvale-Santa Clara, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:10:47-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06419405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -578,SMU06419405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Jose-Sunnyvale-Santa Clara, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:30-05,1,1,No description provided. -579,SMU06419405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Jose-Sunnyvale-Santa Clara, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:53-05,1,1,No description provided. -580,SMU36350045552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Nassau County-Suffolk County, NY (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 03:37:05-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +548,SMU06419405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Jose-Sunnyvale-Santa Clara, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:30-05,1,1,No description provided. +549,SMU06419405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Jose-Sunnyvale-Santa Clara, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:53-05,1,1,No description provided. +550,SMU11479005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:50-05,1,1,No description provided. +551,SMU11479005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:01-05,1,1,No description provided. +552,SMU11479005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:06:51-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36350045552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU11479005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -581,SMU36350045552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Nassau County-Suffolk County, NY (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:34:14-05,1,1,No description provided. -582,SMU36350045552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Nassau County-Suffolk County, NY (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:44-05,1,1,No description provided. -583,SMU42379805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:24:24-05,1,1,No description provided. -584,SMU42379805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:24:16-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +553,SMU48124205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Austin-Round Rock, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:20:04-05,1,1,No description provided. +554,SMU48124205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Austin-Round Rock, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:20:06-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48124205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +555,SMU36153805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Buffalo-Cheektowaga-Niagara Falls, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:34:34-05,1,1,No description provided. +556,SMU36153805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Buffalo-Cheektowaga-Niagara Falls, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:36:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36153805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +557,SMU36153805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Buffalo-Cheektowaga-Niagara Falls, NY (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:08-05,1,1,No description provided. +558,SMS42000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Pennsylvania,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:07-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +559,SMU42000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Pennsylvania,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:24:37-05,1,1,No description provided. +560,SMU42000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Pennsylvania,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:46-05,1,1,No description provided. +561,SMU42379805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:24:24-05,1,1,No description provided. +562,SMU42379805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:24:16-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42379805552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -585,SMU42379805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:29-05,1,1,No description provided. -586,SMS42000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Pennsylvania,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:07-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -587,SMU42000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Pennsylvania,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:24:37-05,1,1,No description provided. -588,SMU42000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Pennsylvania,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:46-05,1,1,No description provided. -589,BOGZ1FL613163005Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613163005.Q +563,SMU42379805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:29-05,1,1,No description provided. +564,SMU12453005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Tampa-St. Petersburg-Clearwater, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:56:45-05,1,1,No description provided. +565,SMU12453005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Tampa-St. Petersburg-Clearwater, FL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:03:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12453005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +566,SMU12453005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Tampa-St. Petersburg-Clearwater, FL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:59-05,1,1,No description provided. +567,BOGZ1FL613163005Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613163005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613163005&t=) provided by the source." -590,SMU51472605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Virginia Beach-Norfolk-Newport News, VA-NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:14:41-05,1,1,No description provided. -591,SMU51472605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Virginia Beach-Norfolk-Newport News, VA-NC (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:40-05,1,1,No description provided. -592,SMU51472605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Virginia Beach-Norfolk-Newport News, VA-NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:14:27-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +568,SMU51472605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Virginia Beach-Norfolk-Newport News, VA-NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:14:41-05,1,1,No description provided. +569,SMU51472605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Virginia Beach-Norfolk-Newport News, VA-NC (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:40-05,1,1,No description provided. +570,SMU51472605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Virginia Beach-Norfolk-Newport News, VA-NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:14:27-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU51472605552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -593,BOGZ1FA613069505Q,2022-03-17,2022-03-17,"Finance Companies; Nonfinancial Business Loans, Including Paycheck Protection Program Loans; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:33-05,1,1,"Source ID: FA613069505.Q +571,SMS19000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Iowa,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:17-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +572,SMU19000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Iowa,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:46-05,1,1,No description provided. +573,SMU19000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Iowa,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:50-05,1,1,No description provided. +574,SMU36350045552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Nassau County-Suffolk County, NY (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 03:37:05-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36350045552000001). -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613069505&t=) provided by the source." -594,SMS19000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Iowa,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:17-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -595,SMU19000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Iowa,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:46-05,1,1,No description provided. -596,SMU19000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Iowa,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:50-05,1,1,No description provided. -597,BOGZ1FA613065105Q,2022-03-17,2022-03-17,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:36:25-06,1,1,"Source ID: FA613065105.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065105&t=) provided by the source." -598,BOGZ1FA613065105A,2022-03-17,2022-03-17,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065105.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065105&t=) provided by the source." -599,BOGZ1FR613163093Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613163093.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613163093&t=) provided by the source." -600,BOGZ1FR613163093A,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613163093.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613163093&t=) provided by the source." -601,BOGZ1FL613065503Q,2022-03-17,2022-03-17,"Finance Companies; Commercial Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613065503.Q +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +575,SMU36350045552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Nassau County-Suffolk County, NY (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:34:14-05,1,1,No description provided. +576,SMU36350045552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Nassau County-Suffolk County, NY (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:44-05,1,1,No description provided. +577,BOGZ1FL613065503Q,2022-03-21,2022-03-21,"Finance Companies; Commercial Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613065503.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065503&t=) provided by the source." -602,BOGZ1FL613065503A,2022-03-17,2022-03-17,"Finance Companies; Commercial Mortgages; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:37-06,1,1,"Source ID: FL613065503.A +578,BOGZ1FL613065503A,2022-03-21,2022-03-21,"Finance Companies; Commercial Mortgages; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:37-06,1,1,"Source ID: FL613065503.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065503&t=) provided by the source." -603,BOGZ1FA613168005Q,2022-03-17,2022-03-17,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:54-06,1,1,"Source ID: FA613168005.Q +579,BOGZ1FA613168005Q,2022-03-21,2022-03-21,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:54-06,1,1,"Source ID: FA613168005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613168005&t=) provided by the source." -604,BOGZ1FU613168005A,2022-03-17,2022-03-17,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:06-06,1,1,"Source ID: FU613168005.A +580,BOGZ1FU613168005A,2022-03-21,2022-03-21,"Finance Companies; Depository Institution Loans N.E.C., Including Loans Through the Paycheck Protection Program Liquidity Facility (PPPLF); Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:06-06,1,1,"Source ID: FU613168005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613168005&t=) provided by the source." -605,SMU47000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Tennessee,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:24:27-05,1,1,No description provided. -606,SMS47000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Tennessee,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:57-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -607,SMU47000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Tennessee,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:21:24-05,1,1,No description provided. -608,BOGZ1FL613066005Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613066005.Q +581,BOGZ1FA613069505Q,2022-03-21,2022-03-21,"Finance Companies; Nonfinancial Business Loans, Including Paycheck Protection Program Loans; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:01:33-05,1,1,"Source ID: FA613069505.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613069505&t=) provided by the source." +582,SMU47000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Tennessee,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:24:27-05,1,1,No description provided. +583,SMS47000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Tennessee,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:57-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +584,SMU47000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Tennessee,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:21:24-05,1,1,No description provided. +585,BOGZ1FL613066005Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613066005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066005&t=) provided by the source." -609,BOGZ1FL613066005A,2022-03-17,2022-03-17,"Finance Companies; Consumer Credit; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:05-06,1,1,"Source ID: FL613066005.A +586,BOGZ1FL613066005A,2022-03-21,2022-03-21,"Finance Companies; Consumer Credit; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:05-06,1,1,"Source ID: FL613066005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066005&t=) provided by the source." -610,BOGZ1FA713068863Q,2022-03-17,2022-03-17,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:33-06,1,1,"Source ID: FA713068863.Q +587,BOGZ1FR613163093Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613163093.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA713068863&t=) provided by the source." -611,BOGZ1FU713068863Q,2022-03-17,2022-03-17,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:47:49-06,1,1,"Source ID: FU713068863.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613163093&t=) provided by the source." +588,BOGZ1FR613163093A,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Revaluation",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613163093.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU713068863&t=) provided by the source." -612,BOGZ1FU713068863A,2022-03-17,2022-03-17,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:56:08-06,1,1,"Source ID: FU713068863.A +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613163093&t=) provided by the source." +589,SMS12000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Florida,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:03-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +590,SMU12000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Florida,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:57:40-05,1,1,No description provided. +591,SMU12000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Florida,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:32-05,1,1,No description provided. +592,BOGZ1FA613065105Q,2022-03-21,2022-03-21,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:36:25-06,1,1,"Source ID: FA613065105.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU713068863&t=) provided by the source." -613,SMU08197405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Denver-Aurora-Lakewood, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:03-05,1,1,No description provided. -614,SMU08197405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Denver-Aurora-Lakewood, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:10:02-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065105&t=) provided by the source." +593,BOGZ1FA613065105A,2022-03-21,2022-03-21,"Finance Companies; One-to-Four-Family Residential Mortgages; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065105.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065105&t=) provided by the source." +594,SMU08197405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Denver-Aurora-Lakewood, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:03-05,1,1,No description provided. +595,SMU08197405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Denver-Aurora-Lakewood, CO (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:10:02-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU08197405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -615,SMU08197405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Denver-Aurora-Lakewood, CO (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:03-05,1,1,No description provided. -616,SMU06418845552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Redwood City-South San Francisco, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:12-05,1,1,No description provided. -617,SMU06418845552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Redwood City-South San Francisco, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:11:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +596,SMU08197405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Denver-Aurora-Lakewood, CO (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:03-05,1,1,No description provided. +597,SMS51000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Virginia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:48-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +598,SMU51000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Virginia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:02-05,1,1,No description provided. +599,SMU51000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Virginia,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:22:06-05,1,1,No description provided. +600,SMU06418845552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Redwood City-South San Francisco, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:01:12-05,1,1,No description provided. +601,SMU06418845552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Redwood City-South San Francisco, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:11:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06418845552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -618,SMU06418845552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Redwood City-South San Francisco, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:59-05,1,1,No description provided. -619,SMS12000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Florida,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:03-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -620,SMU12000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Florida,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:57:40-05,1,1,No description provided. -621,SMU12000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Florida,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:32-05,1,1,No description provided. -622,SMU13120605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Atlanta-Sandy Springs-Roswell, GA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:56:10-05,1,1,No description provided. -623,SMU13120605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Atlanta-Sandy Springs-Roswell, GA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:02:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +602,SMU06418845552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Francisco-Redwood City-South San Francisco, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:59-05,1,1,No description provided. +603,BOGZ1FL613065403Q,2022-03-21,2022-03-21,"Finance Companies; Multifamily Residential Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613065403.Q -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU13120605552000001). +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -624,SMU13120605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Atlanta-Sandy Springs-Roswell, GA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:43-05,1,1,No description provided. -625,SMS51000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Virginia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:48-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -626,SMU51000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Virginia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:02-05,1,1,No description provided. -627,SMU51000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Virginia,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:22:06-05,1,1,No description provided. -628,SMS04000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Arizona,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:07-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -629,SMU04000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Arizona,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:26-05,1,1,No description provided. -630,SMU04000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Arizona,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:08-05,1,1,No description provided. -631,BOGZ1FL613030003Q,2022-03-17,2022-03-17,"Finance Companies; Total Time and Savings Deposits; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613030003.Q +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065403&t=) provided by the source." +604,BOGZ1FL613065403A,2022-03-21,2022-03-21,"Finance Companies; Multifamily Residential Mortgages; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:37-06,1,1,"Source ID: FL613065403.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065403&t=) provided by the source." +605,BOGZ1FL613030003Q,2022-03-21,2022-03-21,"Finance Companies; Total Time and Savings Deposits; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613030003.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613030003&t=) provided by the source." -632,BOGZ1FL613065403Q,2022-03-17,2022-03-17,"Finance Companies; Multifamily Residential Mortgages; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613065403.Q +606,SMU13120605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Atlanta-Sandy Springs-Roswell, GA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:56:10-05,1,1,No description provided. +607,SMU13120605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Atlanta-Sandy Springs-Roswell, GA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:02:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU13120605552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +608,SMU13120605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Atlanta-Sandy Springs-Roswell, GA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:43-05,1,1,No description provided. +609,BOGZ1FA713068863Q,2022-03-21,2022-03-21,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:33-06,1,1,"Source ID: FA713068863.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065403&t=) provided by the source." -633,BOGZ1FL613065403A,2022-03-17,2022-03-17,"Finance Companies; Multifamily Residential Mortgages; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:37-06,1,1,"Source ID: FL613065403.A +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA713068863&t=) provided by the source." +610,BOGZ1FU713068863Q,2022-03-21,2022-03-21,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:47:49-06,1,1,"Source ID: FU713068863.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613065403&t=) provided by the source." -634,SMU55333405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Milwaukee-Waukesha-West Allis, WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:12:35-05,1,1,No description provided. -635,SMU55333405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Milwaukee-Waukesha-West Allis, WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:00-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU713068863&t=) provided by the source." +611,BOGZ1FU713068863A,2022-03-21,2022-03-21,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:56:08-06,1,1,"Source ID: FU713068863.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU713068863&t=) provided by the source." +612,SMS04000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Arizona,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:07-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +613,SMU04000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Arizona,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:26-05,1,1,No description provided. +614,SMU04000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Arizona,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:08-05,1,1,No description provided. +615,SMU55333405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Milwaukee-Waukesha-West Allis, WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:12:35-05,1,1,No description provided. +616,SMU55333405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Milwaukee-Waukesha-West Allis, WI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:00-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU55333405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -636,SMU55333405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Milwaukee-Waukesha-West Allis, WI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:20:52-05,1,1,No description provided. -637,SMU41389005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Portland-Vancouver-Hillsboro, OR-WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:25:44-05,1,1,No description provided. -638,SMU41389005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Portland-Vancouver-Hillsboro, OR-WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:26:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU41389005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -639,SMU41389005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Portland-Vancouver-Hillsboro, OR-WA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:17-05,1,1,No description provided. -640,SMU39181405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Columbus, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:46-05,1,1,No description provided. -641,SMU39181405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Columbus, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +617,SMU55333405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Milwaukee-Waukesha-West Allis, WI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:20:52-05,1,1,No description provided. +618,SMU39181405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Columbus, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:46-05,1,1,No description provided. +619,SMU39181405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Columbus, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39181405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -642,BOGZ1FL613169333Q,2022-03-17,2022-03-17,"Finance Companies; FHLB Advances; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613169333.Q +620,BOGZ1FL613169333Q,2022-03-21,2022-03-21,"Finance Companies; FHLB Advances; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613169333.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613169333&t=) provided by the source." -643,BOGZ1FL613169333A,2022-03-17,2022-03-17,"Finance Companies; FHLB Advances; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:04-06,1,1,"Source ID: FL613169333.A +621,BOGZ1FL613169333A,2022-03-21,2022-03-21,"Finance Companies; FHLB Advances; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:04-06,1,1,"Source ID: FL613169333.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613169333&t=) provided by the source." -644,SMU48417005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Antonio-New Braunfels, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:17:17-05,1,1,No description provided. -645,SMU48417005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Antonio-New Braunfels, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:16:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +622,SMU48417005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Antonio-New Braunfels, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:17:17-05,1,1,No description provided. +623,SMU48417005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Antonio-New Braunfels, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:16:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48417005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -646,SMU48417005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in San Antonio-New Braunfels, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:11-05,1,1,No description provided. -647,BOGZ1FA613169100A,2022-03-17,2022-03-17,"Finance Companies; Commercial Paper; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:02-06,1,1,"Source ID: FA613169100.A +624,SMU48417005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in San Antonio-New Braunfels, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:11-05,1,1,No description provided. +625,SMU41389005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Portland-Vancouver-Hillsboro, OR-WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:25:44-05,1,1,No description provided. +626,SMU41389005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Portland-Vancouver-Hillsboro, OR-WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:26:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU41389005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +627,SMU41389005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Portland-Vancouver-Hillsboro, OR-WA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:17-05,1,1,No description provided. +628,SMU24125805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baltimore-Columbia-Towson, MD (MSA)",1991-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:46-05,1,1,No description provided. +629,SMU24125805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baltimore-Columbia-Towson, MD (MSA)",1991-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:51:58-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24125805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +630,SMS09000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Connecticut,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:09-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +631,SMU09000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Connecticut,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:50-05,1,1,No description provided. +632,SMU09000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Connecticut,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:41-05,1,1,No description provided. +633,SMU05000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Arkansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:31-05,1,1,No description provided. +634,SMS05000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Arkansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:04-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +635,SMU05000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Arkansas,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:59-05,1,1,No description provided. +636,SMS39000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Ohio,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:30-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +637,SMU39000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Ohio,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:58-05,1,1,No description provided. +638,SMU39000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Ohio,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:22-05,1,1,No description provided. +639,SMU40364205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Oklahoma City, OK (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:00-05,1,1,No description provided. +640,SMU40364205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Oklahoma City, OK (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU40364205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +641,SMU40364205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Oklahoma City, OK (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:59-05,1,1,No description provided. +642,SMU37395805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Raleigh, NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:53-05,1,1,No description provided. +643,SMU37395805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Raleigh, NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:31:52-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU37395805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +644,SMU37395805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Raleigh, NC (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:03-05,1,1,No description provided. +645,SMU39171405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cincinnati, OH-KY-IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:00-05,1,1,No description provided. +646,SMU39171405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cincinnati, OH-KY-IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39171405552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +647,SMU39171405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cincinnati, OH-KY-IN (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:21-05,1,1,No description provided. +648,SMU26198205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Detroit-Warren-Dearborn, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:43:26-05,1,1,No description provided. +649,SMU26198205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Detroit-Warren-Dearborn, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:47:47-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26198205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +650,SMU26198205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Detroit-Warren-Dearborn, MI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:35:11-05,1,1,No description provided. +651,SMU21311405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Louisville/Jefferson County, KY-IN (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:48:43-05,1,1,No description provided. +652,SMU21311405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Louisville/Jefferson County, KY-IN (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:54:03-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU21311405552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +653,SMU21311405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Louisville/Jefferson County, KY-IN (MSA)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:00-05,1,1,No description provided. +654,SMU11478945552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:08-05,1,1,No description provided. +655,SMU11478945552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:02-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU11478945552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +656,SMU11478945552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:56-05,1,1,No description provided. +657,BOGZ1FA613169100A,2022-03-21,2022-03-21,"Finance Companies; Commercial Paper; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:02-06,1,1,"Source ID: FA613169100.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613169100&t=) provided by the source." -648,SMU24125805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baltimore-Columbia-Towson, MD (MSA)",1991-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:46-05,1,1,No description provided. -649,SMU24125805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baltimore-Columbia-Towson, MD (MSA)",1991-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:51:58-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +658,SMU53440605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Spokane-Spokane Valley, WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:14:09-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24125805552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU53440605552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -650,SMU40364205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Oklahoma City, OK (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:00-05,1,1,No description provided. -651,SMU40364205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Oklahoma City, OK (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU40364205552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -652,SMU40364205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Oklahoma City, OK (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:59-05,1,1,No description provided. -653,SMU39171405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cincinnati, OH-KY-IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:00-05,1,1,No description provided. -654,SMU39171405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cincinnati, OH-KY-IN (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39171405552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -655,SMU39171405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cincinnati, OH-KY-IN (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:21-05,1,1,No description provided. -656,SMU37395805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Raleigh, NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:53-05,1,1,No description provided. -657,SMU37395805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Raleigh, NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:31:52-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU37395805552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -658,SMU37395805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Raleigh, NC (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:03-05,1,1,No description provided. -659,BOGZ1FR616330023Q,2022-03-17,2022-03-17,"Finance Companies; Consumption of Fixed Capital, Nonresidential Equipment, Current Cost Basis, Revaluation",1947-01-01,1951-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2019-05-23 14:32:02-05,1,1,"Source ID: FR616330023.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR616330023&t=) provided by the source." -660,SMU26198205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Detroit-Warren-Dearborn, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:43:26-05,1,1,No description provided. -661,SMU26198205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Detroit-Warren-Dearborn, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:47:47-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26198205552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -662,SMU26198205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Detroit-Warren-Dearborn, MI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:35:11-05,1,1,No description provided. -663,SMU21311405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Louisville/Jefferson County, KY-IN (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:48:43-05,1,1,No description provided. -664,SMU21311405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Louisville/Jefferson County, KY-IN (MSA)",2003-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:54:03-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU21311405552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -665,SMU21311405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Louisville/Jefferson County, KY-IN (MSA)",2003-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:00-05,1,1,No description provided. -666,SMS09000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Connecticut,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:09-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -667,SMU09000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Connecticut,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:50-05,1,1,No description provided. -668,SMU09000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Connecticut,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:41-05,1,1,No description provided. -669,SMU11478945552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:08-05,1,1,No description provided. -670,SMU11478945552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:02-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU11478945552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -671,SMU11478945552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Washington-Arlington-Alexandria, DC-VA-MD-WV (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:42:56-05,1,1,No description provided. -672,SMU05000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Arkansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:04:31-05,1,1,No description provided. -673,SMS05000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Arkansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:11:04-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -674,SMU05000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Arkansas,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:59-05,1,1,No description provided. -675,SMS39000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Ohio,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:30-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -676,SMU39000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Ohio,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:58-05,1,1,No description provided. -677,SMU39000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Ohio,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:22-05,1,1,No description provided. -678,BOGZ1FA614190005Q,2022-03-17,2022-03-17,"Finance Companies; Total Liabilities, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA614190005.Q +659,SMU53440605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Spokane-Spokane Valley, WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:16-05,1,1,No description provided. +660,SMU53440605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Spokane-Spokane Valley, WA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:49-05,1,1,No description provided. +661,BOGZ1FA614190005Q,2022-03-21,2022-03-21,"Finance Companies; Total Liabilities, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA614190005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA614190005&t=) provided by the source." -679,SMU48191245552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dallas-Plano-Irving, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:18:54-05,1,1,No description provided. -680,SMU48191245552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dallas-Plano-Irving, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:18:38-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48191245552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -681,SMU48191245552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dallas-Plano-Irving, TX (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:46-05,1,1,No description provided. -682,BOGZ1FA613065183Q,2022-03-17,2022-03-17,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613065183.Q +662,BOGZ1FA613065183Q,2022-03-21,2022-03-21,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613065183.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065183&t=) provided by the source." -683,SMU53440605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Spokane-Spokane Valley, WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:14:09-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +663,SMU48191245552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dallas-Plano-Irving, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:18:54-05,1,1,No description provided. +664,SMU48191245552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dallas-Plano-Irving, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:18:38-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU53440605552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48191245552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -684,SMU53440605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Spokane-Spokane Valley, WA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:15:16-05,1,1,No description provided. -685,SMU53440605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Spokane-Spokane Valley, WA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:21:49-05,1,1,No description provided. -686,BOGZ1FL113169535Q,2022-03-17,2022-03-17,"Nonfinancial Noncorporate Business; Finance Companies Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:53:36-06,1,1,"Source ID: FL113169535.Q +665,SMU48191245552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dallas-Plano-Irving, TX (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:46-05,1,1,No description provided. +666,BOGZ1FL113169535Q,2022-03-21,2022-03-21,"Nonfinancial Noncorporate Business; Finance Companies Loans; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:53:36-06,1,1,"Source ID: FL113169535.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL113169535&t=) provided by the source." -687,SMU06473005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Visalia-Porterville, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:09:17-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +667,SMU06473005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Visalia-Porterville, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:09:17-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06473005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -688,SMU06473005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Visalia-Porterville, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:43-05,1,1,No description provided. -689,SMU06473005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Visalia-Porterville, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:33-05,1,1,No description provided. -690,SMU06360845552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Oakland-Hayward-Berkeley, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:21-05,1,1,No description provided. -691,SMU06360845552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Oakland-Hayward-Berkeley, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:12:42-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06360845552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -692,SMU06360845552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Oakland-Hayward-Berkeley, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:25-05,1,1,No description provided. -693,SMU49416205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Salt Lake City, UT (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:17:30-05,1,1,No description provided. -694,SMU49416205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Salt Lake City, UT (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:16:57-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU49416205552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -695,SMU48231045552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Fort Worth-Arlington, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:18:41-05,1,1,No description provided. -696,SMU22353805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New Orleans-Metairie, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:48:16-05,1,1,No description provided. -697,SMU48231045552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Fort Worth-Arlington, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:19:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48231045552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -698,SMU22353805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New Orleans-Metairie, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:52:59-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU22353805552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -699,BOGZ1FR113169563Q,2022-03-17,2022-03-17,"Nonfinancial Noncorporate Business; Finance Companies Loans from Paycheck Protection Program; Liability, Revaluation",1946-10-01,2021-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2021-12-20 09:36:58-06,1,1,"Source ID: FR113169563.Q +668,SMU06473005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Visalia-Porterville, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:43-05,1,1,No description provided. +669,SMU06473005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Visalia-Porterville, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:33-05,1,1,No description provided. +670,SMS18000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Indiana,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:20-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +671,SMU18000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Indiana,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:52:51-05,1,1,No description provided. +672,BOGZ1FR616330023Q,2022-03-21,2022-03-21,"Finance Companies; Consumption of Fixed Capital, Nonresidential Equipment, Current Cost Basis, Revaluation",1947-01-01,1951-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2019-05-23 14:32:02-05,1,1,"Source ID: FR616330023.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR113169563&t=) provided by the source." -700,SMU39174605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cleveland-Elyria, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:52-05,1,1,No description provided. -701,SMU39174605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cleveland-Elyria, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:59-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR616330023&t=) provided by the source." +673,SMU06360845552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Oakland-Hayward-Berkeley, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:21-05,1,1,No description provided. +674,SMU06360845552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Oakland-Hayward-Berkeley, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:12:42-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06360845552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +675,SMU06360845552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Oakland-Hayward-Berkeley, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:25-05,1,1,No description provided. +676,BOGZ1FA613065200A,2022-03-21,2022-03-21,"Finance Companies; Home Equity Lines of Credit; Asset, Transactions",1971-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613065200.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065200&t=) provided by the source." +677,BOGZ1FA613065200Q,2022-03-21,2022-03-21,"Finance Companies; Home Equity Lines of Credit; Asset, Transactions",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613065200.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065200&t=) provided by the source." +678,SMU49416205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Salt Lake City, UT (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:17:30-05,1,1,No description provided. +679,SMU49416205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Salt Lake City, UT (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:16:57-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU49416205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +680,SMU48231045552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Fort Worth-Arlington, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:18:41-05,1,1,No description provided. +681,SMU22353805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New Orleans-Metairie, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:48:16-05,1,1,No description provided. +682,SMU48231045552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Fort Worth-Arlington, TX (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:19:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48231045552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +683,SMU22353805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New Orleans-Metairie, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:52:59-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU22353805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +684,SMU44000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Rhode Island,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:25:03-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU44000005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +685,SMU44000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Rhode Island,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:22:46-05,1,1,No description provided. +686,SMU44000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Rhode Island,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:43-05,1,1,No description provided. +687,SMU39174605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cleveland-Elyria, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:52-05,1,1,No description provided. +688,SMU39174605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cleveland-Elyria, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:59-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39174605552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -702,SMU39174605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cleveland-Elyria, OH (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:16-05,1,1,No description provided. -703,SMS18000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Indiana,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:20-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -704,SMU18000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Indiana,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:52:51-05,1,1,No description provided. -705,BOGZ1FA613163093Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613163093.Q +689,SMU39174605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cleveland-Elyria, OH (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:16-05,1,1,No description provided. +690,SMU39000005552000008SA,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2019-01-18 17:10:45-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. +691,SMU39000005552000008,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2019-01-18 17:13:38-06,1,1,No description provided. +692,SMU39000005552000008A,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2017-01-01,Annual,A,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2018-03-12 15:13:03-05,1,1,No description provided. +693,SMU41000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Oregon,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:27:33-05,1,1,No description provided. +694,SMU41000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Oregon,1990-01-01,2017-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-03-13 14:23:54-05,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +695,SMU41000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Oregon,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:55-05,1,1,No description provided. +696,SMU16142605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boise City, ID (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:01:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU16142605552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +697,SMU16142605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boise City, ID (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:54:19-05,1,1,No description provided. +698,SMU16142605552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boise City, ID (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:17-05,1,1,No description provided. +699,SMS24000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Maryland,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:33-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +700,SMU24000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Maryland,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:58-05,1,1,No description provided. +701,SMU24000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Maryland,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:37:09-05,1,1,No description provided. +702,HOLN0A175MFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:37:43-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +703,HOLN0A158MFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1990-02-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted,SA,2022-03-11 15:51:35-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +704,HOLN0A674AFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1991-01-01,2021-01-01,Annual,A,December to December Percent Change,Dec to Dec % Chg.,Not Seasonally Adjusted,NSA,2022-03-11 14:37:43-06,1,1,"The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. + +Please note that this annual series created by the Federal Reserve bank of Dallas was calculated based on a seasonally adjusted monthly series." +705,BOGZ1FA613163083Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613163083.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613163083&t=) provided by the source." +706,BOGZ1FA613163083A,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613163083.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613163083&t=) provided by the source." +707,SMU34350845552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Newark, NJ-PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:36:13-05,1,1,No description provided. +708,SMU34350845552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Newark, NJ-PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:38:57-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34350845552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +709,SMS26000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Michigan,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:26-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +710,SMU26000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Michigan,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:44:12-05,1,1,No description provided. +711,SMU26000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Michigan,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:35:33-05,1,1,No description provided. +712,BOGZ1FA613163093Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613163093.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613163093&t=) provided by the source." -706,BOGZ1FU613163093Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU613163093.Q +713,BOGZ1FU613163093Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bonds, Notes, Bonds, and Debentures; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:12-05,1,1,"Source ID: FU613163093.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU613163093&t=) provided by the source." -707,SMU39000005552000008SA,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2019-01-18 17:10:45-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. -708,BOGZ1FA613065200A,2022-03-17,2022-03-17,"Finance Companies; Home Equity Lines of Credit; Asset, Transactions",1971-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613065200.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065200&t=) provided by the source." -709,BOGZ1FA613065200Q,2022-03-17,2022-03-17,"Finance Companies; Home Equity Lines of Credit; Asset, Transactions",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:35-05,1,1,"Source ID: FA613065200.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065200&t=) provided by the source." -710,SMU39000005552000008,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2019-01-18 17:13:38-06,1,1,No description provided. -711,SMU39000005552000008A,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2017-01-01,Annual,A,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2018-03-12 15:13:03-05,1,1,No description provided. -712,SMU34350845552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Newark, NJ-PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:36:13-05,1,1,No description provided. -713,SMU34350845552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Newark, NJ-PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:38:57-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34350845552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -714,SMU44000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Rhode Island,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:25:03-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU44000005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -715,SMU44000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Rhode Island,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:22:46-05,1,1,No description provided. -716,SMU44000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Rhode Island,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:43-05,1,1,No description provided. -717,SMU41000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Oregon,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:27:33-05,1,1,No description provided. -718,SMU41000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Oregon,1990-01-01,2017-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-03-13 14:23:54-05,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -719,SMU41000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Oregon,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:55-05,1,1,No description provided. -720,SMU16142605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Boise City, ID (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:01:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU16142605552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -721,SMU16142605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Boise City, ID (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:54:19-05,1,1,No description provided. -722,SMU16142605552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Boise City, ID (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:17-05,1,1,No description provided. -723,SMS24000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Maryland,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:33-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -724,SMU24000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Maryland,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:58-05,1,1,No description provided. -725,SMU24000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Maryland,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:37:09-05,1,1,No description provided. -726,HOLN0A175MFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:37:43-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -727,HOLN0A158MFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1990-02-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted,SA,2022-03-11 15:51:35-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -728,HOLN0A674AFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1991-01-01,2021-01-01,Annual,A,December to December Percent Change,Dec to Dec % Chg.,Not Seasonally Adjusted,NSA,2022-03-11 14:37:43-06,1,1,"The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. - -Please note that this annual series created by the Federal Reserve bank of Dallas was calculated based on a seasonally adjusted monthly series." -729,BOGZ1FA613163083Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613163083.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613163083&t=) provided by the source." -730,BOGZ1FA613163083A,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:12-05,1,1,"Source ID: FA613163083.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613163083&t=) provided by the source." -731,SMS26000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Michigan,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:26-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -732,SMU26000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Michigan,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:44:12-05,1,1,No description provided. -733,SMU26000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Michigan,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:35:33-05,1,1,No description provided. -734,SMU26243405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Grand Rapids-Wyoming, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:46:29-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +714,SMU26243405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Grand Rapids-Wyoming, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:46:29-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26243405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -735,SMU26243405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Grand Rapids-Wyoming, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:42:24-05,1,1,No description provided. -736,SMU26243405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Grand Rapids-Wyoming, MI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:35:29-05,1,1,No description provided. -737,SMS30000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Montana,1990-01-01,2016-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-01-24 13:47:01-06,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -738,SMU30000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Montana,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:39:47-05,1,1,No description provided. -739,BOGZ1FL113169563A,2022-03-17,2022-03-17,"Nonfinancial Noncorporate Business; Finance Companies Loans from Paycheck Protection Program; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:05:24-05,1,1,"Source ID: FL113169563.A +715,SMU26243405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Grand Rapids-Wyoming, MI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:42:24-05,1,1,No description provided. +716,SMU26243405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Grand Rapids-Wyoming, MI (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:35:29-05,1,1,No description provided. +717,SMS49000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Utah,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:53-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +718,SMU49000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Utah,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:22:52-05,1,1,No description provided. +719,SMU49000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Utah,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:17:08-05,1,1,No description provided. +720,BOGZ1FR113169563Q,2022-03-21,2022-03-21,"Nonfinancial Noncorporate Business; Finance Companies Loans from Paycheck Protection Program; Liability, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-18 13:02:35-05,1,1,"Source ID: FR113169563.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR113169563&t=) provided by the source." +721,SMS30000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Montana,1990-01-01,2016-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-01-24 13:47:01-06,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +722,SMU30000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Montana,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:39:47-05,1,1,No description provided. +723,SMS38000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in North Dakota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:32-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +724,SMU38000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in North Dakota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:54-05,1,1,No description provided. +725,SMU38000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in North Dakota,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:28:44-05,1,1,No description provided. +726,BOGZ1FL113169563A,2022-03-21,2022-03-21,"Nonfinancial Noncorporate Business; Finance Companies Loans from Paycheck Protection Program; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:05:24-05,1,1,"Source ID: FL113169563.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL113169563&t=) provided by the source." -740,SMS49000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Utah,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:53-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -741,SMU49000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Utah,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:22:52-05,1,1,No description provided. -742,SMU49000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Utah,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:17:08-05,1,1,No description provided. -743,SMS38000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in North Dakota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:32-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -744,SMU38000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in North Dakota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:54-05,1,1,No description provided. -745,SMU38000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in North Dakota,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:28:44-05,1,1,No description provided. -746,BOGZ1FR613178033Q,2022-03-17,2022-03-17,"Finance Companies; Taxes Payable to the Federal Government; Liability, Revaluation",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:25:30-06,1,1,"Source ID: FR613178033.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613178033&t=) provided by the source." -747,SMU15000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Hawaii,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:01:41-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +727,SMU15000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Hawaii,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:01:41-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU15000005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -748,SMU15000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Hawaii,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:55:43-05,1,1,No description provided. -749,SMU15000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Hawaii,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:49-05,1,1,No description provided. -750,SMS37000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in North Carolina,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:35-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -751,SMU37000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in North Carolina,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:05-05,1,1,No description provided. -752,SMU37000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in North Carolina,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:30-05,1,1,No description provided. -753,AULN0A175MFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Austin-Round Rock-San Marcos, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:35:16-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -754,AULN0A158MFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Austin-Round Rock-San Marcos, TX (MSA)",1990-02-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted,SA,2022-03-11 14:35:14-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -755,AULN0A674AFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Austin-Round Rock-San Marcos, TX (MSA)",1991-01-01,2021-01-01,Annual,A,December to December Percent Change,Dec to Dec % Chg.,Not Seasonally Adjusted,NSA,2022-03-11 14:35:16-06,1,1,"The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +728,SMU15000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Hawaii,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:55:43-05,1,1,No description provided. +729,SMU15000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Hawaii,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:49-05,1,1,No description provided. +730,SMS37000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in North Carolina,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:35-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +731,SMU37000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in North Carolina,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:05-05,1,1,No description provided. +732,SMU37000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in North Carolina,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:30-05,1,1,No description provided. +733,AULN0A175MFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Austin-Round Rock-San Marcos, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:35:16-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +734,AULN0A158MFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Austin-Round Rock-San Marcos, TX (MSA)",1990-02-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted,SA,2022-03-11 14:35:14-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +735,AULN0A674AFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Austin-Round Rock-San Marcos, TX (MSA)",1991-01-01,2021-01-01,Annual,A,December to December Percent Change,Dec to Dec % Chg.,Not Seasonally Adjusted,NSA,2022-03-11 14:35:16-06,1,1,"The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. Please note that this annual series created by the Federal Reserve bank of Dallas was calculated based on a seasonally adjusted monthly series." -756,BOGZ1FA613065593Q,2022-03-17,2022-03-17,"Finance Companies; Commercial Mortgages Issued by Non-Depository Financial Institutions; Asset, Flow",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-12-16 12:22:28-06,1,1,"Source ID: FA613065593.Q +736,BOGZ1FA613065593Q,2022-03-21,2022-03-21,"Finance Companies; Commercial Mortgages Issued by Non-Depository Financial Institutions; Asset, Flow",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted,SA,2020-12-16 12:22:28-06,1,1,"Source ID: FA613065593.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065593&t=) provided by the source." -757,SMS56000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Wyoming,1990-01-01,2016-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-01-24 13:47:04-06,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -758,SMU56000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Wyoming,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:12:11-05,1,1,No description provided. -759,BOGZ1LM613092003A,2022-03-17,2022-03-17,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Market Value Levels",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:55:52-06,1,1,"Source ID: LM613092003.A +737,SMS56000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Wyoming,1990-01-01,2016-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-01-24 13:47:04-06,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +738,SMU56000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Wyoming,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:12:11-05,1,1,No description provided. +739,BOGZ1FR613178033Q,2022-03-21,2022-03-21,"Finance Companies; Taxes Payable to the Federal Government; Liability, Revaluation",1946-10-01,2020-07-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2020-12-16 12:25:30-06,1,1,"Source ID: FR613178033.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613092003&t=) provided by the source." -760,BOGZ1LM613092003Q,2022-03-17,2022-03-17,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Market Value Levels",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:46:50-06,1,1,"Source ID: LM613092003.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613178033&t=) provided by the source." +740,SMS29000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Missouri,1990-01-01,2016-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-01-24 13:47:31-06,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +741,SMU29000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Missouri,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:41:57-05,1,1,No description provided. +742,SMU29000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Missouri,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:19-05,1,1,No description provided. +743,SMS50000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Vermont,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:50-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +744,SMU50000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Vermont,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:16:25-05,1,1,No description provided. +745,SMU50000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Vermont,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:00-05,1,1,No description provided. +746,DALN0A175MFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:42:00-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +747,DALN0A158MFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1990-02-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted,SA,2022-03-11 14:42:00-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +748,DALN0A674AFRBDAL,2022-03-21,2022-03-21,"Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1991-01-01,2021-01-01,Annual,A,December to December Percent Change,Dec to Dec % Chg.,Not Seasonally Adjusted,NSA,2022-03-11 14:41:59-06,1,1,"The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613092003&t=) provided by the source." -761,SMS29000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Missouri,1990-01-01,2016-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-01-24 13:47:31-06,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -762,SMU29000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Missouri,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:41:57-05,1,1,No description provided. -763,SMU29000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Missouri,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:19-05,1,1,No description provided. -764,BOGZ1FL613069505Q,2022-03-17,2022-03-17,"Finance Companies; Nonfinancial Business Loans, Including Paycheck Protection Program Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:08-05,1,1,"Source ID: FL613069505.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613069505&t=) provided by the source." -765,BOGZ1FL713068863Q,2022-03-17,2022-03-17,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:27-06,1,1,"Source ID: FL713068863.Q +Please note that this annual series created by the Federal Reserve bank of Dallas was calculated based on a seasonally adjusted monthly series." +749,BOGZ1FL713068863Q,2022-03-21,2022-03-21,"Monetary Authority; Loans to Finance Companies Under the Paycheck Protection Program Liquidity Facility (PPPLF); Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:27-06,1,1,"Source ID: FL713068863.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL713068863&t=) provided by the source." -766,SMS50000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Vermont,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:05:50-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -767,SMU50000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Vermont,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:16:25-05,1,1,No description provided. -768,SMU50000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Vermont,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:23:00-05,1,1,No description provided. -769,SMU27334605552000030SA,2022-03-17,2022-03-17,"Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:44:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +750,SMS23000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Maine,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:36-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +751,SMU23000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Maine,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:48:51-05,1,1,No description provided. +752,SMU27334605552000030SA,2022-03-21,2022-03-21,"Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:44:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27334605552000030). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -770,SMU27334605552000030,2022-03-17,2022-03-17,"Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:41:59-05,1,1,No description provided. -771,SMU27334605552000030A,2022-03-17,2022-03-17,"Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2003-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:34:49-05,1,1,No description provided. -772,DALN0A175MFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:42:00-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -773,DALN0A158MFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1990-02-01,2022-01-01,Monthly,M,Percent Change at Annual Rate,% Chg. at Annual Rate,Seasonally Adjusted,SA,2022-03-11 14:42:00-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -774,DALN0A674AFRBDAL,2022-03-17,2022-03-17,"Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1991-01-01,2021-01-01,Annual,A,December to December Percent Change,Dec to Dec % Chg.,Not Seasonally Adjusted,NSA,2022-03-11 14:41:59-06,1,1,"The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. - -Please note that this annual series created by the Federal Reserve bank of Dallas was calculated based on a seasonally adjusted monthly series." -775,SMS23000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Maine,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:36-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -776,SMU23000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Maine,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:48:51-05,1,1,No description provided. -777,SMS13000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Georgia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:58-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -778,SMU13000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Georgia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:57:21-05,1,1,No description provided. -779,SMU06112445552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Anaheim-Santa Ana-Irvine, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:30-05,1,1,No description provided. -780,SMU06112445552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Anaheim-Santa Ana-Irvine, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:54-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06112445552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -781,SMU06112445552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Anaheim-Santa Ana-Irvine, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:12-05,1,1,No description provided. -782,SMU24925815552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baltimore City, MD",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:19-05,1,1,"Baltimore City, MD constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Baltimore-Columbia-Townson MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -783,SMU24925815552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baltimore City, MD",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:51:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +753,SMU27334605552000030,2022-03-21,2022-03-21,"Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:41:59-05,1,1,No description provided. +754,SMU27334605552000030A,2022-03-21,2022-03-21,"Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2003-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:34:49-05,1,1,No description provided. +755,SMS13000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Georgia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:58-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +756,SMU13000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Georgia,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:57:21-05,1,1,No description provided. +757,SMU24925815552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baltimore City, MD",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:46:19-05,1,1,"Baltimore City, MD constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Baltimore-Columbia-Townson MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +758,SMU24925815552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baltimore City, MD",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:51:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU24925815552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" Baltimore City, MD constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Baltimore-Columbia-Townson MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -784,SMU24925815552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baltimore City, MD",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:36:49-05,1,1,"Baltimore City, MD constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Baltimore-Columbia-Townson MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -785,BOGZ1FR613163083Q,2022-03-17,2022-03-17,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613163083.Q +759,SMU24925815552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baltimore City, MD",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:36:49-05,1,1,"Baltimore City, MD constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Baltimore-Columbia-Townson MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +760,SMU06112445552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Anaheim-Santa Ana-Irvine, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:30-05,1,1,No description provided. +761,SMU06112445552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Anaheim-Santa Ana-Irvine, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:54-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06112445552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +762,SMU06112445552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Anaheim-Santa Ana-Irvine, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:12-05,1,1,No description provided. +763,BOGZ1LM613092003Q,2022-03-21,2022-03-21,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Market Value Levels",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:46:50-06,1,1,"Source ID: LM613092003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613092003&t=) provided by the source." +764,BOGZ1LM613092003A,2022-03-21,2022-03-21,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Market Value Levels",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:55:52-06,1,1,"Source ID: LM613092003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM613092003&t=) provided by the source." +765,BOGZ1FL613069505Q,2022-03-21,2022-03-21,"Finance Companies; Nonfinancial Business Loans, Including Paycheck Protection Program Loans; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:02:08-05,1,1,"Source ID: FL613069505.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613069505&t=) provided by the source." +766,SMU10488645552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Wilmington, DE-MD-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:08:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU10488645552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +767,SMU10488645552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Wilmington, DE-MD-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:38-05,1,1,No description provided. +768,SMU10488645552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Wilmington, DE-MD-NJ (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:14-05,1,1,No description provided. +769,BOGZ1FR613163083Q,2022-03-21,2022-03-21,"Finance Companies; Corporate and Foreign Bond, Non-Recourse Debt; Liability, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613163083.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613163083&t=) provided by the source." -786,SMU10488645552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Wilmington, DE-MD-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:08:13-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +770,SMU42109005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Allentown-Bethlehem-Easton, PA-NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:28:20-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU10488645552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42109005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -787,SMU10488645552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Wilmington, DE-MD-NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:38-05,1,1,No description provided. -788,SMU10488645552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Wilmington, DE-MD-NJ (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:14-05,1,1,No description provided. -789,SMU12484245552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in West Palm Beach-Boca Raton-Delray Beach, FL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:50-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +771,SMU42109005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Allentown-Bethlehem-Easton, PA-NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:25:39-05,1,1,No description provided. +772,SMU42109005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Allentown-Bethlehem-Easton, PA-NJ (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:01-05,1,1,No description provided. +773,SMU12484245552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in West Palm Beach-Boca Raton-Delray Beach, FL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:04:50-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12484245552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -790,SMU12484245552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in West Palm Beach-Boca Raton-Delray Beach, FL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:56:39-05,1,1,No description provided. -791,BOGZ1FR613065183Q,2022-03-17,2022-03-17,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613065183.Q +774,SMU12484245552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in West Palm Beach-Boca Raton-Delray Beach, FL (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:56:39-05,1,1,No description provided. +775,BOGZ1FR613065183Q,2022-03-21,2022-03-21,"Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR613065183.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613065183&t=) provided by the source." -792,SMU42109005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Allentown-Bethlehem-Easton, PA-NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:28:20-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42109005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -793,SMU42109005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Allentown-Bethlehem-Easton, PA-NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:25:39-05,1,1,No description provided. -794,SMU42109005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Allentown-Bethlehem-Easton, PA-NJ (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:27:01-05,1,1,No description provided. -795,SMU22129405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baton Rouge, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:53:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +776,SMU22129405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baton Rouge, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:53:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU22129405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -796,SMU22129405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baton Rouge, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:30-05,1,1,No description provided. -797,SMU22129405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Baton Rouge, LA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:37:44-05,1,1,No description provided. -798,BOGZ1FA613093005A,2022-03-17,2022-03-17,"Finance Companies; Unidentified Miscellaneous Assets, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:02-06,1,1,"Source ID: FA613093005.A +777,SMU22129405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baton Rouge, LA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:30-05,1,1,No description provided. +778,SMU22129405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Baton Rouge, LA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:37:44-05,1,1,No description provided. +779,SMU16000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Idaho,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:54:27-05,1,1,No description provided. +780,SMS16000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Idaho,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:26-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +781,SMU16000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Idaho,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:23-05,1,1,No description provided. +782,BOGZ1FA613093005A,2022-03-21,2022-03-21,"Finance Companies; Unidentified Miscellaneous Assets, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:02-06,1,1,"Source ID: FA613093005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613093005&t=) provided by the source." -799,SMU16000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Idaho,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:54:27-05,1,1,No description provided. -800,SMS16000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Idaho,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:26-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -801,SMU16000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Idaho,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:41:23-05,1,1,No description provided. -802,BOGZ1FA615090005Q,2022-03-17,2022-03-17,"Finance Companies; Gross Investment, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA615090005.Q +783,BOGZ1FA615090005Q,2022-03-21,2022-03-21,"Finance Companies; Gross Investment, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:10-05,1,1,"Source ID: FA615090005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615090005&t=) provided by the source." -803,BOGZ1FL613020003Q,2022-03-17,2022-03-17,"Finance Companies; Checkable Deposits and Currency; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613020003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613020003&t=) provided by the source." -804,SMU09757005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New Haven, CT (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:41-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +784,SMU09757005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New Haven, CT (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:41-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU09757005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -805,SMU09757005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New Haven, CT (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:28-05,1,1,No description provided. -806,SMU09757005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New Haven, CT (NECTA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:55-05,1,1,No description provided. -807,SMU40000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Oklahoma,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU40000005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -808,SMU40000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Oklahoma,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:16-05,1,1,No description provided. -809,SMU40000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Oklahoma,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:28:08-05,1,1,No description provided. -810,SMU42979615552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Philadelphia City, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:00-05,1,1,"Philadelphia City, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -811,SMU42979615552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Philadelphia City, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:25:30-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +785,SMU09757005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New Haven, CT (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:28-05,1,1,No description provided. +786,SMU09757005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New Haven, CT (NECTA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:55-05,1,1,No description provided. +787,SMU42979615552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Philadelphia City, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:00-05,1,1,"Philadelphia City, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +788,SMU42979615552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Philadelphia City, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:25:30-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42979615552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" Philadelphia City, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -812,SMU42979615552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Philadelphia City, PA",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:12-05,1,1,"Philadelphia City, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -813,SMU39193805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dayton, OH (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:28:25-05,1,1,No description provided. -814,SMU39193805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dayton, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:40-05,1,1,No description provided. -815,SMU39193805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Dayton, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:40-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +789,SMU42979615552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Philadelphia City, PA",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:12-05,1,1,"Philadelphia City, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +790,SMU40000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Oklahoma,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39193805552000001). +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU40000005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -816,SMU34207645552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Edison-New Brunswick, NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:29:40-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -817,SMU34207645552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Edison-New Brunswick, NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:29:40-06,1,1,No description provided. -818,BOGZ1FL613066303Q,2022-03-17,2022-03-17,"Finance Companies; Consumer Leases; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613066303.Q +791,SMU40000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Oklahoma,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:16-05,1,1,No description provided. +792,SMU40000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Oklahoma,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:28:08-05,1,1,No description provided. +793,BOGZ1FL613020003Q,2022-03-21,2022-03-21,"Finance Companies; Checkable Deposits and Currency; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:53-06,1,1,"Source ID: FL613020003.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066303&t=) provided by the source." -819,BOGZ1FR613070103Q,2022-03-17,2022-03-17,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:50-06,1,1,"Source ID: FR613072003.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613020003&t=) provided by the source." +794,SMU34207645552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Edison-New Brunswick, NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:29:40-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +795,SMU34207645552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Edison-New Brunswick, NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:29:40-06,1,1,No description provided. +796,BOGZ1FR613070103Q,2022-03-21,2022-03-21,"Finance Companies; Paycheck Protection Program Receivables Due from Federal Government; Asset, Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:50-06,1,1,"Source ID: FR613072003.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613072003&t=) provided by the source." -820,SMS48000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Texas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:46-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -821,SMU48000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Texas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:18:58-05,1,1,No description provided. -822,SMU48000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Texas,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:24:53-05,1,1,No description provided. -823,SMS33000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New Hampshire,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:51-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -824,SMU33000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New Hampshire,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:01-05,1,1,No description provided. -825,SMU33000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New Hampshire,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:50-05,1,1,No description provided. -826,BOGZ1FA617005005Q,2022-03-17,2022-03-17,"Finance Companies; Sector Discrepancy, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA617005005.Q +797,SMU39193805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dayton, OH (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:28:25-05,1,1,No description provided. +798,SMU39193805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dayton, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:40-05,1,1,No description provided. +799,SMU39193805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Dayton, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:29:40-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39193805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +800,SMS48000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Texas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:46-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +801,SMU48000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Texas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:18:58-05,1,1,No description provided. +802,SMU48000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Texas,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:24:53-05,1,1,No description provided. +803,BOGZ1FL613066303Q,2022-03-21,2022-03-21,"Finance Companies; Consumer Leases; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:52-06,1,1,"Source ID: FL613066303.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA617005005&t=) provided by the source." -827,SMS27000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Minnesota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:55-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -828,SMU27000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Minnesota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:40:59-05,1,1,No description provided. -829,SMU27000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Minnesota,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. -830,BOGZ1FA615013005Q,2022-03-17,2022-03-17,"Finance Companies; Gross Fixed Investment, Nonresidential Structures, Equipment, and Intellectual Property Products Excluding Securitized Equipment, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA615013005.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613066303&t=) provided by the source." +804,SMS33000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New Hampshire,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:51-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +805,SMU33000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New Hampshire,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:01-05,1,1,No description provided. +806,SMU33000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New Hampshire,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:50-05,1,1,No description provided. +807,SMS27000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Minnesota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:55-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +808,SMU27000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Minnesota,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:40:59-05,1,1,No description provided. +809,SMU27000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Minnesota,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. +810,BOGZ1FA615013005Q,2022-03-21,2022-03-21,"Finance Companies; Gross Fixed Investment, Nonresidential Structures, Equipment, and Intellectual Property Products Excluding Securitized Equipment, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA615013005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615013005&t=) provided by the source." -831,BOGZ1FU615013005A,2022-03-17,2022-03-17,"Finance Companies; Gross Fixed Investment, Nonresidential Structures, Equipment, and Intellectual Property Products Excluding Securitized Equipment, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:06-06,1,1,"Source ID: FU615013005.A +811,BOGZ1FU615013005A,2022-03-21,2022-03-21,"Finance Companies; Gross Fixed Investment, Nonresidential Structures, Equipment, and Intellectual Property Products Excluding Securitized Equipment, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:58:06-06,1,1,"Source ID: FU615013005.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FU615013005&t=) provided by the source." -832,BACBANAICS52NSAUS,2022-03-17,2022-03-17,Business Applications from Corporations: Finance and Insurance in the United States,2004-07-01,2022-02-01,Monthly,M,Number,Number,Not Seasonally Adjusted,NSA,2022-03-09 11:05:28-06,1,1,"High-Propensity Business Applications (HBA) from a corporation or personal service corporation, based on the legal form of organization stated in the IRS Form SS-4. +812,BOGZ1FA617005005Q,2022-03-21,2022-03-21,"Finance Companies; Sector Discrepancy, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:54:52-06,1,1,"Source ID: FA617005005.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA617005005&t=) provided by the source." +813,BACBANAICS52NSAUS,2022-03-21,2022-03-21,Business Applications from Corporations: Finance and Insurance in the United States,2004-07-01,2022-02-01,Monthly,M,Number,Number,Not Seasonally Adjusted,NSA,2022-03-09 11:05:28-06,1,1,"High-Propensity Business Applications (HBA) from a corporation or personal service corporation, based on the legal form of organization stated in the IRS Form SS-4. See the U.S. Census's FAQs (https://www.census.gov/econ/bfs/faqs.html) for more details." -833,BACBANAICS52SAUS,2022-03-17,2022-03-17,Business Applications from Corporations: Finance and Insurance in the United States,2004-07-01,2022-02-01,Monthly,M,Number,Number,Seasonally Adjusted,SA,2022-03-09 11:05:28-06,1,1,"High-Propensity Business Applications (HBA) from a corporation or personal service corporation, based on the legal form of organization stated in the IRS Form SS-4. +814,BACBANAICS52SAUS,2022-03-21,2022-03-21,Business Applications from Corporations: Finance and Insurance in the United States,2004-07-01,2022-02-01,Monthly,M,Number,Number,Seasonally Adjusted,SA,2022-03-09 11:05:28-06,1,1,"High-Propensity Business Applications (HBA) from a corporation or personal service corporation, based on the legal form of organization stated in the IRS Form SS-4. See the U.S. Census's FAQs (https://www.census.gov/econ/bfs/faqs.html) for more details." -834,BOGZ1FL673069503Q,2022-03-17,2022-03-17,"Issuers of Asset-Backed Securities; Nonfinancial Business Loans Securitized by Finance Companies; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:07-05,1,1,"Source ID: FL673069503.Q +815,BOGZ1FL673069503Q,2022-03-21,2022-03-21,"Issuers of Asset-Backed Securities; Nonfinancial Business Loans Securitized by Finance Companies; Asset, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:07-05,1,1,"Source ID: FL673069503.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL673069503&t=) provided by the source." -835,BOGZ1FL673069503A,2022-03-17,2022-03-17,"Issuers of Asset-Backed Securities; Nonfinancial Business Loans Securitized by Finance Companies; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:07-05,1,1,"Source ID: FL673069503.A +816,BOGZ1FL673069503A,2022-03-21,2022-03-21,"Issuers of Asset-Backed Securities; Nonfinancial Business Loans Securitized by Finance Companies; Asset, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:04:07-05,1,1,"Source ID: FL673069503.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL673069503&t=) provided by the source." -836,BOGZ1FL613194003Q,2022-03-17,2022-03-17,"Finance Companies; Investment by Parent Companies; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:50-06,1,1,"Source ID: FL613194003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613194003&t=) provided by the source." -837,BOGZ1FL613194003A,2022-03-17,2022-03-17,"Finance Companies; Investment by Parent Companies; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL613194003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613194003&t=) provided by the source." -838,SMU45000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in South Carolina,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:13-05,1,1,No description provided. -839,SMU45000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in South Carolina,1990-01-01,2017-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-03-13 14:05:30-05,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -840,SMU06125405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Bakersfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:25-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +817,SMU45000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in South Carolina,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:13-05,1,1,No description provided. +818,SMU45000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in South Carolina,1990-01-01,2017-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2017-03-13 14:05:30-05,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +819,SMU06125405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bakersfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:25-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06125405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -841,SMU06125405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Bakersfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:08-05,1,1,No description provided. -842,SMU06125405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Bakersfield, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:06-05,1,1,No description provided. -843,SMS32000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Nevada,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:53-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -844,SMU32000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Nevada,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:38:06-05,1,1,No description provided. -845,SMU32000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Nevada,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:05-05,1,1,No description provided. -846,SMU06420445552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Santa Ana-Anaheim-Irvine, CA (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:01:22-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -847,SMU06420445552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Santa Ana-Anaheim-Irvine, CA (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:01:22-06,1,1,No description provided. -848,SMU06420445552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Santa Ana-Anaheim-Irvine, CA (MD) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 20:03:10-05,1,1,No description provided. -849,SMU28271405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Jackson, MS (MSA)",2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:41:25-05,1,1,No description provided. -850,SMU28271405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Jackson, MS (MSA)",2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:43:46-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +820,SMU06125405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bakersfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:08-05,1,1,No description provided. +821,SMU06125405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bakersfield, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:06-05,1,1,No description provided. +822,SMU06420445552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Santa Ana-Anaheim-Irvine, CA (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:01:22-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +823,SMU06420445552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Santa Ana-Anaheim-Irvine, CA (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:01:22-06,1,1,No description provided. +824,SMU06420445552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Santa Ana-Anaheim-Irvine, CA (MD) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 20:03:10-05,1,1,No description provided. +825,SMS32000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Nevada,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:53-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +826,SMU32000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Nevada,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:38:06-05,1,1,No description provided. +827,SMU32000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Nevada,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:05-05,1,1,No description provided. +828,SMU28271405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Jackson, MS (MSA)",2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:41:25-05,1,1,No description provided. +829,SMU28271405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Jackson, MS (MSA)",2001-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:43:46-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU28271405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -851,SMU28271405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Jackson, MS (MSA)",2001-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:55-05,1,1,No description provided. -852,SMS34000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New Jersey,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:43-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -853,SMU34000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New Jersey,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:28-05,1,1,No description provided. -854,SMU34000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in New Jersey,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:07-05,1,1,No description provided. -855,SMU42338745552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Montgomery County-Bucks County-Chester County, PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:24:33-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +830,SMU28271405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Jackson, MS (MSA)",2001-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:55-05,1,1,No description provided. +831,SMS34000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New Jersey,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:06:43-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +832,SMU34000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New Jersey,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:28-05,1,1,No description provided. +833,SMU34000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in New Jersey,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:07-05,1,1,No description provided. +834,BOGZ1FL613194003Q,2022-03-21,2022-03-21,"Finance Companies; Investment by Parent Companies; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:50-06,1,1,"Source ID: FL613194003.Q -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42338745552000001). +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -856,SMU42338745552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Montgomery County-Bucks County-Chester County, PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:45-05,1,1,No description provided. -857,SMU42338745552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Montgomery County-Bucks County-Chester County, PA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:35-05,1,1,No description provided. -858,SMS31000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Nebraska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:45-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -859,SMU31000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Nebraska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:38:34-05,1,1,No description provided. -860,SMU31000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Nebraska,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:32-05,1,1,No description provided. -861,SMU55000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Wisconsin,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613194003&t=) provided by the source." +835,BOGZ1FL613194003A,2022-03-21,2022-03-21,"Finance Companies; Investment by Parent Companies; Liability, Level",1945-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 12:00:03-06,1,1,"Source ID: FL613194003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613194003&t=) provided by the source." +836,SMS31000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Nebraska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:07:45-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +837,SMU31000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Nebraska,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:38:34-05,1,1,No description provided. +838,SMU31000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Nebraska,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:33:32-05,1,1,No description provided. +839,SMU55000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Wisconsin,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU55000005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -862,SMU55000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Wisconsin,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:12:57-05,1,1,No description provided. -863,SMU55000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Wisconsin,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:20:31-05,1,1,No description provided. -864,SMU28000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Mississippi,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:44:00-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +840,SMU55000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Wisconsin,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:12:57-05,1,1,No description provided. +841,SMU55000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Wisconsin,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:20:31-05,1,1,No description provided. +842,SMU28000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Mississippi,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:44:00-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU28000005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -865,SMU28000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Mississippi,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:40:24-05,1,1,No description provided. -866,SMU28000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Mississippi,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:11-05,1,1,No description provided. -867,SMU39000005552000030SA,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2019-01-18 17:10:44-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. -868,SMU39000005552000030,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2019-01-18 17:13:38-06,1,1,No description provided. -869,SMU39000005552000030A,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2017-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2018-03-12 15:13:03-05,1,1,No description provided. -870,BOGZ1FA673069503Q,2022-03-17,2022-03-17,"Issuers of Asset-Backed Securities; Nonfinancial Business Loans Securitized by Finance Companies; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:02-05,1,1,"Source ID: FA673069503.Q +843,SMU28000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Mississippi,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:40:24-05,1,1,No description provided. +844,SMU28000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Mississippi,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:11-05,1,1,No description provided. +845,SMU42338745552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Montgomery County-Bucks County-Chester County, PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:24:33-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42338745552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +846,SMU42338745552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Montgomery County-Bucks County-Chester County, PA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:23:45-05,1,1,No description provided. +847,SMU42338745552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Montgomery County-Bucks County-Chester County, PA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:35-05,1,1,No description provided. +848,BOGZ1FA673069503Q,2022-03-21,2022-03-21,"Issuers of Asset-Backed Securities; Nonfinancial Business Loans Securitized by Finance Companies; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:02-05,1,1,"Source ID: FA673069503.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA673069503&t=) provided by the source." -871,BOGZ1FR613065123Q,2022-03-17,2022-03-17,"Finance Companies; Home Equity Loans; Asset, Revaluation",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:50-06,1,1,"Source ID: FR613065123.Q +849,SMU06310845552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Glendale, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:16-05,1,1,No description provided. +850,SMU06310845552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Glendale, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:14:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06310845552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +851,SMU06310845552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Glendale, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:33-05,1,1,No description provided. +852,BOGZ1FR613065123Q,2022-03-21,2022-03-21,"Finance Companies; Home Equity Loans; Asset, Revaluation",1970-07-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:49:50-06,1,1,"Source ID: FR613065123.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR613065123&t=) provided by the source." -872,SMU06310845552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Glendale, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:16-05,1,1,No description provided. -873,SMU06310845552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Glendale, CA (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:14:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06310845552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -874,SMU06310845552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Glendale, CA (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:47:33-05,1,1,No description provided. -875,SMS20000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Kansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:15-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. -876,SMU20000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Kansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:50:32-05,1,1,No description provided. -877,BOGZ1FA613092003Q,2022-03-17,2022-03-17,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613092003.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613092003&t=) provided by the source." -878,BOGZ1FA613092003A,2022-03-17,2022-03-17,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:02-06,1,1,"Source ID: FA613092003.A - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613092003&t=) provided by the source." -879,SMU20458205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Topeka, KS (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:50:24-05,1,1,No description provided. -880,SMU20458205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Topeka, KS (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:56:21-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +853,SMS20000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Kansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:08:15-05,1,1,This series is seasonally adjusted by the U.S. Bureau of Labor Statistics. +854,SMU20000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Kansas,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:50:32-05,1,1,No description provided. +855,SMU20458205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Topeka, KS (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:50:24-05,1,1,No description provided. +856,SMU20458205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Topeka, KS (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:56:21-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU20458205552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -881,SMU20458205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Topeka, KS (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:25-05,1,1,No description provided. -882,SMU26476645552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:42:51-05,1,1,No description provided. -883,SMU26476645552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:45:57-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +857,SMU20458205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Topeka, KS (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:25-05,1,1,No description provided. +858,BOGZ1FA613092003Q,2022-03-21,2022-03-21,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613092003.Q + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613092003&t=) provided by the source." +859,BOGZ1FA613092003A,2022-03-21,2022-03-21,"Finance Companies; U.S. Direct Investment Abroad (Market Value); Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:02-06,1,1,"Source ID: FA613092003.A + +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). + +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). + +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613092003&t=) provided by the source." +860,SMU26476645552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:42:51-05,1,1,No description provided. +861,SMU26476645552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:45:57-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26476645552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -884,SMU26476645552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:39-05,1,1,No description provided. -885,BOGZ1FA615013075Q,2022-03-17,2022-03-17,"Finance Companies; Gross Fixed Investment, Nonresidential Intellectual Property Products, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:11-05,1,1,"Source ID: FA615013075.Q +862,SMU26476645552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:34:39-05,1,1,No description provided. +863,SMU39000005552000030SA,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2019-01-18 17:10:44-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. +864,SMU39000005552000030,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2019-01-18 17:13:38-06,1,1,No description provided. +865,SMU39000005552000030A,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2017-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2018-03-12 15:13:03-05,1,1,No description provided. +866,BOGZ1FA615013075Q,2022-03-21,2022-03-21,"Finance Companies; Gross Fixed Investment, Nonresidential Intellectual Property Products, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-17 02:06:11-05,1,1,"Source ID: FA615013075.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA615013075&t=) provided by the source." -886,SMU34459405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Trenton, NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:36:45-05,1,1,No description provided. -887,SMU34459405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Trenton, NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:39:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +867,SMU34459405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Trenton, NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:36:45-05,1,1,No description provided. +868,SMU34459405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Trenton, NJ (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:39:39-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34459405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -888,SMU34459405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Trenton, NJ (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:53-05,1,1,No description provided. -889,BOGZ1FA613030003A,2022-03-17,2022-03-17,"Finance Companies; Total Time and Savings Deposits; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613030003.A +869,SMU34459405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Trenton, NJ (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:31:53-05,1,1,No description provided. +870,BOGZ1FA613030003A,2022-03-21,2022-03-21,"Finance Companies; Total Time and Savings Deposits; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613030003.A For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613030003&t=) provided by the source." -890,BOGZ1FA613030003Q,2022-03-17,2022-03-17,"Finance Companies; Total Time and Savings Deposits; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:16-06,1,1,"Source ID: FA613030003.Q +871,BOGZ1FA613030003Q,2022-03-21,2022-03-21,"Finance Companies; Total Time and Savings Deposits; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:16-06,1,1,"Source ID: FA613030003.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613030003&t=) provided by the source." -891,SMU10000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Delaware,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:20-05,1,1,No description provided. -892,SMU10000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Delaware,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:23-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU10000005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -893,SMU10000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in Delaware,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:46-05,1,1,No description provided. -894,SMU39496605552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Youngstown-Warren-Boardman, OH-PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:45-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39496605552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -895,SMU39496605552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Youngstown-Warren-Boardman, OH-PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:32-05,1,1,No description provided. -896,SMU19197805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Des Moines-West Des Moines, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:17-05,1,1,No description provided. -897,SMU19197805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Des Moines-West Des Moines, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:55:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +872,SMU19197805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Des Moines-West Des Moines, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:17-05,1,1,No description provided. +873,SMU19197805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Des Moines-West Des Moines, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:55:44-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU19197805552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -898,SMU19197805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Des Moines-West Des Moines, IA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:43-05,1,1,No description provided. -899,BOGZ1FL613178005Q,2022-03-17,2022-03-17,"Finance Companies; Total Taxes Payable; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613178005.Q +874,SMU19197805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Des Moines-West Des Moines, IA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:43-05,1,1,No description provided. +875,SMU39496605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Youngstown-Warren-Boardman, OH-PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:45-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39496605552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +876,SMU39496605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Youngstown-Warren-Boardman, OH-PA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:28:32-05,1,1,No description provided. +877,SMU10000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Delaware,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:20-05,1,1,No description provided. +878,SMU10000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Delaware,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:23-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU10000005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +879,SMU10000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in Delaware,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:43:46-05,1,1,No description provided. +880,SMU17379005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Peoria, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:19-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17379005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +881,SMU17379005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Peoria, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:52:06-05,1,1,No description provided. +882,SMU17379005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Peoria, IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:52-05,1,1,No description provided. +883,BOGZ1FL613178005Q,2022-03-21,2022-03-21,"Finance Companies; Total Taxes Payable; Liability, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613178005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613178005&t=) provided by the source." -900,SMU17379005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Peoria, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:19-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17379005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -901,SMU17379005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Peoria, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:52:06-05,1,1,No description provided. -902,SMU17379005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Peoria, IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:52-05,1,1,No description provided. -903,SMU06311005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Santa Ana, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 13:58:16-06,1,1,No description provided. -904,SMU06311005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Santa Ana, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 13:58:16-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -905,SMU06311005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Santa Ana, CA (MSA) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-25 05:47:58-05,1,1,No description provided. -906,SMU36403805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Rochester, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:32:33-05,1,1,No description provided. -907,SMU36403805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Rochester, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:36:11-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +884,SMU06311005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Santa Ana, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 13:58:16-06,1,1,No description provided. +885,SMU06311005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Santa Ana, CA (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 13:58:16-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +886,SMU06311005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Los Angeles-Long Beach-Santa Ana, CA (MSA) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-25 05:47:58-05,1,1,No description provided. +887,SMU36403805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Rochester, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:32:33-05,1,1,No description provided. +888,SMU36403805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Rochester, NY (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:36:11-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36403805552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -908,SMU36403805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Rochester, NY (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:18-05,1,1,No description provided. -909,SMU17193405552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Davenport-Moline-Rock Island, IA-IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:52:23-05,1,1,No description provided. -910,SMU17193405552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Davenport-Moline-Rock Island, IA-IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:59:46-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +889,SMU36403805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Rochester, NY (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:18-05,1,1,No description provided. +890,SMU17193405552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Davenport-Moline-Rock Island, IA-IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:52:23-05,1,1,No description provided. +891,SMU17193405552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Davenport-Moline-Rock Island, IA-IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:59:46-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17193405552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -911,SMU17193405552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Davenport-Moline-Rock Island, IA-IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:32-05,1,1,No description provided. -912,SMU72000005552000007SA,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Puerto Rico (DISCONTINUED),2001-01-01,2013-12-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2014-02-02 10:28:21-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -913,SMU72000005552000007,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Puerto Rico (DISCONTINUED),2001-01-01,2013-12-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2014-02-02 01:49:12-06,1,1,No description provided. -914,SMU54000005552000007,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:13:19-05,1,1,No description provided. -915,SMU54000005552000007SA,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2022-03-15 04:12:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +892,SMU17193405552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Davenport-Moline-Rock Island, IA-IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:40:32-05,1,1,No description provided. +893,SMU72000005552000007SA,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Puerto Rico (DISCONTINUED),2001-01-01,2013-12-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2014-02-02 10:28:21-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +894,SMU72000005552000007,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Puerto Rico (DISCONTINUED),2001-01-01,2013-12-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2014-02-02 01:49:12-06,1,1,No description provided. +895,SMU54000005552000007,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:13:19-05,1,1,No description provided. +896,SMU54000005552000007SA,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2022-03-15 04:12:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU54000005552000007). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -916,SMU42979625552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Delaware County, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:25:14-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +897,SMU42979625552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Delaware County, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:25:14-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42979625552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" Delaware County, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -917,SMU34935635552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Bergen-Hudson-Passaic, NJ",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:38:35-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +898,SMU34935635552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bergen-Hudson-Passaic, NJ",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:38:35-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34935635552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" Bergen-Hudson-Passaic, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -918,SMU15261805552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Honolulu, HI (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:06:46-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -919,SMU42979625552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Delaware County, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:22:55-05,1,1,"Delaware County, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -920,SMU34935635552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Bergen-Hudson-Passaic, NJ",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:35:48-05,1,1,"Bergen-Hudson-Passaic, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -921,SMU15261805552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Honolulu, HI (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:06:47-06,1,1,No description provided. -922,SMU42979625552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Delaware County, PA",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:48-05,1,1,"Delaware County, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -923,SMU34935635552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Bergen-Hudson-Passaic, NJ",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:15-05,1,1,"Bergen-Hudson-Passaic, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -924,SMU15261805552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Honolulu, HI (MSA) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:55:06-05,1,1,No description provided. -925,SMU54000005552000030,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:13:19-05,1,1,No description provided. -926,SMU54000005552000030SA,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:12:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +899,SMU15261805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Honolulu, HI (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:06:46-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +900,SMU42979625552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Delaware County, PA",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:22:55-05,1,1,"Delaware County, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +901,SMU34935635552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bergen-Hudson-Passaic, NJ",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:35:48-05,1,1,"Bergen-Hudson-Passaic, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +902,SMU15261805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Honolulu, HI (MSA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:06:47-06,1,1,No description provided. +903,SMU42979625552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Delaware County, PA",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:26:48-05,1,1,"Delaware County, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +904,SMU34935635552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bergen-Hudson-Passaic, NJ",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:15-05,1,1,"Bergen-Hudson-Passaic, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +905,SMU15261805552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Honolulu, HI (MSA) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:55:06-05,1,1,No description provided. +906,SMU54000005552000030,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:13:19-05,1,1,No description provided. +907,SMU54000005552000030SA,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:12:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU54000005552000030). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -927,SMU54000005552000030A,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:21:04-05,1,1,No description provided. -928,SMU06170205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Chico, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +908,SMU54000005552000030A,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:21:04-05,1,1,No description provided. +909,SMU06170205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Chico, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:18-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06170205552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -929,SMU06170205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Chico, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:05-05,1,1,No description provided. -930,SMU06170205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Chico, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:03-05,1,1,No description provided. -931,SMU26000005552000030SA,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:48:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26000005552000030). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -932,SMU26000005552000030,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:44:11-05,1,1,No description provided. -933,SMU26000005552000030A,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:35:33-05,1,1,No description provided. -934,SMU39000005552000007SA,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2019-01-18 17:10:45-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. -935,SMU39000005552000007,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2019-01-18 17:10:45-06,1,1,No description provided. -936,SMU39000005552000007A,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2017-01-01,Annual,A,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2018-03-12 15:13:03-05,1,1,No description provided. -937,BOGZ1FR614090610Q,2022-03-17,2022-03-17,"Finance Companies; Total Assets (Balance Sheet), Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR614090610.Q +910,SMU06170205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Chico, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:05-05,1,1,No description provided. +911,SMU06170205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Chico, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:03-05,1,1,No description provided. +912,SMU39000005552000007SA,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2019-01-18 17:10:45-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. +913,SMU39000005552000007,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2018-12-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2019-01-18 17:10:45-06,1,1,No description provided. +914,SMU39000005552000007A,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Ohio,2001-01-01,2017-01-01,Annual,A,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2018-03-12 15:13:03-05,1,1,No description provided. +915,BOGZ1FR614090610Q,2022-03-21,2022-03-21,"Finance Companies; Total Assets (Balance Sheet), Revaluation",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-17 02:03:05-05,1,1,"Source ID: FR614090610.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FR614090610&t=) provided by the source." -938,DALN0A175MCFRBDAL,2022-03-17,2022-03-17,"Change in Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1990-02-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:42:00-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -939,BOGZ1FA613065403Q,2022-03-17,2022-03-17,"Finance Companies; Multifamily Residential Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613065403.Q - -For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). - -With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). - -In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065403&t=) provided by the source." -940,SMU78000005552000001SA,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in the U.S. Virgin Islands,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:07-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +916,DALN0A175MCFRBDAL,2022-03-21,2022-03-21,"Change in Finance and Insurance Payroll Employment for Dallas-Plano-Irving, TX (MSAD)",1990-02-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:42:00-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +917,SMU78000005552000001SA,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in the U.S. Virgin Islands,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:10:07-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU78000005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -941,SMU78000005552000001,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in the U.S. Virgin Islands,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:10:21-05,1,1,No description provided. -942,BOGZ1FA613065403A,2022-03-17,2022-03-17,"Finance Companies; Multifamily Residential Mortgages; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065403.A +918,SMU78000005552000001,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in the U.S. Virgin Islands,1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:10:21-05,1,1,No description provided. +919,SMU78000005552000001A,2022-03-21,2022-03-21,All Employees: Financial Activities: Finance and Insurance in the U.S. Virgin Islands,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:19:51-05,1,1,No description provided. +920,SMU34158045552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Camden, NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:36:22-05,1,1,No description provided. +921,SMU48111005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Amarillo, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:20:09-05,1,1,No description provided. +922,SMU48111005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Amarillo, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:19:15-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48111005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +923,SMU34158045552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Camden, NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:39:10-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34158045552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +924,SMU34158045552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Camden, NJ (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:03-05,1,1,No description provided. +925,SMU48111005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Amarillo, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:24:15-05,1,1,No description provided. +926,SMU33769005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Portsmouth, NH-ME (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:40:03-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU33769005552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +927,SMU33769005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Portsmouth, NH-ME (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:45-05,1,1,No description provided. +928,SMU33769005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Portsmouth, NH-ME (NECTA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:41-05,1,1,No description provided. +929,BOGZ1FA613065403Q,2022-03-21,2022-03-21,"Finance Companies; Multifamily Residential Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613065403.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065403&t=) provided by the source." -943,SMU78000005552000001A,2022-03-17,2022-03-17,All Employees: Financial Activities: Finance and Insurance in the U.S. Virgin Islands,1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:19:51-05,1,1,No description provided. -944,SMU34158045552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Camden, NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:36:22-05,1,1,No description provided. -945,SMU48111005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Amarillo, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:20:09-05,1,1,No description provided. -946,SMU48111005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Amarillo, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:19:15-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +930,BOGZ1FA613065403A,2022-03-21,2022-03-21,"Finance Companies; Multifamily Residential Mortgages; Asset, Transactions",1946-01-01,2021-01-01,Annual,A,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 12:03:03-06,1,1,"Source ID: FA613065403.A -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48111005552000001). +For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -947,SMU34158045552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Camden, NJ (MD)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:39:10-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34158045552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -948,SMU34158045552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Camden, NJ (MD)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:03-05,1,1,No description provided. -949,SMU48111005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Amarillo, TX (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:24:15-05,1,1,No description provided. -950,SMU33769005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Portsmouth, NH-ME (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:40:03-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). - -Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU33769005552000001). - -Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -951,SMU33769005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Portsmouth, NH-ME (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:37:45-05,1,1,No description provided. -952,SMU33769005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Portsmouth, NH-ME (NECTA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:32:41-05,1,1,No description provided. -953,BOGZ1FL613190005Q,2022-03-17,2022-03-17,"Finance Companies; Total Miscellaneous Liabilities, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613190005.Q +In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065403&t=) provided by the source." +931,BOGZ1FL613190005Q,2022-03-21,2022-03-21,"Finance Companies; Total Miscellaneous Liabilities, Level",1945-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Not Seasonally Adjusted,NSA,2022-03-10 11:51:51-06,1,1,"Source ID: FL613190005.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL613190005&t=) provided by the source." -954,HOLN0A175MCFRBDAL,2022-03-17,2022-03-17,"Change in Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1990-02-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:37:43-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. -955,BOGZ1FA613065503Q,2022-03-17,2022-03-17,"Finance Companies; Commercial Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613065503.Q +932,HOLN0A175MCFRBDAL,2022-03-21,2022-03-21,"Change in Finance and Insurance Payroll Employment for Houston-The Woodlands-Sugar Land, TX (MSA)",1990-02-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-11 14:37:43-06,1,1,The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm. +933,BOGZ1FA613065503Q,2022-03-21,2022-03-21,"Finance Companies; Commercial Mortgages; Asset, Transactions",1946-10-01,2021-10-01,Quarterly,Q,Millions of Dollars,Mil. of $,Seasonally Adjusted Annual Rate,SAAR,2022-03-10 11:56:39-06,1,1,"Source ID: FA613065503.Q For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx). With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx). In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FA613065503&t=) provided by the source." -956,A09025USA174NNBR,2022-03-17,2022-03-17,"Deficits, Finance Companies for United States",1917-01-01,1940-01-01,Annual,A,Thousands of Dollars,Thous. Of $,Not Seasonally Adjusted,NSA,2012-08-17 15:02:03-05,1,1,"1936-1940 Data Were Computed By NBER From Data In Statistics Of Income (See Notes). The 1938 Data Were Adjusted By NBER To Conform To 1937 Industrial Classification (See Notes). Data For 1939-1940 Are Based On 1938 Standard Industrial Classification. Source: Bureau Of Internal Revenue: Statistics Of Income, 1916 And Successive Years. +934,A09025USA174NNBR,2022-03-21,2022-03-21,"Deficits, Finance Companies for United States",1917-01-01,1940-01-01,Annual,A,Thousands of Dollars,Thous. Of $,Not Seasonally Adjusted,NSA,2012-08-17 15:02:03-05,1,1,"1936-1940 Data Were Computed By NBER From Data In Statistics Of Income (See Notes). The 1938 Data Were Adjusted By NBER To Conform To 1937 Industrial Classification (See Notes). Data For 1939-1940 Are Based On 1938 Standard Industrial Classification. Source: Bureau Of Internal Revenue: Statistics Of Income, 1916 And Successive Years. This NBER data series a09025 appears on the NBER website in Chapter 9 at http://www.nber.org/databases/macrohistory/contents/chapter09.html. NBER Indicator: a09025" -957,SMU17441005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Springfield, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:12-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +935,SMU17441005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Springfield, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:58:12-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17441005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -958,SMU17441005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Springfield, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:13-05,1,1,No description provided. -959,SMU17441005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Springfield, IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:44-05,1,1,No description provided. -960,SMU39104205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Akron, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:11-05,1,1,No description provided. -961,SMU39104205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Akron, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +936,SMU17441005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Springfield, IL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:53:13-05,1,1,No description provided. +937,SMU17441005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Springfield, IL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:39:44-05,1,1,No description provided. +938,SMU39104205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Akron, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:29:11-05,1,1,No description provided. +939,SMU39104205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Akron, OH (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:30:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU39104205552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -962,SMU19163005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cedar Rapids, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:21-05,1,1,No description provided. -963,SMU19163005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cedar Rapids, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:55:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +940,SMU19163005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cedar Rapids, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:49:21-05,1,1,No description provided. +941,SMU19163005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cedar Rapids, IA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:55:48-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU19163005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -964,SMU06467005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Vallejo-Fairfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:45-05,1,1,No description provided. -965,SMU06467005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Vallejo-Fairfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:09:23-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +942,SMU06467005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Vallejo-Fairfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:00:45-05,1,1,No description provided. +943,SMU06467005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Vallejo-Fairfield, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:09:23-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06467005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -966,SMU36935625552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Orange-Rockland-Westchester, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:38-05,1,1,"Orange-Rockland-Westchester, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -967,SMU36935625552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Orange-Rockland-Westchester, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:34:30-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +944,SMU36935625552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Orange-Rockland-Westchester, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:31:38-05,1,1,"Orange-Rockland-Westchester, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +945,SMU36935625552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Orange-Rockland-Westchester, NY",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:34:30-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36935625552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator."" Orange-Rockland-Westchester, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -968,SMU27000005552000007,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:40:59-05,1,1,No description provided. -969,SMU27000005552000007SA,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2022-03-15 04:45:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +946,SMU27000005552000007,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:40:59-05,1,1,No description provided. +947,SMU27000005552000007SA,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2022-03-15 04:45:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27000005552000007). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -970,SMU27000005552000030,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:40:58-05,1,1,No description provided. -971,SMU27000005552000030SA,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:45:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +948,SMU27000005552000030,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:40:58-05,1,1,No description provided. +949,SMU27000005552000030SA,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:45:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27000005552000030). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -972,SMU54000005552000008,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 03:13:19-05,1,1,No description provided. -973,SMU54000005552000008SA,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2022-03-15 04:12:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +950,SMU54000005552000008,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 03:13:19-05,1,1,No description provided. +951,SMU54000005552000008SA,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2022-03-15 04:12:04-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU54000005552000008). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -974,SMU26476445552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:37:16-06,1,1,No description provided. -975,SMU26476445552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:37:16-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -976,SMU36356445552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-White Plains-Wayne, NY-NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:28:11-06,1,1,No description provided. -977,SMU36356445552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-White Plains-Wayne, NY-NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:28:11-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -978,SMU36391005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Poughkeepsie-Newburgh-Middletown, NY (MSA) (DISCONTINUED)",2005-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:28:00-06,1,1,No description provided. -979,SMU36391005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Poughkeepsie-Newburgh-Middletown, NY (MSA) (DISCONTINUED)",2005-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:28:00-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. -980,SMU39104205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Akron, OH (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:27-05,1,1,No description provided. -981,SMU19163005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Cedar Rapids, IA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:45-05,1,1,No description provided. -982,SMU06467005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Vallejo-Fairfield, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:51-05,1,1,No description provided. -983,SMU36935625552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Orange-Rockland-Westchester, NY",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:41-05,1,1,"Orange-Rockland-Westchester, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." -984,SMU27000005552000007A,2022-03-17,2022-03-17,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2021-01-01,Annual,A,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. -985,SMU27000005552000030A,2022-03-17,2022-03-17,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. -986,SMU54000005552000008A,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2021-01-01,Annual,A,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 05:21:04-05,1,1,No description provided. -987,SMU26476445552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:41:59-05,1,1,No description provided. -988,SMU36356445552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in New York-White Plains-Wayne, NY-NJ (MD) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:32:55-05,1,1,No description provided. -989,SMU36391005552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Poughkeepsie-Newburgh-Middletown, NY (MSA) (DISCONTINUED)",2005-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:32:12-05,1,1,No description provided. -990,SMU06234205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Fresno, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:00-05,1,1,No description provided. -991,SMU06234205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Fresno, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:07-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +952,SMU26476445552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:37:16-06,1,1,No description provided. +953,SMU26476445552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:37:16-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +954,SMU36356445552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-White Plains-Wayne, NY-NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:28:11-06,1,1,No description provided. +955,SMU36356445552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-White Plains-Wayne, NY-NJ (MD) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:28:11-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +956,SMU36391005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Poughkeepsie-Newburgh-Middletown, NY (MSA) (DISCONTINUED)",2005-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:28:00-06,1,1,No description provided. +957,SMU36391005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Poughkeepsie-Newburgh-Middletown, NY (MSA) (DISCONTINUED)",2005-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:28:00-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +958,SMU39104205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Akron, OH (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:29:27-05,1,1,No description provided. +959,SMU19163005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Cedar Rapids, IA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:38:45-05,1,1,No description provided. +960,SMU06467005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Vallejo-Fairfield, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:44:51-05,1,1,No description provided. +961,SMU36935625552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Orange-Rockland-Westchester, NY",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:30:41-05,1,1,"Orange-Rockland-Westchester, NY constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics." +962,SMU27000005552000007A,2022-03-21,2022-03-21,Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2021-01-01,Annual,A,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. +963,SMU27000005552000030A,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2021-01-01,Annual,A,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. +964,SMU54000005552000008A,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in West Virginia,2003-01-01,2021-01-01,Annual,A,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 05:21:04-05,1,1,No description provided. +965,SMU26476445552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Warren-Troy-Farmington Hills, MI (MD) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:41:59-05,1,1,No description provided. +966,SMU36356445552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in New York-White Plains-Wayne, NY-NJ (MD) (DISCONTINUED)",1990-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:32:55-05,1,1,No description provided. +967,SMU36391005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Poughkeepsie-Newburgh-Middletown, NY (MSA) (DISCONTINUED)",2005-01-01,2013-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2014-03-24 19:32:12-05,1,1,No description provided. +968,SMU06234205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Fresno, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:03:00-05,1,1,No description provided. +969,SMU06234205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Fresno, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:15:07-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06234205552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -992,SMU27000005552000008,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 03:40:59-05,1,1,No description provided. -993,SMU27000005552000008SA,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2022-03-15 04:45:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +970,SMU27000005552000008,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 03:40:59-05,1,1,No description provided. +971,SMU27000005552000008SA,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2022-03-15 04:45:31-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27000005552000008). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -994,SMU06234205552000001A,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Fresno, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:26-05,1,1,No description provided. -995,SMU27000005552000008A,2022-03-17,2022-03-17,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2021-01-01,Annual,A,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. -996,SMU01138205552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Birmingham-Hoover, AL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:05:23-05,1,1,No description provided. -997,SMU01138205552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Birmingham-Hoover, AL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:19:21-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +972,SMU06234205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Fresno, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:46:26-05,1,1,No description provided. +973,SMU27000005552000008A,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Minnesota,2001-01-01,2021-01-01,Annual,A,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 05:34:33-05,1,1,No description provided. +974,SMU13179805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Columbus, GA-AL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:55:59-05,1,1,No description provided. +975,SMU13179805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Columbus, GA-AL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:02:30-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU13179805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +976,SMU15465205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Urban Honolulu, HI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:54:47-05,1,1,No description provided. +977,SMU15465205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Urban Honolulu, HI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:01:16-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU15465205552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +978,SMU48185805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Corpus Christi, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:19:54-05,1,1,No description provided. +979,SMU48185805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Corpus Christi, TX (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:19:49-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU48185805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +980,SMU01138205552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Birmingham-Hoover, AL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:05:23-05,1,1,No description provided. +981,SMU01138205552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Birmingham-Hoover, AL (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:19:21-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU01138205552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." -998,SMU06409005552000001,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Sacramento--Roseville--Arden-Arcade, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:00-05,1,1,No description provided. -999,SMU06409005552000001SA,2022-03-17,2022-03-17,"All Employees: Financial Activities: Finance and Insurance in Sacramento--Roseville--Arden-Arcade, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:13:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). +982,SMU37246605552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Greensboro-High Point, NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:32:09-05,1,1,No description provided. +983,SMU37246605552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Greensboro-High Point, NC (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 04:32:12-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU37246605552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +984,SMU15279805552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Kahului-Wailuku-Lahaina, HI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:55:34-05,1,1,No description provided. +985,SMU15279805552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Kahului-Wailuku-Lahaina, HI (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:01:30-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU15279805552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +986,SMU09719505552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bridgeport-Stamford-Norwalk, CT (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 03:59:44-05,1,1,No description provided. +987,SMU09719505552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Bridgeport-Stamford-Norwalk, CT (NECTA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:07:55-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU09719505552000001). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +988,SMU26000005552000008,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Not Seasonally Adjusted,NSA,2022-03-15 03:44:11-05,1,1,No description provided. +989,SMU26000005552000008SA,2022-03-21,2022-03-21,Average Hourly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2022-01-01,Monthly,M,Dollars per Hour,$ per Hour,Seasonally Adjusted,SA,2022-03-15 04:48:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26000005552000008). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +990,SMU26000005552000030,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:44:11-05,1,1,No description provided. +991,SMU26000005552000030SA,2022-03-21,2022-03-21,Average Weekly Earnings of Production Employees: Financial Activities: Finance and Insurance in Michigan,2003-01-01,2022-01-01,Monthly,M,Dollars per Week,$ per Week,Seasonally Adjusted,SA,2022-03-15 04:48:32-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU26000005552000030). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +992,SMU06409005552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Sacramento--Roseville--Arden-Arcade, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 04:02:00-05,1,1,No description provided. +993,SMU06409005552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Sacramento--Roseville--Arden-Arcade, CA (MSA)",1990-01-01,2022-01-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2022-03-15 05:13:28-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06409005552000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +994,SMU25716505552000001,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Quincy, MA-NH (NECTA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2015-01-28 14:39:56-06,1,1,No description provided. +995,SMU25716505552000001SA,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Boston-Cambridge-Quincy, MA-NH (NECTA) (DISCONTINUED)",1990-01-01,2014-12-01,Monthly,M,Thousands of Persons,Thous. of Persons,Seasonally Adjusted,SA,2015-01-28 14:39:56-06,1,1,The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. +996,SMU27334605552000007,2022-03-21,2022-03-21,"Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2001-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Not Seasonally Adjusted,NSA,2022-03-15 03:42:00-05,1,1,No description provided. +997,SMU27334605552000007SA,2022-03-21,2022-03-21,"Average Weekly Hours of Production Employees: Financial Activities: Finance and Insurance in Minneapolis-St. Paul-Bloomington, MN-WI (MSA)",2001-01-01,2022-01-01,Monthly,M,Hours per Week,Hours per Week,Seasonally Adjusted,SA,2022-03-15 04:44:29-05,1,1,"The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). + +Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU27334605552000007). + +Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." +998,SMU01138205552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Birmingham-Hoover, AL (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:48:04-05,1,1,No description provided. +999,SMU06409005552000001A,2022-03-21,2022-03-21,"All Employees: Financial Activities: Finance and Insurance in Sacramento--Roseville--Arden-Arcade, CA (MSA)",1990-01-01,2021-01-01,Annual,A,Thousands of Persons,Thous. of Persons,Not Seasonally Adjusted,NSA,2022-03-15 05:45:39-05,1,1,No description provided. diff --git a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_load_data[get_series_data-kwargs_dict0].csv b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_load_data[get_series_data-kwargs_dict0].csv index de99d109032..d9a49e642ec 100644 --- a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_load_data[get_series_data-kwargs_dict0].csv +++ b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_load_data[get_series_data-kwargs_dict0].csv @@ -574,3 +574,5 @@ 2022-03-11,2.0 2022-03-14,2.14 2022-03-15,2.15 +2022-03-16,2.19 +2022-03-17,2.2 diff --git a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_yield_curve[date0].csv b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_yield_curve[date0].csv new file mode 100644 index 00000000000..f61a2363700 --- /dev/null +++ b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_yield_curve[date0].csv @@ -0,0 +1,12 @@ +,Rate,Maturity +0,0.22,0.08333333333333333 +1,0.54,0.25 +2,0.95,0.5 +3,1.4,1.0 +4,2.14,2.0 +5,2.34,3.0 +6,2.33,5.0 +7,2.36,7.0 +8,2.32,10.0 +9,2.67,20.0 +10,2.55,30.0 diff --git a/tests/gamestonk_terminal/economy/csv/test_fred_model/test_yield_curve_none[None].csv b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_yield_curve_none[None].csv new file mode 100644 index 00000000000..f61a2363700 --- /dev/null +++ b/tests/gamestonk_terminal/economy/csv/test_fred_model/test_yield_curve_none[None].csv @@ -0,0 +1,12 @@ +,Rate,Maturity +0,0.22,0.08333333333333333 +1,0.54,0.25 +2,0.95,0.5 +3,1.4,1.0 +4,2.14,2.0 +5,2.34,3.0 +6,2.33,5.0 +7,2.36,7.0 +8,2.32,10.0 +9,2.67,20.0 +10,2.55,30.0 diff --git a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_bonds].csv b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_bonds].csv index 1e383d6a960..fd6156958c2 100644 --- a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_bonds].csv +++ b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_bonds].csv @@ -1,7 +1,7 @@ , ,Rate (%),Yld (%),Yld Chg (%) -0,U.S. 10 Year Treasury Note,1.875,2.140,-0.056 -1,Germany 10 Year Government Bond,0.000,0.373,-0.020 -2,U.K. 10 Year Gilt,4.250,1.602,-0.030 -3,Japan 10 Year Government Bond,0.100,0.203,-0.000 -4,Australia 10 Year Government Bond,1.000,2.509,0.018 -5,China 10 Year Government Bond,2.890,2.818,0.002 +0,U.S. 10 Year Treasury Note,1.875,2.187,0.034 +1,Germany 10 Year Government Bond,0.000,0.405,0.034 +2,U.K. 10 Year Gilt,4.250,1.545,0.045 +3,Japan 10 Year Government Bond,0.100,0.208,0.000 +4,Australia 10 Year Government Bond,1.000,2.575,0.003 +5,China 10 Year Government Bond,2.890,2.831,0.011 diff --git a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_currencies].csv b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_currencies].csv index be91f62f1bb..c89cd03e6bd 100644 --- a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_currencies].csv +++ b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[global_currencies].csv @@ -1,11 +1,11 @@ , ,Last,Chng,%Chng -0,Euro (EUR/USD),1.1052,0.0018,0.16 -1,Japanese Yen (USD/JPY),118.65,-0.10,-0.09 -2,U.K. Pound (GBP/USD),1.3190,0.0041,0.31 -3,Swiss Franc (USD/CHF),0.9407,-0.0001,-0.01 -4,Chinese Yuan (USD/CNY),6.3478,-0.0045,-0.07 -5,Canadian $ (USD/CAD),1.2662,-0.0017,-0.13 -6,Mexican Peso (USD/MXN),20.6145,-0.0239,-0.12 -7,Bitcoin (BTC/USD),40887,-366,-0.89 -8,WSJ Dollar Index,91.19,-0.18,-0.20 -9,U.S. Dollar Index,98.24,-0.38,-0.38 +0,Euro (EUR/USD),1.1042,-0.0008,-0.07 +1,Japanese Yen (USD/JPY),119.22,0.08,0.07 +2,U.K. Pound (GBP/USD),1.3130,-0.0047,-0.36 +3,Swiss Franc (USD/CHF),0.9315,-0.0004,-0.04 +4,Chinese Yuan (USD/CNY),6.3553,-0.0059,-0.09 +5,Canadian $ (USD/CAD),1.2612,0.0007,0.06 +6,Mexican Peso (USD/MXN),20.4113,0.0486,0.24 +7,Bitcoin (BTC/USD),41336,28,0.07 +8,WSJ Dollar Index,91.20,0.11,0.12 +9,U.S. Dollar Index,98.29,0.06,0.06 diff --git a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[market_overview].csv b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[market_overview].csv index 34850ff422e..620b4a2d191 100644 --- a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[market_overview].csv +++ b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[market_overview].csv @@ -1,10 +1,10 @@ , ,Price,Chg,%Chg -0,DJIA,34063.10,518.76,1.55 -1,S&P 500,4357.86,95.41,2.24 -2,Nasdaq Composite,13436.55,487.93,3.77 -3,Japan: Nikkei 225,26652.89,890.88,3.46 -4,UK: FTSE 100,7316.51,24.83,0.34 -5,Crude Oil Futures,98.21,3.17,3.34 -6,Gold Futures,1942.30,33.10,1.73 -7,Yen,118.65,-0.10,-0.09 -8,Euro,1.1054,0.0019,0.17 +0,DJIA,34754.93,274.17,0.80 +1,S&P 500,4463.12,51.45,1.17 +2,Nasdaq Composite,13893.84,279.06,2.05 +3,Japan: Nikkei 225,26827.43,174.54,0.65 +4,UK: FTSE 100,7450.86,46.13,0.62 +5,Crude Oil Futures,109.18,4.48,4.28 +6,Gold Futures,1926.30,-3.00,-0.16 +7,Yen,119.22,0.08,0.07 +8,Euro,1.1042,-0.0008,-0.07 diff --git a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[top_commodities].csv b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[top_commodities].csv index a4ee38af08d..2589d617923 100644 --- a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[top_commodities].csv +++ b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[top_commodities].csv @@ -1,11 +1,11 @@ , ,Price,Chg,%Chg -0,Crude Oil Futures,98.28,3.24,3.41 -1,Brent Crude Futures,101.29,3.27,3.34 -2,Gold Futures,1942.10,32.90,1.72 -3,Silver Futures,25.480,0.770,3.12 -4,Natural Gas Futures,4.755,-0.029,-0.61 -5,Unleaded Gasoline Futures,3.0623,0.0748,2.50 -6,Copper Futures,4.6610,0.0605,1.32 -7,Corn Futures,737.75,7.75,1.06 -8,Wheat Futures,1071.75,2.50,0.23 -9,Bloomberg Commodity Index,121.91,1.24,1.03 +0,Crude Oil Futures,109.18,4.48,4.28 +1,Brent Crude Futures,112.35,4.42,4.10 +2,Gold Futures,1926.20,-3.10,-0.16 +3,Silver Futures,25.185,0.098,0.39 +4,Natural Gas Futures,4.945,0.043,0.88 +5,Unleaded Gasoline Futures,3.3230,0.0842,2.60 +6,Copper Futures,4.6710,-0.0685,-1.45 +7,Corn Futures,752.25,10.50,1.42 +8,Wheat Futures,1079.25,15.50,1.46 +9,Bloomberg Commodity Index,125.48,2.12,1.72 diff --git a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_bonds].csv b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_bonds].csv index c0d0305cc3b..e301c67107d 100644 --- a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_bonds].csv +++ b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_bonds].csv @@ -1,11 +1,11 @@ , ,Rate (%),Yld (%),Yld Chg (%) -0,30-Year Bond,2.25,2.400,-0.059 -1,10-Year Note,1.875,2.137,-0.059 -2,7-Year Note,1.875,2.168,-0.060 -3,5-Year Note,1.875,2.137,-0.057 -4,3-Year Note,1.75,2.121,-0.035 -5,2-Year Note,1.5,1.930,-0.024 -6,1-Year Bill,0.0,1.215,-0.017 -7,6-Month Bill,0.0,0.778,-0.037 -8,3-Month Bill,0.0,0.393,-0.046 -9,1-Month Bill,0.0,0.182,-0.020 +0,30-Year Bond,2.25,2.440,0.012 +1,10-Year Note,1.875,2.188,0.035 +2,7-Year Note,1.875,2.220,0.036 +3,5-Year Note,1.875,2.194,0.049 +4,3-Year Note,1.75,2.183,0.030 +5,2-Year Note,1.5,1.986,0.042 +6,1-Year Bill,0.0,1.198,-0.030 +7,6-Month Bill,0.0,0.782,-0.005 +8,3-Month Bill,0.0,0.388,-0.018 +9,1-Month Bill,0.0,0.210,0.020 diff --git a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_indices].csv b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_indices].csv index 83857c0207b..163afdc3da1 100644 --- a/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_indices].csv +++ b/tests/gamestonk_terminal/economy/csv/test_wsj_model/test_call_func[us_indices].csv @@ -1,11 +1,11 @@ , ,Price,Chg,%Chg -0,DJIA,34063.10,518.76,1.55 -1,Nasdaq Composite,13436.55,487.93,3.77 -2,S&P 500,4357.86,95.41,2.24 -3,DJ Total Stock Market,44110.08,1065.28,2.47 -4,Russell 2000,2030.72,61.75,3.14 -5,NYSE Composite,16261.50,365.83,2.30 -6,Barron's 400,1011.50,26.13,2.65 -7,CBOE Volatility,26.78,0.11,0.41 -8,DJIA Futures,33957,3,0.01 -9,S&P 500 Futures,4348.25,-1.25,-0.03 +0,DJIA,34754.93,274.17,0.80 +1,Nasdaq Composite,13893.84,279.06,2.05 +2,S&P 500,4463.12,51.45,1.17 +3,DJ Total Stock Market,45264.01,558.62,1.25 +4,Russell 2000,2086.14,21.12,1.02 +5,NYSE Composite,16612.64,129.06,0.78 +6,Barron's 400,1031.79,10.22,1.00 +7,CBOE Volatility,24.54,0.67,2.81 +8,DJIA Futures,34485,-148,-0.43 +9,S&P 500 Futures,4442.00,-11.50,-0.26 diff --git a/tests/gamestonk_terminal/economy/json/test_finviz_model/test_get_futures.json b/tests/gamestonk_terminal/economy/json/test_finviz_model/test_get_futures.json index cd93586ad47..7dd2c716784 100644 --- a/tests/gamestonk_terminal/economy/json/test_finviz_model/test_get_futures.json +++ b/tests/gamestonk_terminal/economy/json/test_finviz_model/test_get_futures.json @@ -1 +1 @@ -{"Indices": [{"label": "DJIA", "ticker": "YM", "last": 33969.0, "change": 0.04, "prevClose": 33954.0, "high": 34040.0, "low": 33852.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "S&P 500", "ticker": "ES", "last": 4347.25, "change": -0.05, "prevClose": 4349.5, "high": 4363.5, "low": 4334.5, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Nasdaq 100", "ticker": "NQ", "last": 13949.5, "change": -0.0, "prevClose": 13949.75, "high": 14022.25, "low": 13900.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Russell 2000", "ticker": "ER2", "last": 2029.5, "change": 0.06, "prevClose": 2028.3, "high": 2037.0, "low": 2022.3, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Nikkei 225", "ticker": "NKD", "last": 26495.0, "change": 1.26, "prevClose": 26165.0, "high": 26565.0, "low": 26095.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Euro Stoxx 50", "ticker": "EX", "last": 3906.0, "change": 0.33, "prevClose": 3893.0, "high": 3920.0, "low": 3885.5, "sparkline": [], "sparklineDateChanges": {}}, {"label": "DAX", "ticker": "DY", "last": 14464.0, "change": 0.11, "prevClose": 14448.0, "high": 14529.0, "low": 14406.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "VIX", "ticker": "VX", "last": 27.5, "change": -0.15, "prevClose": 27.5411, "high": 28.1, "low": 27.3, "sparkline": [], "sparklineDateChanges": {}}], "Energy": [{"label": "Crude Oil WTI", "ticker": "CL", "last": 98.32, "change": 3.45, "prevClose": 95.04, "high": 98.69, "low": 94.85, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Crude Oil Brent", "ticker": "QA", "last": 101.34, "change": 3.39, "prevClose": 98.02, "high": 102.13, "low": 97.75, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Gasoline RBOB", "ticker": "RB", "last": 3.0635, "change": 2.54, "prevClose": 2.9875, "high": 3.0969, "low": 2.9687, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Heating Oil", "ticker": "HO", "last": 3.2688, "change": 5.44, "prevClose": 3.1001, "high": 3.2949, "low": 3.1312, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Natural Gas", "ticker": "NG", "last": 4.725, "change": -0.48, "prevClose": 4.748, "high": 4.771, "low": 4.691, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Ethanol", "ticker": "ZK", "last": 2.22, "change": 2.78, "prevClose": 2.16, "high": 2.22, "low": 2.16, "sparkline": [], "sparklineDateChanges": {}}], "Metals": [{"label": "Gold", "ticker": "GC", "last": 1941.9, "change": 1.71, "prevClose": 1909.2, "high": 1943.7, "low": 1924.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Silver", "ticker": "SI", "last": 25.48, "change": 3.12, "prevClose": 24.71, "high": 25.6, "low": 25.165, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Platinum", "ticker": "PL", "last": 1028.1, "change": 1.98, "prevClose": 1008.1, "high": 1030.9, "low": 1013.2, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Copper", "ticker": "HG", "last": 4.6615, "change": 1.33, "prevClose": 4.6005, "high": 4.673, "low": 4.6105, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Palladium", "ticker": "PA", "last": 2466.5, "change": 4.19, "prevClose": 2367.3, "high": 2477.0, "low": 2366.0, "sparkline": [], "sparklineDateChanges": {}}], "Meats": [{"label": "Live Cattle", "ticker": "LC", "last": 139.325, "change": -1.08, "prevClose": 140.85, "high": 141.475, "low": 139.25, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Feeder Cattle", "ticker": "FC", "last": 162.725, "change": 0.08, "prevClose": 162.6, "high": 164.775, "low": 162.45, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Lean Hogs", "ticker": "LH", "last": 102.175, "change": -0.22, "prevClose": 102.4, "high": 104.7, "low": 102.025, "sparkline": [], "sparklineDateChanges": {}}], "Grains": [{"label": "Corn", "ticker": "ZC", "last": 737.75, "change": 1.06, "prevClose": 730.0, "high": 739.75, "low": 728.5, 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1201.0, "low": 1125.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Sugar", "ticker": "SB", "last": 18.77, "change": 1.13, "prevClose": 18.56, "high": 18.82, "low": 18.56, "sparkline": [], "sparklineDateChanges": {}}], "Bonds": [{"label": "30 Year Bond", "ticker": "ZB", "last": 152.59375, "change": 0.51, "prevClose": 151.8125, "high": 152.96875, "low": 151.625, "sparkline": [], "sparklineDateChanges": {}}, {"label": "10 Year Note", "ticker": "ZN", "last": 124.5625, "change": 0.26, "prevClose": 124.234375, "high": 124.796875, "low": 124.125, "sparkline": [], "sparklineDateChanges": {}}, {"label": "5 Year Note", "ticker": "ZF", "last": 116.0859375, "change": 0.19, "prevClose": 115.8671875, "high": 116.2890625, "low": 115.84375, "sparkline": [], "sparklineDateChanges": {}}, {"label": "2 Year Note", "ticker": "ZT", "last": 106.57421875, "change": 0.07, "prevClose": 106.50390625, "high": 106.66796875, "low": 106.51171875, "sparkline": [], "sparklineDateChanges": {}}], "Currencies": [{"label": "USD", "ticker": "DX", "last": 98.23, "change": -0.4, "prevClose": 98.621, "high": 98.48, "low": 98.14, "sparkline": [], "sparklineDateChanges": {}}, {"label": "EUR", "ticker": "6E", "last": 1.10895, "change": 0.45, "prevClose": 1.104, "high": 1.11025, "low": 1.1042, "sparkline": [], "sparklineDateChanges": {}}, {"label": "JPY", "ticker": "6J", "last": 0.844, "change": -0.04, "prevClose": 0.8443499999999999, "high": 0.8448000000000001, "low": 0.8418, "sparkline": [], "sparklineDateChanges": {}}, {"label": "GBP", "ticker": "6B", "last": 1.3181, "change": 0.55, "prevClose": 1.3109, "high": 1.3184, "low": 1.3117, "sparkline": [], "sparklineDateChanges": {}}, {"label": "CAD", "ticker": "6C", "last": 0.78985, "change": 0.47, "prevClose": 0.78615, "high": 0.79025, "low": 0.7875, "sparkline": [], "sparklineDateChanges": {}}, {"label": "CHF", "ticker": "6S", "last": 1.0672, "change": 0.21, "prevClose": 1.065, "high": 1.0682, "low": 1.0639, "sparkline": [], "sparklineDateChanges": {}}, {"label": "AUD", "ticker": "6A", "last": 0.7339, "change": 0.9, "prevClose": 0.72735, "high": 0.73415, "low": 0.7291, "sparkline": [], "sparklineDateChanges": {}}, {"label": "NZD", "ticker": "6N", "last": 0.68365, "change": 0.4, "prevClose": 0.6809, "high": 0.68395, "low": 0.68125, "sparkline": [], "sparklineDateChanges": {}}]} \ No newline at end of file +{"Indices": [{"label": "DJIA", "ticker": "YM", "last": 34487.0, "change": -0.42, "prevClose": 34633.0, "high": 34688.0, "low": 34464.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "S&P 500", "ticker": "ES", "last": 4442.5, "change": -0.25, "prevClose": 4453.5, "high": 4463.25, "low": 4430.75, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Nasdaq 100", "ticker": "NQ", "last": 14356.0, "change": -0.4, "prevClose": 14413.5, "high": 14435.0, "low": 14307.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Russell 2000", "ticker": "ER2", "last": 2071.5, "change": -0.42, "prevClose": 2080.2, "high": 2086.3, "low": 2068.1, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Nikkei 225", "ticker": "NKD", "last": 27045.0, "change": -0.15, "prevClose": 27085.0, "high": 27155.0, "low": 26965.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Euro Stoxx 50", "ticker": "EX", "last": 3815.0, "change": 0.24, "prevClose": 3806.0, "high": 3831.0, "low": 3781.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "DAX", "ticker": "DY", "last": 14425.0, "change": 0.34, "prevClose": 14376.0, "high": 14467.0, "low": 14290.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "VIX", "ticker": "VX", "last": 26.05, "change": 1.6, "prevClose": 25.6406, "high": 26.25, "low": 25.65, "sparkline": [], "sparklineDateChanges": {}}], "Energy": [{"label": "Crude Oil WTI", "ticker": "CL", "last": 107.67, "change": 4.44, "prevClose": 103.09, "high": 108.19, "low": 102.47, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Crude Oil Brent", "ticker": "QA", "last": 112.51, "change": 4.24, "prevClose": 107.93, "high": 112.91, "low": 107.06, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Gasoline RBOB", "ticker": "RB", "last": 3.3195, "change": 2.49, "prevClose": 3.2388, "high": 3.3315, "low": 3.2222, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Heating Oil", "ticker": "HO", "last": 3.7743, "change": 4.9, "prevClose": 3.5981, "high": 3.8153, "low": 3.592, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Natural Gas", "ticker": "NG", "last": 4.903, "change": 0.82, "prevClose": 4.863, "high": 4.971, "low": 4.884, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Ethanol", "ticker": "ZK", "last": 2.22, "change": 2.78, "prevClose": 2.16, "high": 2.22, "low": 2.16, "sparkline": [], "sparklineDateChanges": {}}], "Metals": [{"label": "Gold", "ticker": "GC", "last": 1926.4, "change": -0.15, "prevClose": 1929.3, "high": 1929.5, "low": 1918.4, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Silver", "ticker": "SI", "last": 25.185, "change": 0.39, "prevClose": 25.087, "high": 25.295, "low": 25.05, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Platinum", "ticker": "PL", "last": 1038.2, "change": 0.22, "prevClose": 1035.9, "high": 1043.0, "low": 1028.2, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Copper", "ticker": "HG", "last": 4.6705, "change": -1.46, "prevClose": 4.7395, "high": 4.732, "low": 4.6595, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Palladium", "ticker": "PA", "last": 2540.5, "change": 1.91, "prevClose": 2493.0, "high": 2608.0, "low": 2475.0, "sparkline": [], "sparklineDateChanges": {}}], "Meats": [{"label": "Live Cattle", "ticker": "LC", "last": 137.15, "change": -1.67, "prevClose": 139.475, "high": 137.4, "low": 135.6, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Feeder Cattle", "ticker": "FC", "last": 168.0, "change": 4.28, "prevClose": 161.1, "high": 168.2, "low": 165.95, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Lean Hogs", "ticker": "LH", "last": 99.5, "change": -0.85, "prevClose": 100.35, "high": 101.625, "low": 99.225, "sparkline": [], "sparklineDateChanges": {}}], "Grains": [{"label": "Corn", "ticker": "ZC", "last": 752.5, "change": 1.45, "prevClose": 741.75, "high": 753.25, "low": 743.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Soybean oil", "ticker": "ZL", "last": 73.91, "change": 2.24, "prevClose": 72.29, "high": 74.1, "low": 72.58, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Soybean Meal", "ticker": "ZM", "last": 480.8, "change": 0.8, "prevClose": 477.0, "high": 481.6, "low": 475.8, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Oats", "ticker": "ZO", "last": 705.0, "change": 1.44, "prevClose": 695.0, "high": 705.0, "low": 693.5, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Rough Rice", "ticker": "ZR", "last": 15.73, "change": 0.67, "prevClose": 15.625, "high": 15.745, "low": 15.5, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Soybeans", "ticker": "ZS", "last": 1685.25, "change": 1.03, "prevClose": 1668.0, "high": 1690.75, "low": 1666.75, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Wheat", "ticker": "ZW", "last": 1079.25, "change": 1.46, "prevClose": 1063.75, "high": 1083.5, "low": 1061.75, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Canola", "ticker": "RS", "last": 1133.9, "change": 0.81, "prevClose": 1124.8, "high": 1133.9, "low": 1120.0, "sparkline": [], "sparklineDateChanges": {}}], "Softs": [{"label": "Cocoa", "ticker": "CC", "last": 2504.0, "change": -1.3, "prevClose": 2537.0, "high": 2526.0, "low": 2498.0, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Cotton", "ticker": "CT", "last": 128.88, "change": 1.59, "prevClose": 126.86, "high": 130.15, "low": 127.1, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Orange Juice", "ticker": "JO", "last": 147.3, "change": 1.87, "prevClose": 144.6, "high": 147.9, "low": 144.05, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Coffee", "ticker": "KC", "last": 221.9, "change": 0.84, "prevClose": 220.05, "high": 222.4, "low": 219.75, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Lumber", "ticker": "LB", "last": 1199.6, "change": -0.61, "prevClose": 1207.0, "high": 1246.0, "low": 1173.5, "sparkline": [], "sparklineDateChanges": {}}, {"label": "Sugar", "ticker": "SB", "last": 19.13, "change": 1.06, "prevClose": 18.93, "high": 19.2, "low": 18.98, "sparkline": [], "sparklineDateChanges": {}}], "Bonds": [{"label": "30 Year Bond", "ticker": "ZB", "last": 151.9375, "change": -0.35, "prevClose": 152.46875, "high": 152.34375, "low": 151.6875, "sparkline": [], "sparklineDateChanges": {}}, {"label": "10 Year Note", "ticker": "ZN", "last": 124.3125, "change": -0.26, "prevClose": 124.640625, "high": 124.5625, "low": 124.1875, "sparkline": [], "sparklineDateChanges": {}}, {"label": "5 Year Note", "ticker": "ZF", "last": 115.90625, "change": -0.17, "prevClose": 116.109375, "high": 116.0859375, "low": 115.8359375, "sparkline": [], "sparklineDateChanges": {}}, {"label": "2 Year Note", "ticker": "ZT", "last": 106.46875, "change": -0.04, "prevClose": 106.515625, "high": 106.53515625, "low": 106.44140625, "sparkline": [], "sparklineDateChanges": {}}], "Currencies": [{"label": "USD", "ticker": "DX", "last": 98.275, "change": 0.05, "prevClose": 98.227, "high": 98.38, "low": 98.16, "sparkline": [], "sparklineDateChanges": {}}, {"label": "EUR", "ticker": "6E", "last": 1.10815, "change": -0.02, "prevClose": 1.10835, "high": 1.11045, "low": 1.10685, "sparkline": [], "sparklineDateChanges": {}}, {"label": "JPY", "ticker": "6J", "last": 0.8404999999999999, "change": -0.05, "prevClose": 0.8409500000000001, "high": 0.8411500000000001, "low": 0.83975, "sparkline": [], "sparklineDateChanges": {}}, {"label": "GBP", "ticker": "6B", "last": 1.3136, "change": -0.35, "prevClose": 1.3182, "high": 1.3172, "low": 1.3129, "sparkline": [], "sparklineDateChanges": {}}, {"label": "CAD", "ticker": "6C", "last": 0.7928, "change": -0.1, "prevClose": 0.7936, "high": 0.7943, "low": 0.79245, "sparkline": [], "sparklineDateChanges": {}}, {"label": "CHF", "ticker": "6S", "last": 1.0787, "change": 0.18, "prevClose": 1.0768, "high": 1.0794, "low": 1.0729, "sparkline": [], "sparklineDateChanges": {}}, {"label": "AUD", "ticker": "6A", "last": 0.739, "change": -0.36, "prevClose": 0.7417, "high": 0.74335, "low": 0.7385, "sparkline": [], "sparklineDateChanges": {}}, {"label": "NZD", "ticker": "6N", "last": 0.68785, "change": -0.25, "prevClose": 0.6896, "high": 0.69125, "low": 0.68765, "sparkline": [], "sparklineDateChanges": {}}]} \ No newline at end of file diff --git a/tests/gamestonk_terminal/economy/json/test_fred_model/test_output[check_series_id-kwargs_dict0].json b/tests/gamestonk_terminal/economy/json/test_fred_model/test_output[check_series_id-kwargs_dict0].json index 76545362659..5cfa88f62ae 100644 --- a/tests/gamestonk_terminal/economy/json/test_fred_model/test_output[check_series_id-kwargs_dict0].json +++ b/tests/gamestonk_terminal/economy/json/test_fred_model/test_output[check_series_id-kwargs_dict0].json @@ -1 +1 @@ -[{"id": "DGS5", "realtime_start": "2022-03-17", "realtime_end": "2022-03-17", "title": "Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity", "observation_start": "1962-01-02", "observation_end": "2022-03-15", "frequency": "Daily", "frequency_short": "D", "units": "Percent", "units_short": "%", "seasonal_adjustment": "Not Seasonally Adjusted", "seasonal_adjustment_short": "NSA", "last_updated": "2022-03-16 15:19:10-05", "popularity": 77, "notes": "For further information regarding treasury constant maturity data, please refer to the Board of Governors (http://www.federalreserve.gov/releases/h15/current/h15.pdf) and the Treasury (http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx)."}] \ No newline at end of file +[{"id": "DGS5", "realtime_start": "2022-03-21", "realtime_end": "2022-03-21", "title": "Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity", "observation_start": "1962-01-02", "observation_end": "2022-03-17", "frequency": "Daily", "frequency_short": "D", "units": "Percent", "units_short": "%", "seasonal_adjustment": "Not Seasonally Adjusted", "seasonal_adjustment_short": "NSA", "last_updated": "2022-03-18 15:24:43-05", "popularity": 76, "notes": "For further information regarding treasury constant maturity data, please refer to the Board of Governors (http://www.federalreserve.gov/releases/h15/current/h15.pdf) and the Treasury (http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx)."}] \ No newline at end of file diff --git a/tests/gamestonk_terminal/economy/test_econdb_model.py b/tests/gamestonk_terminal/economy/test_econdb_model.py index 54f492a3899..0509ffcce6c 100644 --- a/tests/gamestonk_terminal/economy/test_econdb_model.py +++ b/tests/gamestonk_terminal/economy/test_econdb_model.py @@ -16,11 +16,12 @@ from gamestonk_terminal.economy import econdb_model ], ) def test_get_macro_data(parameter, country, start_date, end_date, convert_currency): - result_df = econdb_model.get_macro_data( + result_df, units = econdb_model.get_macro_data( parameter, country, start_date, end_date, convert_currency ) assert isinstance(result_df, pd.Series) + assert isinstance(units, str) assert not result_df.empty @@ -36,11 +37,12 @@ def test_get_macro_data(parameter, country, start_date, end_date, convert_curren def test_get_aggregated_macro_data( parameters, countries, start_date, end_date, convert_currency ): - result_df = econdb_model.get_aggregated_macro_data( + result_df, units = econdb_model.get_aggregated_macro_data( parameters, countries, start_date, end_date, convert_currency ) assert isinstance(result_df, pd.DataFrame) + assert isinstance(units, dict) assert not result_df.empty diff --git a/tests/gamestonk_terminal/economy/test_econdb_view.py b/tests/gamestonk_terminal/economy/test_econdb_view.py index 17b07c54acf..7938acbec13 100644 --- a/tests/gamestonk_terminal/economy/test_econdb_view.py +++ b/tests/gamestonk_terminal/economy/test_econdb_view.py @@ -11,15 +11,15 @@ from gamestonk_terminal.economy import econdb_view, econdb_model [ [["RGDP"], ["United States", "Germany"], "2020-01-01", "2020-10-02", False], [["EMP", "PPI"], ["France"], "2010-01-01", "2019-01-01", False], - [["GDP, RGDP"], ["Italy", "Netherlands"], "2016-01-01", "2016-10-10", False], + [["GDP", "RGDP"], ["Italy", "Netherlands"], "2016-01-01", "2016-10-10", False], ], ) def test_show_macro_data( - parameters, mocker, countries, start_date, end_date, convert_currency + mocker, parameters, countries, start_date, end_date, convert_currency ): - mocker.patch(target="gamestonk_terminal.economy.econdb_view.theme.visualize_output") - # MOCK EXPORT_DATA - mocker.patch(target="gamestonk_terminal.economy.econdb_view.export_data") + mocker.patch( + target="gamestonk_terminal.helper_classes.TerminalStyle.visualize_output" + ) econdb_view.show_macro_data( parameters, countries, start_date, end_date, convert_currency ) @@ -36,11 +36,11 @@ def test_show_macro_data( ], ) def test_show_treasuries( - instruments, mocker, maturities, frequency, start_date, end_date + mocker, instruments, maturities, frequency, start_date, end_date ): - mocker.patch(target="gamestonk_terminal.economy.econdb_view.theme.visualize_output") - # MOCK EXPORT_DATA - mocker.patch(target="gamestonk_terminal.economy.econdb_view.export_data") + mocker.patch( + target="gamestonk_terminal.helper_classes.TerminalStyle.visualize_output" + ) econdb_view.show_treasuries( instruments, maturities, frequency, start_date, end_date ) diff --git a/tests/gamestonk_terminal/economy/test_fred_model.py b/tests/gamestonk_terminal/economy/test_fred_model.py index 50bc5e9b4ca..8a11fdbfeb9 100644 --- a/tests/gamestonk_terminal/economy/test_fred_model.py +++ b/tests/gamestonk_terminal/economy/test_fred_model.py @@ -1,4 +1,5 @@ # IMPORTATION STANDARD +import datetime # IMPORTATION THIRDPARTY import pytest @@ -79,3 +80,32 @@ def test_load_data(func, kwargs_dict, recorder): assert not result_df.empty recorder.capture(result_df) + + +@pytest.mark.skip(reason="Date needs to be mocked.") +@pytest.mark.vcr +@pytest.mark.parametrize("date", [datetime.datetime(2022, 3, 21, 0, 0)]) +def test_yield_curve(date, recorder): + result_df, returned_date = fred_model.get_yield_curve(date) + assert date.strftime("%Y-%m-%d") == returned_date.strftime("%Y-%m-%d") + assert not result_df.empty + recorder.capture(result_df) + + +@pytest.mark.skip(reason="Date needs to be mocked.") +@pytest.mark.vcr +@pytest.mark.parametrize("date", [datetime.datetime(2021, 7, 17, 0, 0)]) +def test_yield_curve_weekend(date): + result_df, returned_date = fred_model.get_yield_curve(date) + assert date.strftime("%Y-%m-%d") == returned_date + assert result_df.empty + + +@pytest.mark.skip(reason="Date needs to be mocked.") +@pytest.mark.vcr +@pytest.mark.parametrize("date", [None]) +def test_yield_curve_none(date, recorder): + result_df, returned_date = fred_model.get_yield_curve(date) + assert returned_date.strftime("%Y-%m-%d") == "2022-03-21" + assert not result_df.empty + recorder.capture(result_df) diff --git a/tests/gamestonk_terminal/economy/test_yfinance_view.py b/tests/gamestonk_terminal/economy/test_yfinance_view.py index 3069c7c6edf..3f92f4417a9 100644 --- a/tests/gamestonk_terminal/economy/test_yfinance_view.py +++ b/tests/gamestonk_terminal/economy/test_yfinance_view.py @@ -11,9 +11,17 @@ from gamestonk_terminal.economy import yfinance_view @pytest.mark.vcr @pytest.mark.parametrize( - "indices, interval, start_date, end_date, column, store", + "indices, interval, start_date, end_date, column, returns, store", [ - [["sp500", "nasdaq"], "1d", "2022-01-01", "2022-02-02", "Adj Close", False], + [ + ["sp500", "nasdaq"], + "1d", + "2022-01-01", + "2022-02-02", + "Adj Close", + False, + False, + ], [ ["cac40", "dowjones", "nasdaq"], "1wk", @@ -21,12 +29,15 @@ from gamestonk_terminal.economy import yfinance_view "2020-07-07", "Close", False, + False, ], - [["dowjones"], "1mo", "2015-01-01", "2015-02-02", "Volume", False], - [["cac40"], "3mo", "2010-01-01", "2016-02-06", "High", True], + [["dowjones"], "1mo", "2015-01-01", "2015-02-02", "Volume", False, False], + [["cac40"], "3mo", "2010-01-01", "2016-02-06", "High", False, True], ], ) -def test_show_indices(mocker, indices, interval, start_date, end_date, column, store): +def test_show_indices( + mocker, indices, interval, start_date, end_date, column, returns, store +): yf_download = yfinance.download def mock_yf_download(*args, **kwargs): @@ -38,7 +49,43 @@ def test_show_indices(mocker, indices, interval, start_date, end_date, column, s target="gamestonk_terminal.helper_classes.TerminalStyle.visualize_output" ) result_df = yfinance_view.show_indices( - indices, interval, start_date, end_date, column, store + indices, interval, start_date, end_date, column, returns, store + ) + + assert isinstance(result_df, pd.DataFrame) + + +@pytest.mark.skip +@pytest.mark.vcr +@pytest.mark.parametrize( + "indices, interval, start_date, end_date, column, returns, store", + [ + [ + ["sp500", "nasdaq", "dowjones"], + "1m", + "2022-03-18", + "2022-03-21", + "Adj Close", + True, + False, + ], + ], +) +def test_show_indices_returns( + mocker, indices, interval, start_date, end_date, column, returns, store +): + yf_download = yfinance.download + + def mock_yf_download(*args, **kwargs): + kwargs["threads"] = False + return yf_download(*args, **kwargs) + + mocker.patch("yfinance.download", side_effect=mock_yf_download) + mocker.patch( + target="gamestonk_terminal.helper_classes.TerminalStyle.visualize_output" + ) + result_df = yfinance_view.show_indices( + indices, interval, start_date, end_date, column, returns, store ) assert isinstance(result_df, pd.DataFrame) diff --git a/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True0].txt b/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True0].txt index 3decfcbf74f..928c1dfc768 100644 --- a/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True0].txt +++ b/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True0].txt @@ -1,12 +1,12 @@ Rank A: Real-Time Performance -Consumer Discretionary 3.35 -Information Technology 3.32 -Communication Services 2.93 -Financials 2.88 -Materials 1.58 -Industrials 1.32 -Health Care 1.22 -Real Estate 1.13 -Consumer Staples 0.13 -Utilities -0.17 -Energy -0.43 +Information Technology 2.19 +Consumer Discretionary 2.18 +Communication Services 1.44 +Health Care 0.65 +Materials 0.60 +Industrials 0.40 +Financials 0.29 +Consumer Staples 0.27 +Real Estate 0.21 +Energy 0.09 +Utilities -0.90 diff --git a/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True1].txt b/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True1].txt index 3decfcbf74f..928c1dfc768 100644 --- a/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True1].txt +++ b/tests/gamestonk_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True1].txt @@ -1,12 +1,12 @@ Rank A: Real-Time Performance -Consumer Discretionary 3.35 -Information Technology 3.32 -Communication Services 2.93 -Financials 2.88 -Materials 1.58 -Industrials 1.32 -Health Care 1.22 -Real Estate 1.13 -Consumer Staples 0.13 -Utilities -0.17 -Energy -0.43 +Information Technology 2.19 +Consumer Discretionary 2.18 +Communication Services 1.44 +Health Care 0.65 +Materials 0.60 +Industrials 0.40 +Financials 0.29 +Consumer Staples 0.27 +Real Estate 0.21 +Energy 0.09 +Utilities -0.90 diff --git a/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[None].txt b/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[None].txt index 62f4b965dfc..46055606247 100644 --- a/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[None].txt +++ b/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[None].txt @@ -1,31 +1,31 @@ -Fear & Greed Now: 22 (Extreme Fear) - Previous Close: 19 (Extreme Fear) - 1 Week Ago: 18 (Extreme Fear) - 1 Month Ago: 20 (Extreme Fear) - 1 Year Ago: 56 (Greed) +Fear & Greed Now: 38 (Fear) + Previous Close: 26 (Fear) + 1 Week Ago: 15 (Extreme Fear) + 1 Month Ago: 30 (Fear) + 1 Year Ago: 55 (Neutral) -Junk Bond Demand: Neutral [Updated Mar 15 at 8:00pm] - The CBOE Volatility Index (VIX) is at 26.67. This is a neutral reading and indicates that market risks appear low. +Junk Bond Demand: Extreme Greed [Updated Mar 18 at 4:10pm] + Stocks have outperformed bonds by 4.58 percentage points during the last 20 trading days. This is close to the strongest performance for stocks relative to bonds in the past two years and indicates investors are rotating into stocks from the relative safety of bonds. + (Last changed Mar 17 from a Neutral rating) + +Market Volatility: Neutral [Updated Mar 17 at 8:00pm] + The CBOE Volatility Index (VIX) is at 23.87. This is a neutral reading and indicates that market risks appear low. (Last changed Mar 14 from an Extreme Fear rating) -Market Volatility: Fear [Updated Mar 15 at 8:00pm] - Bonds have outperformed stocks by 1.45 percentage points during the last 20 trading days. This is close to the weakest performance for stocks relative to bonds in the past two years and indicates investors are fleeing risky stocks for the safety of bonds. - (Last changed Mar 14 from an Extreme Fear rating) - -Put and Call Options: Fear [Updated Mar 15 at 8:00pm] - Investors in low quality junk bonds are accepting 1.77 percentage points in additional yield over safer investment grade corporate bonds. This spread is higher than what has been typical for the last two years and indicates that investors are risk averse. +Put and Call Options: Fear [Updated Mar 17 at 8:00pm] + Investors in low quality junk bonds are accepting 1.75 percentage points in additional yield over safer investment grade corporate bonds. This spread is higher than what has been typical for the last two years and indicates that investors are risk averse. (Last changed Mar 15 from an Extreme Fear rating) -Market Momentum: Extreme Fear [Updated Mar 16 at 4:00pm] +Market Momentum: Extreme Fear [Updated Mar 18 at 7:00pm] + The McClellan Volume Summation Index measures advancing and declining volume on the NYSE. During the last month, approximately 9.30% more of each day's volume has traded in advancing issues than in declining issues. This indicates that market breadth is improving, though the McClellan Oscillator is still towards the lower end of its range for the last two years. + (Last changed Feb 22 from a Fear rating) + +Stock Price Strength: Extreme Fear [Updated Mar 18 at 7:00pm] The number of stocks hitting 52-week lows exceeds the number hitting highs and is at the lower end of its range, indicating extreme fear. (Last changed Jan 20 from a Fear rating) -Stock Price Strength: Extreme Fear [Updated Mar 16 at 4:05pm] - The McClellan Volume Summation Index measures advancing and declining volume on the NYSE. During the last month, approximately 2.15% more of each day's volume has traded in advancing issues than in declining issues. This indicates that market breadth is improving, though the McClellan Oscillator is still towards the lower end of its range for the last two years. - (Last changed Feb 22 from a Fear rating) - -Stock Price Breadth: Extreme Fear [Updated Mar 16 at 5:12pm] - The S&P 500 is 3.67% below its 125-day average. During the last two years, the S&P 500 has typically been above this average, so rapid declines like this indicate extreme levels of fear. +Stock Price Breadth: Extreme Fear [Updated Mar 18 at 5:07pm] + The S&P 500 is 1.36% below its 125-day average. During the last two years, the S&P 500 has typically been above this average, so rapid declines like this indicate extreme levels of fear. (Last changed Jan 14 from a Fear rating) Safe Heaven Demand: N/A [N/A] diff --git a/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[index].txt b/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[index].txt index 419e52f8b60..02662deecb6 100644 --- a/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[index].txt +++ b/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[index].txt @@ -1,5 +1,5 @@ -Fear & Greed Now: 22 (Extreme Fear) - Previous Close: 19 (Extreme Fear) - 1 Week Ago: 18 (Extreme Fear) - 1 Month Ago: 20 (Extreme Fear) - 1 Year Ago: 56 (Greed) \ No newline at end of file +Fear & Greed Now: 38 (Fear) + Previous Close: 26 (Fear) + 1 Week Ago: 15 (Extreme Fear) + 1 Month Ago: 30 (Fear) + 1 Year Ago: 55 (Neutral) \ No newline at end of file diff --git a/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[sps].txt b/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[sps].txt index 2e57c5fbf1e..d3ab0ab0c44 100644 --- a/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[sps].txt +++ b/tests/gamestonk_terminal/economy/txt/test_cnn_model/test_get_feargreed_report[sps].txt @@ -1,3 +1,3 @@ -Stock Price Strength: Extreme Fear [Updated Mar 16 at 4:05pm] - The McClellan Volume Summation Index measures advancing and declining volume on the NYSE. During the last month, approximately 2.15% more of each day's volume has traded in advancing issues than in declining issues. This indicates that market breadth is improving, though the McClellan Oscillator is still towards the lower end of its range for the last two years. - (Last changed Feb 22 from a Fear rating) +Stock Price Strength: Extreme Fear [Updated Mar 18 at 7:00pm] + The number of stocks hitting 52-week lows exceeds the number hitting highs and is at the lower end of its range, indicating extreme fear. + (Last changed Jan 20 from a Fear rating) diff --git a/tests/gamestonk_terminal/economy/txt/test_cnn_view/test_get_feargreed_report.txt b/tests/gamestonk_terminal/economy/txt/test_cnn_view/test_get_feargreed_report.txt index 3d912a65bd5..0a0499f101b 100644 --- a/tests/gamestonk_terminal/economy/txt/test_cnn_view/test_get_feargreed_report.txt +++ b/tests/gamestonk_terminal/economy/txt/test_cnn_view/test_get_feargreed_report.txt @@ -1,4 +1,4 @@ -Stock Price Strength: Extreme Fear [Updated Mar 16 at 4:05pm] - The McClellan Volume Summation Index measures advancing and declining volume on the NYSE. During the last month, approximately 2.15% more of each day's volume has traded in advancing issues than in declining issues. This indicates that market breadth is improving, though the McClellan Oscillator is still towards the lower end of its range for the last two years. - (Last changed Feb 22 from a Fear rating) +Stock Price Strength: Extreme Fear [Updated Mar 18 at 7:00pm] + The number of stocks hitting 52-week lows exceeds the number hitting highs and is at the lower end of its range, indicating extreme fear. + (Last changed Jan 20 from a Fear rating) diff --git a/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].txt b/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].txt index b01b614bd70..17219394172 100644 --- a/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].txt +++ b/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments0-maturities0-monthly-2020-01-01-2020-02-03].txt @@ -1 +1,2 @@ The maturity 3y is not an option for inflation. Please choose between 5y, 7y, 10y, 20y, 30y + diff --git a/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].txt b/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].txt index 431ccb5ce0b..1e19320c855 100644 --- a/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].txt +++ b/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments1-maturities1-annually-2015-01-04-2015-01-28].txt @@ -1,2 +1,3 @@ No data found for the combination nominal and 1m. No data found for the combination nominal and 30y. + diff --git a/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].txt b/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].txt index b01b614bd70..17219394172 100644 --- a/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].txt +++ b/tests/gamestonk_terminal/economy/txt/test_econdb_view/test_show_treasuries[instruments2-maturities2-weekly-2018-06-05-2018-07-06].txt @@ -1 +1,2 @@ The maturity 3y is not an option for inflation. Please choose between 5y, 7y, 10y, 20y, 30y + diff --git a/tests/gamestonk_terminal/economy/txt/test_economy_controller/test_print_help.txt b/tests/gamestonk_terminal/economy/txt/test_economy_controller/test_print_help.txt index c5593771966..cbe58f75398 100644 --- a/tests/gamestonk_terminal/economy/txt/test_economy_controller/test_print_help.txt +++ b/tests/gamestonk_terminal/economy/txt/test_economy_controller/test_print_help.txt @@ -11,6 +11,7 @@ Macro Data fred collect macro data from FRED based on a series ID [Source: FRED] index find and plot any (major) index on the market [Source: Yahoo Finance] treasury obtain U.S. treasury rates [Source: EconDB] + yield show the U.S. Treasury yield curve [Source: FRED] plot plot data from the above commands together options show the available options for 'plot' or show/export the data diff --git a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[False].txt b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[False].txt index a2ee28829ab..768fdf3cfce 100644 --- a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[False].txt @@ -1,11 +1,11 @@ - prevClose last change -label -DJIA 33954.0000 33969.0 0.04 -VIX 27.5411 27.5 -0.15 -Nasdaq 100 13949.7500 13949.5 -0.00 -Nikkei 225 26165.0000 26495.0 1.26 -Euro Stoxx 50 3893.0000 3906.0 0.33 -S&P 500 4349.5000 4349.5 0.00 -Russell 2000 2028.3000 2029.5 0.06 -DAX 14448.0000 14464.0 0.11 + prevClose last change +label +DJIA 34633.0000 34487.00 -0.42 +VIX 25.6406 26.05 1.60 +Nasdaq 100 14413.5000 14356.00 -0.40 +Nikkei 225 27085.0000 27045.00 -0.15 +Euro Stoxx 50 3806.0000 3815.00 0.24 +S&P 500 4453.5000 4442.50 -0.25 +Russell 2000 2080.2000 2071.50 -0.42 +DAX 14376.0000 14425.00 0.34 diff --git a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[True].txt b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[True].txt index a2ee28829ab..768fdf3cfce 100644 --- a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_future[True].txt @@ -1,11 +1,11 @@ - prevClose last change -label -DJIA 33954.0000 33969.0 0.04 -VIX 27.5411 27.5 -0.15 -Nasdaq 100 13949.7500 13949.5 -0.00 -Nikkei 225 26165.0000 26495.0 1.26 -Euro Stoxx 50 3893.0000 3906.0 0.33 -S&P 500 4349.5000 4349.5 0.00 -Russell 2000 2028.3000 2029.5 0.06 -DAX 14448.0000 14464.0 0.11 + prevClose last change +label +DJIA 34633.0000 34487.00 -0.42 +VIX 25.6406 26.05 1.60 +Nasdaq 100 14413.5000 14356.00 -0.40 +Nikkei 225 27085.0000 27045.00 -0.15 +Euro Stoxx 50 3806.0000 3815.00 0.24 +S&P 500 4453.5000 4442.50 -0.25 +Russell 2000 2080.2000 2071.50 -0.42 +DAX 14376.0000 14425.00 0.34 diff --git a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[False].txt b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[False].txt index 32c62890f33..148bb294ce0 100644 --- a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[False].txt @@ -1,13 +1,13 @@ Name Week Month 3Month 6Month 1Year YTD Recom AvgVolume (1M) RelVolume Change Volume (1M) -0 Basic Materials -0.0052 0.0249 0.0546 0.0385 0.0534 0.0097 2.21 558.28 1.39 0.0190 776.75 -1 Communication Services 0.0071 -0.0375 -0.1241 -0.1875 -0.0663 -0.1365 1.88 760.94 1.39 0.0374 1060.00 -2 Consumer Cyclical 0.0367 -0.0600 -0.1375 -0.1334 -0.1426 -0.1547 2.05 1330.00 1.50 0.0616 1990.00 -3 Consumer Defensive 0.0093 -0.0211 -0.0245 -0.0049 0.0338 -0.0531 2.23 452.89 1.41 0.0078 640.39 -4 Energy -0.0435 -0.0025 0.2170 0.2659 0.2949 0.1941 2.23 882.68 1.03 0.0033 908.76 -5 Financial 0.0356 -0.0341 -0.0057 -0.0089 0.0466 -0.0224 2.23 1050.00 1.24 0.0300 1300.00 -6 Healthcare 0.0211 0.0209 -0.0432 -0.0825 0.0072 -0.0822 2.06 1430.00 1.13 0.0196 1620.00 -7 Industrials 0.0240 0.0176 -0.0294 -0.0155 0.0137 -0.0579 2.26 670.52 1.24 0.0202 832.93 -8 Real Estate 0.0048 0.0141 -0.0546 -0.0318 0.0660 -0.1041 2.22 355.72 1.64 0.0152 585.12 -9 Technology 0.0079 -0.0403 -0.1344 -0.1126 0.0335 -0.1601 1.98 1640.00 1.39 0.0418 2280.00 -10 Utilities 0.0083 0.0642 0.0270 0.0431 0.1125 -0.0111 2.32 160.03 1.13 0.0024 181.12 +0 Basic Materials 0.0303 0.0337 0.0721 0.1212 0.1058 0.0419 2.21 564.39 1.70 0.0070 961.36 +1 Communication Services 0.0637 -0.0258 -0.1031 -0.1558 -0.0248 -0.1145 1.88 764.96 1.78 0.0169 1360.00 +2 Consumer Cyclical 0.1093 -0.0505 -0.0915 -0.1022 -0.0962 -0.1226 2.06 1340.00 1.78 0.0256 2390.00 +3 Consumer Defensive 0.0475 -0.0126 -0.0218 0.0161 0.0506 -0.0392 2.23 455.72 1.71 0.0063 779.27 +4 Energy -0.0182 0.0352 0.2454 0.3398 0.3865 0.2319 2.23 887.41 1.38 -0.0003 1230.00 +5 Financial 0.0690 -0.0310 -0.0012 0.0172 0.0777 -0.0049 2.23 1050.00 1.67 0.0059 1750.00 +6 Healthcare 0.0669 0.0299 -0.0388 -0.0592 0.0399 -0.0565 2.05 1420.00 1.66 0.0086 2360.00 +7 Industrials 0.0541 0.0143 -0.0165 0.0164 0.0458 -0.0400 2.26 672.71 1.56 0.0062 1050.00 +8 Real Estate 0.0291 0.0155 -0.0515 -0.0096 0.1091 -0.0902 2.22 355.26 1.98 0.0031 702.84 +9 Technology 0.0889 -0.0306 -0.0989 -0.0704 0.1030 -0.1294 1.98 1650.00 1.91 0.0251 3160.00 +10 Utilities 0.0090 0.0609 0.0065 0.0638 0.1145 -0.0122 2.32 161.37 2.25 -0.0063 362.51 diff --git a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[True].txt b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[True].txt index 32c62890f33..148bb294ce0 100644 --- a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_performance[True].txt @@ -1,13 +1,13 @@ Name Week Month 3Month 6Month 1Year YTD Recom AvgVolume (1M) RelVolume Change Volume (1M) -0 Basic Materials -0.0052 0.0249 0.0546 0.0385 0.0534 0.0097 2.21 558.28 1.39 0.0190 776.75 -1 Communication Services 0.0071 -0.0375 -0.1241 -0.1875 -0.0663 -0.1365 1.88 760.94 1.39 0.0374 1060.00 -2 Consumer Cyclical 0.0367 -0.0600 -0.1375 -0.1334 -0.1426 -0.1547 2.05 1330.00 1.50 0.0616 1990.00 -3 Consumer Defensive 0.0093 -0.0211 -0.0245 -0.0049 0.0338 -0.0531 2.23 452.89 1.41 0.0078 640.39 -4 Energy -0.0435 -0.0025 0.2170 0.2659 0.2949 0.1941 2.23 882.68 1.03 0.0033 908.76 -5 Financial 0.0356 -0.0341 -0.0057 -0.0089 0.0466 -0.0224 2.23 1050.00 1.24 0.0300 1300.00 -6 Healthcare 0.0211 0.0209 -0.0432 -0.0825 0.0072 -0.0822 2.06 1430.00 1.13 0.0196 1620.00 -7 Industrials 0.0240 0.0176 -0.0294 -0.0155 0.0137 -0.0579 2.26 670.52 1.24 0.0202 832.93 -8 Real Estate 0.0048 0.0141 -0.0546 -0.0318 0.0660 -0.1041 2.22 355.72 1.64 0.0152 585.12 -9 Technology 0.0079 -0.0403 -0.1344 -0.1126 0.0335 -0.1601 1.98 1640.00 1.39 0.0418 2280.00 -10 Utilities 0.0083 0.0642 0.0270 0.0431 0.1125 -0.0111 2.32 160.03 1.13 0.0024 181.12 +0 Basic Materials 0.0303 0.0337 0.0721 0.1212 0.1058 0.0419 2.21 564.39 1.70 0.0070 961.36 +1 Communication Services 0.0637 -0.0258 -0.1031 -0.1558 -0.0248 -0.1145 1.88 764.96 1.78 0.0169 1360.00 +2 Consumer Cyclical 0.1093 -0.0505 -0.0915 -0.1022 -0.0962 -0.1226 2.06 1340.00 1.78 0.0256 2390.00 +3 Consumer Defensive 0.0475 -0.0126 -0.0218 0.0161 0.0506 -0.0392 2.23 455.72 1.71 0.0063 779.27 +4 Energy -0.0182 0.0352 0.2454 0.3398 0.3865 0.2319 2.23 887.41 1.38 -0.0003 1230.00 +5 Financial 0.0690 -0.0310 -0.0012 0.0172 0.0777 -0.0049 2.23 1050.00 1.67 0.0059 1750.00 +6 Healthcare 0.0669 0.0299 -0.0388 -0.0592 0.0399 -0.0565 2.05 1420.00 1.66 0.0086 2360.00 +7 Industrials 0.0541 0.0143 -0.0165 0.0164 0.0458 -0.0400 2.26 672.71 1.56 0.0062 1050.00 +8 Real Estate 0.0291 0.0155 -0.0515 -0.0096 0.1091 -0.0902 2.22 355.26 1.98 0.0031 702.84 +9 Technology 0.0889 -0.0306 -0.0989 -0.0704 0.1030 -0.1294 1.98 1650.00 1.91 0.0251 3160.00 +10 Utilities 0.0090 0.0609 0.0065 0.0638 0.1145 -0.0122 2.32 161.37 2.25 -0.0063 362.51 diff --git a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[False].txt b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[False].txt index 6c98f283add..603dbe664c2 100644 --- a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[False].txt @@ -1,13 +1,13 @@ Name MarketCap P/E FwdP/E PEG P/S P/B P/C P/FCF EPSpast5Y EPSnext5Y Salespast5Y Change Volume (1M) -0 Basic Materials 2191.01 10.71 13.62 0.72 1.58 2.23 12.75 19.68 0.3018 0.1491 0.1227 0.0190 776.75 -1 Communication Services 7234.11 20.86 14.79 1.03 3.36 3.18 13.84 33.20 0.2041 0.2031 0.1988 0.0374 1060.00 -2 Consumer Cyclical 7138.80 24.93 13.83 1.10 1.06 3.43 9.81 40.02 0.2795 0.2260 0.1972 0.0616 1990.00 -3 Consumer Defensive 3683.90 24.47 20.93 2.66 1.37 4.03 20.07 42.58 0.0843 0.0921 0.0479 0.0078 640.39 -4 Energy 3381.60 12.01 9.33 1.08 0.93 1.22 9.87 14.51 0.2803 0.1116 0.0874 0.0033 908.76 -5 Financial 9964.12 12.64 13.46 1.02 2.91 0.01 0.58 12.57 0.1880 0.1236 0.0727 0.0300 1300.00 -6 Healthcare 7599.72 24.30 15.09 2.17 2.17 3.56 12.56 28.67 0.1612 0.1119 0.1581 0.0196 1620.00 -7 Industrials 4889.74 22.91 16.75 1.53 1.98 3.93 13.49 32.16 0.1620 0.1495 0.0563 0.0202 832.93 -8 Real Estate 1758.91 27.56 30.89 2.75 5.55 1.80 22.97 60.58 0.1045 0.1002 0.1005 0.0152 585.12 -9 Technology 13592.54 29.36 21.83 1.71 4.96 4.37 15.75 34.39 0.2460 0.1720 0.1467 0.0418 2280.00 -10 Utilities 1447.17 26.15 3.29 3.71 1.83 1.42 32.50 66.06 0.0136 0.0706 0.0514 0.0024 181.12 +0 Basic Materials 2260.70 11.05 14.06 0.74 1.63 2.30 13.15 20.31 0.3022 0.1501 0.1231 0.0070 961.36 +1 Communication Services 7417.63 21.39 15.17 1.05 3.45 3.26 14.19 34.05 0.2059 0.2033 0.2022 0.0169 1360.00 +2 Consumer Cyclical 7410.15 25.88 14.36 1.14 1.10 3.56 10.19 41.54 0.2838 0.2265 0.2012 0.0256 2390.00 +3 Consumer Defensive 3737.73 24.83 21.23 2.68 1.39 4.09 20.36 43.20 0.0850 0.0925 0.0486 0.0063 779.27 +4 Energy 3488.00 12.39 9.63 1.11 0.96 1.26 10.18 14.97 0.2792 0.1112 0.0878 -0.0003 1230.00 +5 Financial 10142.30 12.87 13.70 1.04 2.96 0.01 0.59 12.79 0.1888 0.1240 0.0734 0.0059 1750.00 +6 Healthcare 7812.17 24.97 15.52 2.22 2.23 3.66 12.91 29.47 0.1617 0.1123 0.1602 0.0086 2360.00 +7 Industrials 4981.60 23.34 17.07 1.56 2.02 4.00 13.74 32.77 0.1624 0.1492 0.0566 0.0062 1050.00 +8 Real Estate 1786.12 27.99 31.37 2.79 5.64 1.83 23.32 61.52 0.1051 0.1005 0.1014 0.0031 702.84 +9 Technology 14089.76 30.43 22.63 1.76 5.14 4.53 16.32 35.65 0.2480 0.1731 0.1491 0.0251 3160.00 +10 Utilities 1445.62 26.13 3.29 3.71 1.82 1.42 32.47 65.99 0.0141 0.0704 0.0520 -0.0063 362.51 diff --git a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[True].txt b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[True].txt index 6c98f283add..603dbe664c2 100644 --- a/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_finviz_view/test_display_valuation[True].txt @@ -1,13 +1,13 @@ Name MarketCap P/E FwdP/E PEG P/S P/B P/C P/FCF EPSpast5Y EPSnext5Y Salespast5Y Change Volume (1M) -0 Basic Materials 2191.01 10.71 13.62 0.72 1.58 2.23 12.75 19.68 0.3018 0.1491 0.1227 0.0190 776.75 -1 Communication Services 7234.11 20.86 14.79 1.03 3.36 3.18 13.84 33.20 0.2041 0.2031 0.1988 0.0374 1060.00 -2 Consumer Cyclical 7138.80 24.93 13.83 1.10 1.06 3.43 9.81 40.02 0.2795 0.2260 0.1972 0.0616 1990.00 -3 Consumer Defensive 3683.90 24.47 20.93 2.66 1.37 4.03 20.07 42.58 0.0843 0.0921 0.0479 0.0078 640.39 -4 Energy 3381.60 12.01 9.33 1.08 0.93 1.22 9.87 14.51 0.2803 0.1116 0.0874 0.0033 908.76 -5 Financial 9964.12 12.64 13.46 1.02 2.91 0.01 0.58 12.57 0.1880 0.1236 0.0727 0.0300 1300.00 -6 Healthcare 7599.72 24.30 15.09 2.17 2.17 3.56 12.56 28.67 0.1612 0.1119 0.1581 0.0196 1620.00 -7 Industrials 4889.74 22.91 16.75 1.53 1.98 3.93 13.49 32.16 0.1620 0.1495 0.0563 0.0202 832.93 -8 Real Estate 1758.91 27.56 30.89 2.75 5.55 1.80 22.97 60.58 0.1045 0.1002 0.1005 0.0152 585.12 -9 Technology 13592.54 29.36 21.83 1.71 4.96 4.37 15.75 34.39 0.2460 0.1720 0.1467 0.0418 2280.00 -10 Utilities 1447.17 26.15 3.29 3.71 1.83 1.42 32.50 66.06 0.0136 0.0706 0.0514 0.0024 181.12 +0 Basic Materials 2260.70 11.05 14.06 0.74 1.63 2.30 13.15 20.31 0.3022 0.1501 0.1231 0.0070 961.36 +1 Communication Services 7417.63 21.39 15.17 1.05 3.45 3.26 14.19 34.05 0.2059 0.2033 0.2022 0.0169 1360.00 +2 Consumer Cyclical 7410.15 25.88 14.36 1.14 1.10 3.56 10.19 41.54 0.2838 0.2265 0.2012 0.0256 2390.00 +3 Consumer Defensive 3737.73 24.83 21.23 2.68 1.39 4.09 20.36 43.20 0.0850 0.0925 0.0486 0.0063 779.27 +4 Energy 3488.00 12.39 9.63 1.11 0.96 1.26 10.18 14.97 0.2792 0.1112 0.0878 -0.0003 1230.00 +5 Financial 10142.30 12.87 13.70 1.04 2.96 0.01 0.59 12.79 0.1888 0.1240 0.0734 0.0059 1750.00 +6 Healthcare 7812.17 24.97 15.52 2.22 2.23 3.66 12.91 29.47 0.1617 0.1123 0.1602 0.0086 2360.00 +7 Industrials 4981.60 23.34 17.07 1.56 2.02 4.00 13.74 32.77 0.1624 0.1492 0.0566 0.0062 1050.00 +8 Real Estate 1786.12 27.99 31.37 2.79 5.64 1.83 23.32 61.52 0.1051 0.1005 0.1014 0.0031 702.84 +9 Technology 14089.76 30.43 22.63 1.76 5.14 4.53 16.32 35.65 0.2480 0.1731 0.1491 0.0251 3160.00 +10 Utilities 1445.62 26.13 3.29 3.71 1.82 1.42 32.47 65.99 0.0141 0.0704 0.0520 -0.0063 362.51 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-False].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-False].txt index eab046eab5c..2ecc3b4ec18 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-False].txt @@ -1,12 +1,12 @@ Last Chng %Chng -0 Euro (EUR/USD) 1.1056 0.0021 0.19 -1 Japanese Yen (USD/JPY) 118.64 -0.11 -0.10 -2 U.K. Pound (GBP/USD) 1.3190 0.0041 0.31 -3 Swiss Franc (USD/CHF) 0.9407 -0.0001 -0.01 -4 Chinese Yuan (USD/CNY) 6.3478 -0.0045 -0.07 -5 Canadian $ (USD/CAD) 1.2658 -0.0021 -0.17 -6 Mexican Peso (USD/MXN) 20.6109 -0.0275 -0.13 -7 Bitcoin (BTC/USD) 40887 -366 -0.89 -8 WSJ Dollar Index 91.18 -0.18 -0.20 -9 U.S. Dollar Index 98.23 -0.39 -0.39 +0 Euro (EUR/USD) 1.1042 -0.0008 -0.07 +1 Japanese Yen (USD/JPY) 119.22 0.08 0.07 +2 U.K. Pound (GBP/USD) 1.3130 -0.0047 -0.36 +3 Swiss Franc (USD/CHF) 0.9315 -0.0004 -0.04 +4 Chinese Yuan (USD/CNY) 6.3553 -0.0059 -0.09 +5 Canadian $ (USD/CAD) 1.2612 0.0007 0.06 +6 Mexican Peso (USD/MXN) 20.4113 0.0486 0.24 +7 Bitcoin (BTC/USD) 41336 28 0.07 +8 WSJ Dollar Index 91.20 0.11 0.12 +9 U.S. Dollar Index 98.29 0.06 0.06 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-True].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-True].txt index 94667982b2c..2ecc3b4ec18 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_currencies-True].txt @@ -1,12 +1,12 @@ Last Chng %Chng -0 Euro (EUR/USD) 1.1052 0.0018 0.16 -1 Japanese Yen (USD/JPY) 118.65 -0.10 -0.09 -2 U.K. Pound (GBP/USD) 1.3190 0.0041 0.31 -3 Swiss Franc (USD/CHF) 0.9407 -0.0001 -0.01 -4 Chinese Yuan (USD/CNY) 6.3478 -0.0045 -0.07 -5 Canadian $ (USD/CAD) 1.2662 -0.0017 -0.13 -6 Mexican Peso (USD/MXN) 20.6145 -0.0239 -0.12 -7 Bitcoin (BTC/USD) 40887 -366 -0.89 -8 WSJ Dollar Index 91.19 -0.18 -0.20 -9 U.S. Dollar Index 98.24 -0.38 -0.38 +0 Euro (EUR/USD) 1.1042 -0.0008 -0.07 +1 Japanese Yen (USD/JPY) 119.22 0.08 0.07 +2 U.K. Pound (GBP/USD) 1.3130 -0.0047 -0.36 +3 Swiss Franc (USD/CHF) 0.9315 -0.0004 -0.04 +4 Chinese Yuan (USD/CNY) 6.3553 -0.0059 -0.09 +5 Canadian $ (USD/CAD) 1.2612 0.0007 0.06 +6 Mexican Peso (USD/MXN) 20.4113 0.0486 0.24 +7 Bitcoin (BTC/USD) 41336 28 0.07 +8 WSJ Dollar Index 91.20 0.11 0.12 +9 U.S. Dollar Index 98.29 0.06 0.06 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-False].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-False].txt index 79953470faf..03478726122 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-False].txt @@ -1,12 +1,12 @@ - Price Chg %Chg -0 Crude Oil Futures 98.28 3.24 3.41 -1 Brent Crude Futures 101.29 3.27 3.34 -2 Gold Futures 1942.10 32.90 1.72 -3 Silver Futures 25.480 0.770 3.12 -4 Natural Gas Futures 4.755 -0.029 -0.61 -5 Unleaded Gasoline Futures 3.0623 0.0748 2.50 -6 Copper Futures 4.6610 0.0605 1.32 -7 Corn Futures 737.75 7.75 1.06 -8 Wheat Futures 1071.75 2.50 0.23 -9 Bloomberg Commodity Index 121.91 1.24 1.03 + Price Chg %Chg +0 Crude Oil Futures 109.25 4.55 4.35 +1 Brent Crude Futures 112.47 4.54 4.21 +2 Gold Futures 1926.30 -3.00 -0.16 +3 Silver Futures 25.180 0.093 0.37 +4 Natural Gas Futures 4.945 0.043 0.88 +5 Unleaded Gasoline Futures 3.3242 0.0854 2.64 +6 Copper Futures 4.6710 -0.0685 -1.45 +7 Corn Futures 752.25 10.50 1.42 +8 Wheat Futures 1079.25 15.50 1.46 +9 Bloomberg Commodity Index 125.49 2.12 1.72 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-True].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-True].txt index 79953470faf..0347ce762c5 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_futures-True].txt @@ -1,12 +1,12 @@ - Price Chg %Chg -0 Crude Oil Futures 98.28 3.24 3.41 -1 Brent Crude Futures 101.29 3.27 3.34 -2 Gold Futures 1942.10 32.90 1.72 -3 Silver Futures 25.480 0.770 3.12 -4 Natural Gas Futures 4.755 -0.029 -0.61 -5 Unleaded Gasoline Futures 3.0623 0.0748 2.50 -6 Copper Futures 4.6610 0.0605 1.32 -7 Corn Futures 737.75 7.75 1.06 -8 Wheat Futures 1071.75 2.50 0.23 -9 Bloomberg Commodity Index 121.91 1.24 1.03 + Price Chg %Chg +0 Crude Oil Futures 109.28 4.58 4.37 +1 Brent Crude Futures 112.49 4.56 4.22 +2 Gold Futures 1927.00 -2.30 -0.12 +3 Silver Futures 25.195 0.108 0.43 +4 Natural Gas Futures 4.940 0.038 0.78 +5 Unleaded Gasoline Futures 3.3258 0.0870 2.69 +6 Copper Futures 4.6730 -0.0665 -1.40 +7 Corn Futures 752.50 10.75 1.45 +8 Wheat Futures 1079.25 15.50 1.46 +9 Bloomberg Commodity Index 125.47 2.11 1.71 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-False].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-False].txt index 2462ca5cf71..0debb8e16ee 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-False].txt @@ -1,8 +1,8 @@ Rate (%) Yld (%) Yld Chg (%) -0 U.S. 10 Year Treasury Note 1.875 2.137 -0.059 -1 Germany 10 Year Government Bond 0.000 0.371 -0.022 -2 U.K. 10 Year Gilt 4.250 1.601 -0.031 -3 Japan 10 Year Government Bond 0.100 0.204 0.000 -4 Australia 10 Year Government Bond 1.000 2.509 0.018 -5 China 10 Year Government Bond 2.890 2.818 0.002 +0 U.S. 10 Year Treasury Note 1.875 2.187 0.034 +1 Germany 10 Year Government Bond 0.000 0.405 0.034 +2 U.K. 10 Year Gilt 4.250 1.545 0.045 +3 Japan 10 Year Government Bond 0.100 0.208 0.000 +4 Australia 10 Year Government Bond 1.000 2.575 0.003 +5 China 10 Year Government Bond 2.890 2.831 0.011 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-True].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-True].txt index 2462ca5cf71..c3711b04a66 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_glbonds-True].txt @@ -1,8 +1,8 @@ Rate (%) Yld (%) Yld Chg (%) -0 U.S. 10 Year Treasury Note 1.875 2.137 -0.059 -1 Germany 10 Year Government Bond 0.000 0.371 -0.022 -2 U.K. 10 Year Gilt 4.250 1.601 -0.031 -3 Japan 10 Year Government Bond 0.100 0.204 0.000 -4 Australia 10 Year Government Bond 1.000 2.509 0.018 -5 China 10 Year Government Bond 2.890 2.818 0.002 +0 U.S. 10 Year Treasury Note 1.875 2.188 0.035 +1 Germany 10 Year Government Bond 0.000 0.404 0.033 +2 U.K. 10 Year Gilt 4.250 1.544 0.044 +3 Japan 10 Year Government Bond 0.100 0.208 0.000 +4 Australia 10 Year Government Bond 1.000 2.575 0.003 +5 China 10 Year Government Bond 2.890 2.831 0.011 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-False].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-False].txt index 625315a0fbd..28b43677c3a 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-False].txt @@ -1,12 +1,12 @@ - Price Chg %Chg -0 DJIA 34063.10 518.76 1.55 -1 Nasdaq Composite 13436.55 487.93 3.77 -2 S&P 500 4357.86 95.41 2.24 -3 DJ Total Stock Market 44110.08 1065.28 2.47 -4 Russell 2000 2030.72 61.75 3.14 -5 NYSE Composite 16261.50 365.83 2.30 -6 Barron's 400 1011.50 26.13 2.65 -7 CBOE Volatility 26.78 0.11 0.41 -8 DJIA Futures 33957 3 0.01 -9 S&P 500 Futures 4348.25 -1.25 -0.03 + Price Chg %Chg +0 DJIA 34754.93 274.17 0.80 +1 Nasdaq Composite 13893.84 279.06 2.05 +2 S&P 500 4463.12 51.45 1.17 +3 DJ Total Stock Market 45264.01 558.62 1.25 +4 Russell 2000 2086.14 21.12 1.02 +5 NYSE Composite 16612.64 129.06 0.78 +6 Barron's 400 1031.79 10.22 1.00 +7 CBOE Volatility 24.57 0.70 2.93 +8 DJIA Futures 34485 -148 -0.43 +9 S&P 500 Futures 4442.50 -11.00 -0.25 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-True].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-True].txt index 625315a0fbd..e305f80a9a2 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_indices-True].txt @@ -1,12 +1,12 @@ - Price Chg %Chg -0 DJIA 34063.10 518.76 1.55 -1 Nasdaq Composite 13436.55 487.93 3.77 -2 S&P 500 4357.86 95.41 2.24 -3 DJ Total Stock Market 44110.08 1065.28 2.47 -4 Russell 2000 2030.72 61.75 3.14 -5 NYSE Composite 16261.50 365.83 2.30 -6 Barron's 400 1011.50 26.13 2.65 -7 CBOE Volatility 26.78 0.11 0.41 -8 DJIA Futures 33957 3 0.01 -9 S&P 500 Futures 4348.25 -1.25 -0.03 + Price Chg %Chg +0 DJIA 34754.93 274.17 0.80 +1 Nasdaq Composite 13893.84 279.06 2.05 +2 S&P 500 4463.12 51.45 1.17 +3 DJ Total Stock Market 45264.01 558.62 1.25 +4 Russell 2000 2086.14 21.12 1.02 +5 NYSE Composite 16612.64 129.06 0.78 +6 Barron's 400 1031.79 10.22 1.00 +7 CBOE Volatility 24.55 0.68 2.85 +8 DJIA Futures 34490 -143 -0.41 +9 S&P 500 Futures 4443.00 -10.50 -0.24 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-False].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-False].txt index 22b6d6653f7..fb3abf02bc6 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-False].txt @@ -1,11 +1,11 @@ - Price Chg %Chg -0 DJIA 34063.10 518.76 1.55 -1 S&P 500 4357.86 95.41 2.24 -2 Nasdaq Composite 13436.55 487.93 3.77 -3 Japan: Nikkei 225 26652.89 890.88 3.46 -4 UK: FTSE 100 7316.51 24.83 0.34 -5 Crude Oil Futures 98.21 3.17 3.34 -6 Gold Futures 1942.30 33.10 1.73 -7 Yen 118.65 -0.10 -0.09 -8 Euro 1.1054 0.0019 0.17 + Price Chg %Chg +0 DJIA 34754.93 274.17 0.80 +1 S&P 500 4463.12 51.45 1.17 +2 Nasdaq Composite 13893.84 279.06 2.05 +3 Japan: Nikkei 225 26827.43 174.54 0.65 +4 UK: FTSE 100 7450.86 46.13 0.62 +5 Crude Oil Futures 109.18 4.48 4.28 +6 Gold Futures 1926.30 -3.00 -0.16 +7 Yen 119.22 0.08 0.07 +8 Euro 1.1042 -0.0008 -0.07 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-True].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-True].txt index 81129a8e0cd..6d421515e18 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_overview-True].txt @@ -1,11 +1,11 @@ - Price Chg %Chg -0 DJIA 34063.10 518.76 1.55 -1 S&P 500 4357.86 95.41 2.24 -2 Nasdaq Composite 13436.55 487.93 3.77 -3 Japan: Nikkei 225 26652.89 890.88 3.46 -4 UK: FTSE 100 7317.32 25.64 0.35 -5 Crude Oil Futures 98.26 3.22 3.39 -6 Gold Futures 1942.10 32.90 1.72 -7 Yen 118.65 -0.10 -0.08 -8 Euro 1.1052 0.0018 0.16 + Price Chg %Chg +0 DJIA 34754.93 274.17 0.80 +1 S&P 500 4463.12 51.45 1.17 +2 Nasdaq Composite 13893.84 279.06 2.05 +3 Japan: Nikkei 225 26827.43 174.54 0.65 +4 UK: FTSE 100 7450.84 46.11 0.62 +5 Crude Oil Futures 109.28 4.58 4.37 +6 Gold Futures 1926.90 -2.40 -0.12 +7 Yen 119.23 0.09 0.07 +8 Euro 1.1040 -0.0010 -0.09 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-False].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-False].txt index 34bb2daa8e2..16ea7f1ce49 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-False].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-False].txt @@ -1,12 +1,12 @@ Rate (%) Yld (%) Yld Chg (%) -0 30-Year Bond 2.250 2.400 -0.059 -1 10-Year Note 1.875 2.137 -0.059 -2 7-Year Note 1.875 2.168 -0.060 -3 5-Year Note 1.875 2.137 -0.057 -4 3-Year Note 1.750 2.121 -0.035 -5 2-Year Note 1.500 1.930 -0.024 -6 1-Year Bill 0.000 1.215 -0.017 -7 6-Month Bill 0.000 0.778 -0.037 -8 3-Month Bill 0.000 0.393 -0.046 -9 1-Month Bill 0.000 0.182 -0.020 +0 30-Year Bond 2.250 2.438 0.011 +1 10-Year Note 1.875 2.188 0.035 +2 7-Year Note 1.875 2.220 0.036 +3 5-Year Note 1.875 2.194 0.049 +4 3-Year Note 1.750 2.183 0.030 +5 2-Year Note 1.500 1.986 0.042 +6 1-Year Bill 0.000 1.198 0.000 +7 6-Month Bill 0.000 0.782 -0.005 +8 3-Month Bill 0.000 0.382 -0.023 +9 1-Month Bill 0.000 0.210 0.020 diff --git a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-True].txt b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-True].txt index 3ab23d8a5f4..16ea7f1ce49 100644 --- a/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-True].txt +++ b/tests/gamestonk_terminal/economy/txt/test_wsj_view/test_call_func[display_usbonds-True].txt @@ -1,12 +1,12 @@ Rate (%) Yld (%) Yld Chg (%) -0 30-Year Bond 2.250 2.403 -0.056 -1 10-Year Note 1.875 2.139 -0.057 -2 7-Year Note 1.875 2.172 -0.057 -3 5-Year Note 1.875 2.142 -0.052 -4 3-Year Note 1.750 2.126 -0.030 -5 2-Year Note 1.500 1.934 -0.020 -6 1-Year Bill 0.000 1.215 -0.049 -7 6-Month Bill 0.000 0.778 -0.037 -8 3-Month Bill 0.000 0.388 -0.045 -9 1-Month Bill 0.000 0.180 -0.022 +0 30-Year Bond 2.250 2.438 0.011 +1 10-Year Note 1.875 2.188 0.035 +2 7-Year Note 1.875 2.220 0.036 +3 5-Year Note 1.875 2.194 0.049 +4 3-Year Note 1.750 2.183 0.030 +5 2-Year Note 1.500 1.986 0.042 +6 1-Year Bill 0.000 1.198 0.000 +7 6-Month Bill 0.000 0.782 -0.005 +8 3-Month Bill 0.000 0.382 -0.023 +9 1-Month Bill 0.000 0.210 0.020 diff --git a/tests/gamestonk_terminal/forex/__init__.py b/tests/gamestonk_terminal/forex/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[CHF-USD].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[CHF-USD].yaml new file mode 100644 index 00000000000..1b9eccda711 --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[CHF-USD].yaml @@ -0,0 +1,525 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: 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Content-Type: + - application/json + Date: + - Mon, 21 Mar 2022 11:22:47 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[EUR-USD].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[EUR-USD].yaml new file mode 100644 index 00000000000..ed2eebdc37c --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[EUR-USD].yaml @@ -0,0 +1,1355 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_symbol=EUR&function=FX_DAILY&outputsize=full&to_symbol=USD + response: + body: + string: !!binary | + H4sIAAAAAAAAAwAAAP//hJdba9tAEEbf+ysGPSXgiLns1a91DYWWljiB0jc3VRqBbQXboU1D/nuR + ghNfyn6P1o7Xx2dmdkdP74iIqs/Ndk6T+XZejelpeDQ8lpo+rm679XK+bbtVNaZq2q2bPzSZt4tH + +rpub5oNnXX3zWpEd+2vuxEtut8jull0m+a8Gr1tpDVN192SZo/LH92i3+jD9eV+gNV01e0tX88m + +8uupi8P2/uHLc3av80A8rBY0Kb/sBfma/o032zpsrldN5u75mcfqax6wXahQiJj9WPm/e+Emq7a + ZUPfu9Ww8fXV+2pYfX4JqobVWbNumw1Nv9HZ8OfPD0W9/cbB853E3lC/t9TCTmXv53d2enu7iMh2 + HGF1b3YXoI6PA1z9Yn0XYmzVa8Tz6JRUEiTNHMqkIimUSVkgqWcGpBGSmgCnYtED0gBJU0qANABS + zi4Bp+xCKpFydjHg7GdA6jGpRkAqOZdJYZ1ydhll30FSiVok5ZxPQQ5IU86YVBGpQNJ0ynGcffNl + p8yQlDPKPsOOipZAR6lomTTi7CcHSDkjpylnD5xmb+XspwR7P3qQfU6Q1FsEdeoTIHUROU05g5Of + I6xTtQBITct1mhhmP3mYfQfrNATUUSGB3k+4TtUJIDVE+r+78rij1JXvKFNYp8EjpwpJ1RwgdeXz + VNh7RDr4KJPCWUolAqdqJmVS3PuiVnSqF5qwUw/qVF0oTyiipxfyEalK9IDUQ9IsyGl05flUApxP + 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i5Y662vrey7amXTbstRspD2//KmVBj4t/ev9/fsfAAAA//8DAKntBLKU+QwA + headers: + Allow: + - GET, HEAD, OPTIONS + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 6ef64fa27a786b39-AMS + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Mon, 21 Mar 2022 11:22:40 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[GBP-EUR].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[GBP-EUR].yaml new file mode 100644 index 00000000000..de0e9e21ba5 --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[GBP-EUR].yaml @@ -0,0 +1,1278 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_symbol=GBP&function=FX_DAILY&outputsize=full&to_symbol=EUR + response: + body: + string: !!binary | + H4sIAAAAAAAAAwAAAP//jNdNb9NAEAbgO79i5FMrJdbs7HeOUIKQQFRNKiFuprjEkhNXdiIoVf87 + sttQJ0X75lh7u3nyzuxHHt4QEWWfy21BF8W2yGb0MDwaHqucPm5um3ZdbKtmk80omzdt+Zsuiqq+ + p8u2uik7Omvuys2EVtXP1YTq5teEbuqmK8+zyctEktO8bda0uF9/b+p+og9vL8cDdE7LZvT6/fXV + +LXJ6ctue7fb0qL6Uw6QXV1T1/8xGmZz+lR0W7oqb9uyW5U/+pHCIlPWU1Gk1EzsjHn8Py6nZbUu + 6VuzGSa+Xr7LhrePT4Oy4e2ibKuyo/lXOhu+/PlhUC+fcfB8H2KfUD+3ylVUfvzx+3T69PYjRNnj + ETrvk30eEHx4NYXJn1Lfz8Ecsn8jHievpSogaTAxAql2kpYaD6WiGUg9zJSjA1ITTFoqjKTBag2k + Dku1RtV3Ki11YlGmCmZqYfW9R9UXDfq0Ly3qU4WkBmaqFVpR+nUnH0otXFHDoktL1Ql9iqSOQfW9 + 9yhTI0jKWMoMVxSQWj5hlwJSjkAqrExaKvyfNjzO9ASpAdIApS4iqQ/p/TR6C6TC/fmSlnoojQFI + 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11:22:44 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[USD-JPY].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[USD-JPY].yaml new file mode 100644 index 00000000000..0dd8b777b88 --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_model/test_get_historical[USD-JPY].yaml @@ -0,0 +1,1392 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_symbol=USD&function=FX_DAILY&outputsize=full&to_symbol=JPY + response: 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6ef64faaca6c9758-AMS + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Mon, 21 Mar 2022 11:22:42 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[CHF-USD].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[CHF-USD].yaml new file mode 100644 index 00000000000..dea2786ab6f --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[CHF-USD].yaml @@ -0,0 +1,53 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_currency=CHF&function=CURRENCY_EXCHANGE_RATE&to_currency=USD + response: + body: + string: !!binary | + H4sIAAAAAAAAA3zQT0vDMBgG8Ps+xUPOLjRpt9netLN4EJG2u+wioX23FdsEkojK2HeXVtToxPP7 + e95/xxkAsJJU77uBkD9bS7p5w81rc1B6TyiVJ5bhOMEJC47CmuHxy+amHQnLbwt28e3kb3evhslV + L51zKKzSTehjjtqcd91U61AlP9Vnz43uPLWovPLksDZ9r2wYW/Czk5jg0SoR8TKNQrnkuFPOo6Sd + JXegdqQyknIexXMpIEQm0yxZhJkVRz2+b2v0xzZ1HpYvOa67Fg+2a/4ZnHJcuac/1YROs9M7AAAA + //8DAJHb3+OvAQAA + headers: + Allow: + - GET, HEAD, OPTIONS + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 6ef65a0cef9b0ba5-AMS + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Mon, 21 Mar 2022 11:29:48 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[EUR-USD].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[EUR-USD].yaml new file mode 100644 index 00000000000..0f6cced688e --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[EUR-USD].yaml @@ -0,0 +1,53 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_currency=EUR&function=CURRENCY_EXCHANGE_RATE&to_currency=USD + response: + body: + string: !!binary | + H4sIAAAAAAAAA3zQy07CQBQG4D1P8WfWMpk5RYXutODKGFPaDRsyaY/S2HaS6ZBoCO9uWgIOl7g+ + 339uuxEAiJRN7auGkWyd47b4weK72Jj2k5EazyLGboAD1hIvzjbrk01s2ROxyFNx9+fo0r2Z5uC2 + zoYwksjsdbt8OQ/V5Fwdm+Vt5bnE0hvPHea2ro0LY/fy6hahpVaRIopUKB8kXk3nkfKH427DZU9J + EY1VNCYNrWOaxRMKM48SWf+3lW0P22RJWJ5KPFcl3l1V/DN4JvHUfd1UA9qP9r8AAAD//wMAvdGY + OKgBAAA= + headers: + Allow: + - GET, HEAD, OPTIONS + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 6ef659f85b93fa44-AMS + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Mon, 21 Mar 2022 11:29:44 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[GBP-EUR].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[GBP-EUR].yaml new file mode 100644 index 00000000000..00ee69751df --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[GBP-EUR].yaml @@ -0,0 +1,53 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_currency=GBP&function=CURRENCY_EXCHANGE_RATE&to_currency=EUR + response: + body: + string: !!binary | + H4sIAAAAAAAAA3zQwUvDMBQG8Pv+io+cXUjSTdfebKm7iJTaXbxIaOMaXBNIU1DG/vfRgJo58fx+ + 7/t477gAAFIrefB6UCgm55RpP1F+tL00e4VaekUyHAMMmFM8ODu8ftvCdjMh27wiNz9O/HZPcggu + d9rrsUdlJ9Ph2St30GYfryYUjb0uKHd1rFaX6iu+nJyN2ZpeXUM45SnbMMZYLG8pHuXoUas3p8Ze + dTMVTIglS5aCg/NMpFlysXNH0cyfe7EmJO+aIh5vKHLdoXK6/ac4pbgf3/9UAZ0WpzMAAAD//wMA + g84xEqoBAAA= + headers: + Allow: + - GET, HEAD, OPTIONS + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 6ef65a05cefafa18-AMS + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Mon, 21 Mar 2022 11:29:46 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Cookie + Via: + - 1.1 vegur + X-Frame-Options: + - SAMEORIGIN + status: + code: 200 + message: OK +version: 1 diff --git a/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[USD-JPY].yaml b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[USD-JPY].yaml new file mode 100644 index 00000000000..84de9486905 --- /dev/null +++ b/tests/gamestonk_terminal/forex/cassettes/test_av_view/test_display_quote[USD-JPY].yaml @@ -0,0 +1,53 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + User-Agent: + - python-requests/2.27.1 + method: GET + uri: https://www.alphavantage.co/query?apikey=MOCK_API_KEY&from_currency=USD&function=CURRENCY_EXCHANGE_RATE&to_currency=JPY + response: + body: + string: !!binary | + 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+2022-03-16,1.09486,1.10468,1.09476,1.10309 +2022-03-17,1.10311,1.11375,1.1006,1.10888 +2022-03-18,1.10906,1.11186,1.1001,1.105 +2022-03-21,1.10421,1.10703,1.1024,1.10312 diff --git a/tests/gamestonk_terminal/forex/technical_analysis/test_ta_api.py b/tests/gamestonk_terminal/forex/technical_analysis/test_ta_api.py new file mode 100644 index 00000000000..2dfc85acead --- /dev/null +++ b/tests/gamestonk_terminal/forex/technical_analysis/test_ta_api.py @@ -0,0 +1,11 @@ +# IMPORTATION STANDARD + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL +from gamestonk_terminal.helper_classes import ModelsNamespace as _models +from gamestonk_terminal.forex.technical_analysis import ta_api + + +def test_models(): + assert isinstance(ta_api.models, _models) diff --git a/tests/gamestonk_terminal/forex/technical_analysis/test_ta_controller.py b/tests/gamestonk_terminal/forex/technical_analysis/test_ta_controller.py new file mode 100644 index 00000000000..a4505c4868f --- /dev/null +++ b/tests/gamestonk_terminal/forex/technical_analysis/test_ta_controller.py @@ -0,0 +1,502 @@ +# IMPORTATION STANDARD +import os +from datetime import datetime + +# IMPORTATION THIRDPARTY +import pandas as pd +import pytest + +# IMPORTATION INTERNAL +from gamestonk_terminal.forex.technical_analysis import ta_controller + +# pylint: disable=E1101 +# pylint: disable=W0603 +# pylint: disable=E1111 + +EMPTY_DF = pd.DataFrame() +MOCK_STOCK_DF = pd.read_csv( + "tests/gamestonk_terminal/forex/technical_analysis/csv/test_ta_controller/data_df.csv", + index_col=0, +) + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + "filter_headers": [("User-Agent", None)], + "filter_query_parameters": [ + ("period1", "MOCK_PERIOD_1"), + ("period2", "MOCK_PERIOD_2"), + ("date", "MOCK_DATE"), + ], + } + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "queue, expected", + [ + (["load", "help"], []), + (["quit", "help"], ["help"]), + ], +) +def test_menu_with_queue(expected, mocker, queue): + path_controller = "gamestonk_terminal.forex.technical_analysis.ta_controller" + + # MOCK SWITCH + mocker.patch( + target=f"{path_controller}.TechnicalAnalysisController.switch", + return_value=["quit"], + ) + + result_menu = ta_controller.TechnicalAnalysisController( + ticker="MOCK_TICKER", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + source="yf", + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=queue, + ).menu() + + assert result_menu == expected + + +@pytest.mark.vcr(record_mode="none") +def test_menu_without_queue_completion(mocker): + path_controller = "gamestonk_terminal.forex.technical_analysis.ta_controller" + + # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU + mocker.patch( + target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target="gamestonk_terminal.parent_classes.session", + ) + mocker.patch( + target="gamestonk_terminal.parent_classes.session.prompt", + return_value="quit", + ) + + # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER + + # DISABLE AUTO-COMPLETION + mocker.patch.object( + target=ta_controller.gtff, + attribute="USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target=f"{path_controller}.session", + ) + mocker.patch( + target=f"{path_controller}.session.prompt", + return_value="quit", + ) + + result_menu = ta_controller.TechnicalAnalysisController( + ticker="MOCK_TICKER", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "mock_input", + ["help", "homee help", "home help", "mock"], +) +def test_menu_without_queue_sys_exit(mock_input, mocker): + path_controller = "gamestonk_terminal.forex.technical_analysis.ta_controller" + + # DISABLE AUTO-COMPLETION + mocker.patch.object( + target=ta_controller.gtff, + attribute="USE_PROMPT_TOOLKIT", + new=False, + ) + mocker.patch( + target=f"{path_controller}.session", + return_value=None, + ) + + # MOCK USER INPUT + mocker.patch("builtins.input", return_value=mock_input) + + # MOCK SWITCH + class SystemExitSideEffect: + def __init__(self): + self.first_call = True + + def __call__(self, *args, **kwargs): + if self.first_call: + self.first_call = False + raise SystemExit() + return ["quit"] + + mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) + mocker.patch( + target=f"{path_controller}.TechnicalAnalysisController.switch", + new=mock_switch, + ) + + result_menu = ta_controller.TechnicalAnalysisController( + ticker="MOCK_TICKER", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.record_stdout +@pytest.mark.parametrize( + "start", + [ + datetime.strptime("2021-12-01", "%Y-%m-%d"), + ], +) +def test_print_help(start): + controller = ta_controller.TechnicalAnalysisController( + ticker="MOCK_TICKER", + source="yf", + start=start, + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ) + controller.print_help() + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "an_input, expected_queue", + [ + ("", []), + ( + "/help", + [ + "home", + "help", + ], + ), + ("help/help", ["help", "help"]), + ("q", ["quit"]), + ("h", []), + ( + "r", + [ + "quit", + "quit", + "reset", + "forex", + "from MOCK_FROM", + "to MOCK_TO", + "load", + "ta", + ], + ), + ], +) +def test_switch(an_input, expected_queue): + controller = ta_controller.TechnicalAnalysisController( + ticker="MOCK_FROM/MOCK_TO", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ) + queue = controller.switch(an_input=an_input) + + assert queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +def test_call_cls(mocker): + mocker.patch("os.system") + controller = ta_controller.TechnicalAnalysisController( + ticker="MOCK_TICKER/MOCK_TICKER", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ) + controller.call_cls([]) + + assert controller.queue == [] + os.system.assert_called_once_with("cls||clear") + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "func, queue, expected_queue", + [ + ( + "call_exit", + [], + ["quit", "quit", "quit"], + ), + ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), + ("call_home", [], ["quit", "quit"]), + ("call_help", [], []), + ("call_quit", [], ["quit"]), + ("call_quit", ["help"], ["quit", "help"]), + ( + "call_reset", + [], + [ + "quit", + "quit", + "reset", + "forex", + "from MOCK_FROM", + "to MOCK_TO", + "load", + "ta", + ], + ), + ( + "call_reset", + ["help"], + [ + "quit", + "quit", + "reset", + "forex", + "from MOCK_FROM", + "to MOCK_TO", + "load", + "ta", + "help", + ], + ), + ], +) +def test_call_func_expect_queue(expected_queue, func, queue): + controller = ta_controller.TechnicalAnalysisController( + ticker="MOCK_FROM/MOCK_TO", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=queue, + ) + result = getattr(controller, func)([]) + + assert result is None + assert controller.queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "tested_func, other_args, mocked_func, called_args, called_kwargs", + [ + ( + "call_ema", + [ + "1,2", + "--offset=2", + "--export=csv", + ], + "overlap_view.view_ma", + [], + dict(), + ), + ( + "call_sma", + [ + "1,2", + "--offset=2", + "--export=csv", + ], + "overlap_view.view_ma", + [], + dict(), + ), + ( + "call_zlma", + [ + "1,2", + "--offset=2", + "--export=csv", + ], + "overlap_view.view_ma", + [], + dict(), + ), + ( + "call_cci", + [ + "--length=1", + "--scalar=2", + "--export=csv", + ], + "momentum_view.display_cci", + [], + dict(), + ), + ( + "call_macd", + [ + "--fast=1", + "--slow=2", + "--signal=3", + "--export=csv", + ], + "momentum_view.display_macd", + [], + dict(), + ), + ( + "call_rsi", + [ + "1", + "--scalar=2", + "--drift=3", + "--export=csv", + ], + "momentum_view.display_rsi", + [], + dict(), + ), + ( + "call_stoch", + [ + "--fastkperiod=1", + "--slowdperiod=2", + "--slowkperiod=3", + "--export=csv", + ], + "momentum_view.display_stoch", + [], + dict(), + ), + ( + "call_fisher", + [ + "1", + "--export=csv", + ], + "momentum_view.display_fisher", + [], + dict(), + ), + ( + "call_cg", + [ + "1", + "--export=csv", + ], + "momentum_view.display_cg", + [], + dict(), + ), + ( + "call_adx", + [ + "1", + "--scalar=2", + "--drift=3", + "--export=csv", + ], + "trend_indicators_view.display_adx", + [], + dict(), + ), + ( + "call_aroon", + [ + "1", + "--scalar=2", + "--export=csv", + ], + "trend_indicators_view.display_aroon", + [], + dict(), + ), + ( + "call_bbands", + [ + "1", + "--std=2", + "--mamode=MOCK_MAMODE", + "--export=csv", + ], + "volatility_view.display_bbands", + [], + dict(), + ), + ( + "call_donchian", + [ + "--length_upper=1", + "--length_lower=2", + "--export=csv", + ], + "volatility_view.display_donchian", + [], + dict(), + ), + ( + "call_fib", + [ + "1", + "--start=2021-12-01", + "--end=2021-12-02", + "--export=csv", + ], + "custom_indicators_view.fibonacci_retracement", + [], + dict(), + ), + ], +) +def test_call_func( + tested_func, mocked_func, other_args, called_args, called_kwargs, mocker +): + path_controller = "gamestonk_terminal.forex.technical_analysis.ta_controller" + + if mocked_func: + mock = mocker.Mock() + mocker.patch( + target=f"{path_controller}.{mocked_func}", + new=mock, + ) + + controller = ta_controller.TechnicalAnalysisController( + ticker="MOCK_FROM/MOCK_TO", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ) + # controller.screen_tickers = ["PM"] + getattr(controller, tested_func)(other_args) + + if called_args or called_kwargs: + mock.assert_called_once_with(*called_args, **called_kwargs) + else: + mock.assert_called_once() + else: + controller = ta_controller.TechnicalAnalysisController( + ticker="MOCK_FROM/MOCK_TO", + source="yf", + start=datetime.strptime("2021-12-01", "%Y-%m-%d"), + interval="MOCK_INTERVAL", + data=MOCK_STOCK_DF, + queue=None, + ) + # controller.screen_tickers = ["PM"] + getattr(controller, tested_func)(other_args) diff --git a/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[2021-12-01].txt b/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[2021-12-01].txt new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[None].txt b/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[None].txt new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[start0].txt b/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[start0].txt new file mode 100644 index 00000000000..efd5b6b58f4 --- /dev/null +++ b/tests/gamestonk_terminal/forex/technical_analysis/txt/test_ta_controller/test_print_help[start0].txt @@ -0,0 +1,24 @@ + +Currency Pair Loaded: MOCK_TICKER (from 2021-12-01) +Source: YahooFinance + +Overlap: + ema exponential moving average + sma simple moving average + zlma zero lag moving average +Momentum: + cci commodity channel index + macd moving average convergence/divergence + rsi relative strength index + stoch stochastic oscillator + fisher fisher transform + cg centre of gravity +Trend: + adx average directional movement index + aroon aroon indicator +Volatility: + bbands bollinger bands + donchian donchian channels +Custom: + fib fibonacci retracement + diff --git a/tests/gamestonk_terminal/forex/test_av_model.py b/tests/gamestonk_terminal/forex/test_av_model.py new file mode 100644 index 00000000000..58db4644c79 --- /dev/null +++ b/tests/gamestonk_terminal/forex/test_av_model.py @@ -0,0 +1,34 @@ +# IMPORTATION STANDARD + +# IMPORTATION THIRDPARTY +import pytest + +# IMPORTATION INTERNAL +from gamestonk_terminal.forex import av_model + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + "filter_query_parameters": [ + ("apikey", "MOCK_API_KEY"), + ] + } + + +@pytest.mark.vcr +@pytest.mark.parametrize( + "from_symbol, to_symbol", + [ + ("EUR", "USD"), + ("USD", "JPY"), + ("GBP", "EUR"), + ("CHF", "USD"), + ], +) +def test_get_historical(from_symbol, to_symbol, recorder): + result = av_model.get_historical( + to_symbol=to_symbol, + from_symbol=from_symbol, + ) + recorder.capture(result) diff --git a/tests/gamestonk_terminal/forex/test_av_view.py b/tests/gamestonk_terminal/forex/test_av_view.py new file mode 100644 index 00000000000..41d31685139 --- /dev/null +++ b/tests/gamestonk_terminal/forex/test_av_view.py @@ -0,0 +1,30 @@ +# IMPORTATION STANDARD + +# IMPORTATION THIRDPARTY +import pytest + +# IMPORTATION INTERNAL +from gamestonk_terminal.forex import av_view + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + "filter_query_parameters": [ + ("apikey", "MOCK_API_KEY"), + ] + } + + +@pytest.mark.vcr +@pytest.mark.parametrize( + "from_symbol, to_symbol", + [ + ("EUR", "USD"), + ("USD", "JPY"), + ("GBP", "EUR"), + ("CHF", "USD"), + ], +) +def test_display_quote(from_symbol, to_symbol): + av_view.display_quote(from_symbol=from_symbol, to_symbol=to_symbol) diff --git a/tests/gamestonk_terminal/forex/test_forex_api.py b/tests/gamestonk_terminal/forex/test_forex_api.py new file mode 100644 index 00000000000..f2445f8a57f --- /dev/null +++ b/tests/gamestonk_terminal/forex/test_forex_api.py @@ -0,0 +1,12 @@ +# IMPORTATION STANDARD + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL +from gamestonk_terminal.helper_classes import ModelsNamespace as _models +from gamestonk_terminal.forex import forex_api + + +def test_models(): + assert isinstance(forex_api.oanda.models, _models) + assert isinstance(forex_api.ta.models, _models) diff --git a/tests/gamestonk_terminal/forex/test_forex_controller.py b/tests/gamestonk_terminal/forex/test_forex_controller.py new file mode 100644 index 00000000000..256e0b2d632 --- /dev/null +++ b/tests/gamestonk_terminal/forex/test_forex_controller.py @@ -0,0 +1,387 @@ +# IMPORTATION STANDARD +import os + +# IMPORTATION THIRDPARTY +import pandas as pd +import pytest + +# IMPORTATION INTERNAL +from gamestonk_terminal.forex import forex_controller +from gamestonk_terminal.forex.technical_analysis.ta_controller import ( + TechnicalAnalysisController, +) + +# pylint: disable=E1101 +# pylint: disable=W0603 +# pylint: disable=E1111 + +NON_EMPTY_DF = pd.DataFrame.from_dict( + data={ + pd.Timestamp("2020-11-30 00:00:00"): { + "Close": 75.75, + }, + pd.Timestamp("2020-12-01 00:00:00"): { + "Close": 77.02999877929688, + }, + }, + orient="index", +) +EMPTY_DF = pd.DataFrame() + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + "filter_headers": [("User-Agent", None)], + "filter_query_parameters": [ + ("period1", "MOCK_PERIOD_1"), + ("period2", "MOCK_PERIOD_2"), + ("date", "MOCK_DATE"), + ], + } + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "queue, expected", + [ + (["load", "help"], []), + (["quit", "help"], ["help"]), + ], +) +def test_menu_with_queue(expected, mocker, queue): + path_controller = "gamestonk_terminal.forex.forex_controller" + + # MOCK SWITCH + mocker.patch( + target=f"{path_controller}.ForexController.switch", + return_value=["quit"], + ) + result_menu = forex_controller.ForexController(queue=queue).menu() + + assert result_menu == expected + + +@pytest.mark.vcr(record_mode="none") +def test_menu_without_queue_completion(mocker): + path_controller = "gamestonk_terminal.forex.forex_controller" + + # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU + mocker.patch( + target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target="gamestonk_terminal.parent_classes.session", + ) + mocker.patch( + target="gamestonk_terminal.parent_classes.session.prompt", + return_value="quit", + ) + + # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER + mocker.patch.object( + target=forex_controller.gtff, + attribute="USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target=f"{path_controller}.session", + ) + mocker.patch( + target=f"{path_controller}.session.prompt", + return_value="quit", + ) + + result_menu = forex_controller.ForexController(queue=None).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "mock_input", + ["help", "homee help", "home help", "mock"], +) +def test_menu_without_queue_sys_exit(mock_input, mocker): + path_controller = "gamestonk_terminal.forex.forex_controller" + + # DISABLE AUTO-COMPLETION + mocker.patch.object( + target=forex_controller.gtff, + attribute="USE_PROMPT_TOOLKIT", + new=False, + ) + mocker.patch( + target=f"{path_controller}.session", + return_value=None, + ) + + # MOCK USER INPUT + mocker.patch("builtins.input", return_value=mock_input) + + # MOCK SWITCH + class SystemExitSideEffect: + def __init__(self): + self.first_call = True + + def __call__(self, *args, **kwargs): + if self.first_call: + self.first_call = False + raise SystemExit() + return ["quit"] + + mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) + mocker.patch( + target=f"{path_controller}.ForexController.switch", + new=mock_switch, + ) + + result_menu = forex_controller.ForexController(queue=None).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.record_stdout +def test_print_help(): + controller = forex_controller.ForexController(queue=None) + controller.print_help() + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "an_input, expected_queue", + [ + ("", []), + ("/help", ["home", "help"]), + ("help/help", ["help", "help"]), + ("q", ["quit"]), + ("h", []), + ( + "r", + [ + "quit", + "reset", + "forex", + ], + ), + ], +) +def test_switch(an_input, expected_queue): + controller = forex_controller.ForexController(queue=None) + queue = controller.switch(an_input=an_input) + + assert queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +def test_call_cls(mocker): + mocker.patch("os.system") + controller = forex_controller.ForexController(queue=None) + controller.call_cls([]) + + assert controller.queue == [] + os.system.assert_called_once_with("cls||clear") + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "func, queue, expected_queue", + [ + ( + "call_exit", + [], + ["quit", "quit"], + ), + ("call_exit", ["help"], ["quit", "quit", "help"]), + ("call_home", [], ["quit"]), + ("call_help", [], []), + ("call_quit", [], ["quit"]), + ("call_quit", ["help"], ["quit", "help"]), + ( + "call_reset", + [], + [ + "quit", + "reset", + "forex", + ], + ), + ( + "call_reset", + ["help"], + [ + "quit", + "reset", + "forex", + "help", + ], + ), + ], +) +def test_call_func_expect_queue(expected_queue, func, queue): + controller = forex_controller.ForexController(queue=queue) + result = getattr(controller, func)([]) + + assert result is None + assert controller.queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "tested_func, other_args, mocked_func, called_args, called_kwargs", + [ + ("call_to", ["GBP"], None, [], dict()), + ("call_from", ["EUR"], None, [], dict()), + ( + "call_ta", + None, + None, + None, + None, + ), + ( + "call_ta", + [], + "ForexController.load_class", + [TechnicalAnalysisController], + dict( + ticker="MOCK_TICKER/MOCK_TICKER", + source="yf", + data=NON_EMPTY_DF, + start=pd.Timestamp("2020-11-30 00:00:00"), + interval="", + queue=[], + ), + ), + ( + "call_oanda", + None, + "ForexController.load_class", + None, + None, + ), + ], +) +def test_call_func( + tested_func, mocked_func, other_args, called_args, called_kwargs, mocker +): + path_controller = "gamestonk_terminal.forex.forex_controller" + + if mocked_func: + mock = mocker.Mock() + mocker.patch( + target=f"{path_controller}.{mocked_func}", + new=mock, + ) + + controller = forex_controller.ForexController(queue=None) + controller.data = NON_EMPTY_DF + controller.to_symbol = "MOCK_TICKER" + controller.from_symbol = "MOCK_TICKER" + controller.interval = "" + + getattr(controller, tested_func)(other_args) + + if called_args or called_kwargs: + mock.assert_called_once_with(*called_args, **called_kwargs) + else: + mock.assert_called_once() + else: + controller = forex_controller.ForexController(queue=None) + controller.stock = EMPTY_DF + controller.source = "yf" + controller.to_symbol = "MOCK_TICKER" + controller.from_symbol = "MOCK_TICKER" + controller.interval = "1440min" + + getattr(controller, tested_func)(other_args) + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "func", + [ + "call_from", + "call_to", + "call_load", + "call_candle", + ], +) +def test_call_func_no_parser(func, mocker): + # MOCK PARSE_KNOWN_ARGS_AND_WARN + mocker.patch( + target="gamestonk_terminal.forex.forex_controller.parse_known_args_and_warn", + return_value=None, + ) + controller = forex_controller.ForexController(queue=None) + + func_result = getattr(controller, func)(other_args=list()) + assert func_result is None + assert controller.queue == [] + getattr(forex_controller, "parse_known_args_and_warn").assert_called_once() + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "func", + [ + "call_candle", + "call_ta", + # "call_qa", + # "call_pred", + ], +) +def test_call_func_no_ticker(func, mocker): + # MOCK PARSE_KNOWN_ARGS_AND_WARN + mocker.patch( + "gamestonk_terminal.forex.forex_controller.parse_known_args_and_warn", + return_value=True, + ) + + controller = forex_controller.ForexController(queue=None) + + func_result = getattr(controller, func)([]) + assert func_result is None + assert controller.queue == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "from_symbol, to_symbol, expected", + [ + (None, None, []), + ("MOCK_FROM", None, []), + (None, "MOCK_TO", []), + ("MOCK_FROM", "MOCK_TO", ["forex", "from MOCK_FROM", "to MOCK_TO"]), + ], +) +def test_custom_reset(from_symbol, to_symbol, expected): + controller = forex_controller.ForexController(queue=None) + controller.from_symbol = from_symbol + controller.to_symbol = to_symbol + + result = controller.custom_reset() + + assert result == expected + + +# @pytest.mark.vcr +# def test_call_load(mocker): +# # FORCE SINGLE THREADING +# yf_download = forex_controller.forex_helper.yf.download + +# def mock_yf_download(*args, **kwargs): +# kwargs["threads"] = False +# return yf_download(*args, **kwargs) + +# mocker.patch("yfinance.download", side_effect=mock_yf_download) + +# controller = forex_controller.ForexController(queue=None) +# other_args = [ +# "TSLA", +# "--start=2021-12-17", +# "--end=2021-12-18", +# ] +# controller.call_load(other_args=other_args) diff --git a/tests/gamestonk_terminal/forex/txt/test_forex_controller/test_print_help.txt b/tests/gamestonk_terminal/forex/txt/test_forex_controller/test_print_help.txt new file mode 100644 index 00000000000..700d39bb6d5 --- /dev/null +++ b/tests/gamestonk_terminal/forex/txt/test_forex_controller/test_print_help.txt @@ -0,0 +1,17 @@ + + from select the "from" currency in a forex pair + to select the "to" currency in a forex pair + +From: USD +To: +Source: YahooFinance[dim] + + quote get last quote [AlphaVantage] + load get historical data + candle show candle plot for loaded data + +> ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv +[/dim] +Forex brokerages: +> oanda Oanda menu + diff --git a/tests/gamestonk_terminal/jupyter/dashboards/test_dashboard_controller.py b/tests/gamestonk_terminal/jupyter/dashboards/test_dashboard_controller.py deleted file mode 100644 index 237d5f93ece..00000000000 --- a/tests/gamestonk_terminal/jupyter/dashboards/test_dashboard_controller.py +++ /dev/null @@ -1,143 +0,0 @@ -import os - -import pytest - -from gamestonk_terminal.jupyter.dashboards import dashboards_controller - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "queue, expected", - [ - (["load", "help"], []), - (["quit", "help"], ["help"]), - ], -) -def test_menu_with_queue(expected, mocker, queue): - path_controller = "gamestonk_terminal.jupyter.dashboards.dashboards_controller" - - # MOCK SWITCH - mocker.patch( - target=f"{path_controller}.DashboardsController.switch", - return_value=["quit"], - ) - result_menu = dashboards_controller.DashboardsController(queue=queue).menu() - - assert result_menu == expected - - -@pytest.mark.vcr(record_mode="none") -def test_menu_without_queue_completion(mocker): - path_controller = "gamestonk_terminal.jupyter.dashboards.dashboards_controller" - - # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU - mocker.patch( - target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="gamestonk_terminal.parent_classes.session", - ) - mocker.patch( - target="gamestonk_terminal.parent_classes.session.prompt", - return_value="quit", - ) - - # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER - mocker.patch.object( - target=dashboards_controller.gtff, - attribute="USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target=f"{path_controller}.session", - ) - mocker.patch( - target=f"{path_controller}.session.prompt", - return_value="quit", - ) - - result_menu = dashboards_controller.DashboardsController(queue=None).menu() - - assert result_menu == [] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.record_stdout -def test_print_help(): - controller = dashboards_controller.DashboardsController(queue=None) - controller.print_help() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "an_input, expected_queue", - [ - ("", []), - ("/help", ["home", "help"]), - ("help/help", ["help", "help"]), - ("q", ["quit"]), - ("h", []), - ( - "r", - ["quit", "quit", "reset", "jupyter", "dashboards"], - ), - ], -) -def test_switch(an_input, expected_queue): - controller = dashboards_controller.DashboardsController(queue=None) - queue = controller.switch(an_input=an_input) - - assert queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -def test_call_cls(mocker): - mocker.patch("os.system") - - controller = dashboards_controller.DashboardsController(queue=None) - controller.call_cls([]) - - assert controller.queue == [] - os.system.assert_called_once_with("cls||clear") - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func, queue, expected_queue", - [ - ( - "call_exit", - [], - ["quit", "quit", "quit"], - ), - ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), - ("call_home", [], ["quit", "quit"]), - ("call_help", [], []), - ("call_quit", [], ["quit"]), - ("call_quit", ["help"], ["quit", "help"]), - ( - "call_reset", - [], - ["quit", "quit", "reset", "jupyter", "dashboards"], - ), - ( - "call_reset", - ["help"], - [ - "quit", - "quit", - "reset", - "jupyter", - "dashboards", - "help", - ], - ), - ], -) -def test_call_func_expect_queue(expected_queue, func, queue): - controller = dashboards_controller.DashboardsController(queue=queue) - result = getattr(controller, func)([]) - - assert result is None - assert controller.queue == expected_queue diff --git a/tests/gamestonk_terminal/jupyter/dashboards/test_dashboards_controller.py b/tests/gamestonk_terminal/jupyter/dashboards/test_dashboards_controller.py index e69de29bb2d..237d5f93ece 100644 --- a/tests/gamestonk_terminal/jupyter/dashboards/test_dashboards_controller.py +++ b/tests/gamestonk_terminal/jupyter/dashboards/test_dashboards_controller.py @@ -0,0 +1,143 @@ +import os + +import pytest + +from gamestonk_terminal.jupyter.dashboards import dashboards_controller + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "queue, expected", + [ + (["load", "help"], []), + (["quit", "help"], ["help"]), + ], +) +def test_menu_with_queue(expected, mocker, queue): + path_controller = "gamestonk_terminal.jupyter.dashboards.dashboards_controller" + + # MOCK SWITCH + mocker.patch( + target=f"{path_controller}.DashboardsController.switch", + return_value=["quit"], + ) + result_menu = dashboards_controller.DashboardsController(queue=queue).menu() + + assert result_menu == expected + + +@pytest.mark.vcr(record_mode="none") +def test_menu_without_queue_completion(mocker): + path_controller = "gamestonk_terminal.jupyter.dashboards.dashboards_controller" + + # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU + mocker.patch( + target="gamestonk_terminal.feature_flags.USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target="gamestonk_terminal.parent_classes.session", + ) + mocker.patch( + target="gamestonk_terminal.parent_classes.session.prompt", + return_value="quit", + ) + + # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER + mocker.patch.object( + target=dashboards_controller.gtff, + attribute="USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target=f"{path_controller}.session", + ) + mocker.patch( + target=f"{path_controller}.session.prompt", + return_value="quit", + ) + + result_menu = dashboards_controller.DashboardsController(queue=None).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.record_stdout +def test_print_help(): + controller = dashboards_controller.DashboardsController(queue=None) + controller.print_help() + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "an_input, expected_queue", + [ + ("", []), + ("/help", ["home", "help"]), + ("help/help", ["help", "help"]), + ("q", ["quit"]), + ("h", []), + ( + "r", + ["quit", "quit", "reset", "jupyter", "dashboards"], + ), + ], +) +def test_switch(an_input, expected_queue): + controller = dashboards_controller.DashboardsController(queue=None) + queue = controller.switch(an_input=an_input) + + assert queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +def test_call_cls(mocker): + mocker.patch("os.system") + + controller = dashboards_controller.DashboardsController(queue=None) + controller.call_cls([]) + + assert controller.queue == [] + os.system.assert_called_once_with("cls||clear") + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "func, queue, expected_queue", + [ + ( + "call_exit", + [], + ["quit", "quit", "quit"], + ), + ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), + ("call_home", [], ["quit", "quit"]), + ("call_help", [], []), + ("call_quit", [], ["quit"]), + ("call_quit", ["help"], ["quit", "help"]), + ( + "call_reset", + [], + ["quit", "quit", "reset", "jupyter", "dashboards"], + ), + ( + "call_reset", + ["help"], + [ + "quit", + "quit", + "reset", + "jupyter", + "dashboards", + "help", + ], + ), + ], +) +def test_call_func_expect_queue(expected_queue, func, queue): + controller = dashboards_controller.DashboardsController(queue=queue) + result = getattr(controller, func)([]) + + assert result is None + assert controller.queue == expected_queue diff --git a/tests/gamestonk_terminal/jupyter/dashboards/txt/test_dashboard_controller/test_print_help.txt b/tests/gamestonk_terminal/jupyter/dashboards/txt/test_dashboards_controller/test_print_help.txt similarity index 78% rename from tests/gamestonk_terminal/jupyter/dashboards/txt/test_dashboard_controller/test_print_help.txt rename to tests/gamestonk_terminal/jupyter/dashboards/txt/test_dashboards_controller/test_print_help.txt index 6cdb205ec41..462da2bae0e 100644 --- a/tests/gamestonk_terminal/jupyter/dashboards/txt/test_dashboard_controller/test_print_help.txt +++ b/tests/gamestonk_terminal/jupyter/dashboards/txt/test_dashboards_controller/test_print_help.txt @@ -4,4 +4,5 @@ vsurf options volatility surface chains options chain analysis shortdata finra shortdata analysis + crypto cryptocurrency exchange rates against USD diff --git a/tests/gamestonk_terminal/jupyter/test_jupter_controller.py b/tests/gamestonk_terminal/jupyter/test_jupyter_controller.py similarity index 100% rename from tests/gamestonk_terminal/jupyter/test_jupter_controller.py rename to tests/gamestonk_terminal/jupyter/test_jupyter_controller.py diff --git a/tests/gamestonk_terminal/jupyter/txt/test_jupter_controller/test_print_help.txt b/tests/gamestonk_terminal/jupyter/txt/test_jupyter_controller/test_print_help.txt similarity index 100% rename from tests/gamestonk_terminal/jupyter/txt/test_jupter_controller/test_print_help.txt rename to tests/gamestonk_terminal/jupyter/txt/test_jupyter_controller/test_print_help.txt diff --git a/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_controller.py b/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_controller.py index 85b66e2d2b1..133c4222e88 100644 --- a/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_controller.py +++ b/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_controller.py @@ -213,7 +213,8 @@ def test_call_func_expect_queue(expected_queue, func, queue): "call_create", [ "--action=buy", - "--symbol=MOCK_SYMBOL" "--price=1", + "--symbol=MOCK_SYMBOL", + "--price=1", "--size=2", ], "DegiroView.create", diff --git a/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_model.py b/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_model.py index f782a490e0b..1a223623703 100644 --- a/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_model.py +++ b/tests/gamestonk_terminal/portfolio/brokers/degiro/test_degiro_model.py @@ -255,7 +255,7 @@ def test_login(mocker): new=mock_get_client_details, ) - model.login(credentials=trading_pb2.Credentials()) + model.login() mock_connect.assert_called_once() mock_get_client_details.assert_called_once() diff --git a/tests/gamestonk_terminal/stocks/comparison_analysis/txt/test_ca_controller/test_print_help.txt b/tests/gamestonk_terminal/stocks/comparison_analysis/txt/test_ca_controller/test_print_help.txt index 7e61d264b03..7cd95fd0b37 100644 --- a/tests/gamestonk_terminal/stocks/comparison_analysis/txt/test_ca_controller/test_print_help.txt +++ b/tests/gamestonk_terminal/stocks/comparison_analysis/txt/test_ca_controller/test_print_help.txt @@ -8,7 +8,6 @@ Ticker to get similar companies from: getfinnhub get similar stocks from finnhub API getfinviz get similar stocks from finviz API[/unvl] - set reset and set similar companies add add more similar companies rmv remove similar companies individually or all diff --git a/tests/gamestonk_terminal/stocks/options/cassettes/test_barchart_view/test_print_options_data.yaml b/tests/gamestonk_terminal/stocks/options/cassettes/test_barchart_view/test_print_options_data.yaml index 414f577e8f4..ae4cbbdd756 100644 --- a/tests/gamestonk_terminal/stocks/options/cassettes/test_barchart_view/test_print_options_data.yaml +++ b/tests/gamestonk_terminal/stocks/options/cassettes/test_barchart_view/test_print_options_data.yaml @@ -13,1131 +13,1153 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA+y963rbRrIo+vvkKTrMZCTvCLyKujiWsnXxRRPL0oiynUxWPi+QaJKIQIDBRTIz - k+/bD3LOy+0nOVXV3bgRIEEKoD1rnLVGJhqNqurq6uqq6uruZ18bzsCfTTkb+xPrmD3Df5jp84k3 - cKAUf+Hro9rY96dPGw1vMOYTve64o8Z73r/WR7zGLN0eHdW4XcPvuW7APxPu62ww1l2P+0e1t7cv - tIMaaxwz+cbWJwDy3uQPU8f1a2zg2D63oeaDafjjI4PfmwOu0cMOM23TN3VL8wa6xY9a9eYOm+gf - 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https://www.gstatic.com https://pagead2.googlesyndication.com - https://www.googletagmanager.com 'nonce-8dd942688765d421e0ff28ff6fb6f53f' + https://www.googletagmanager.com 'nonce-e3c882e46dcead9cc76a63419b65429c' Content-Type: - text/html; charset=UTF-8 Date: - - Thu, 10 Feb 2022 20:29:23 GMT + - Tue, 15 Mar 2022 14:39:38 GMT Expires: - '0' Pragma: diff --git a/tests/gamestonk_terminal/stocks/options/test_chartexchange_view.py b/tests/gamestonk_terminal/stocks/options/test_chartexchange_view.py index 012c775ea50..72d7b55b711 100644 --- a/tests/gamestonk_terminal/stocks/options/test_chartexchange_view.py +++ b/tests/gamestonk_terminal/stocks/options/test_chartexchange_view.py @@ -30,6 +30,11 @@ def test_display_raw(mocker, tab): new=tab, ) + # MOCK VISUALIZE_OUTPUT + mocker.patch( + target="gamestonk_terminal.helper_classes.TerminalStyle.visualize_output" + ) + chartexchange_view.display_raw( export="", ticker="GME", diff --git a/tests/gamestonk_terminal/stocks/options/txt/test_barchart_view/test_print_options_data.txt b/tests/gamestonk_terminal/stocks/options/txt/test_barchart_view/test_print_options_data.txt index 73900ed4bf0..e743922c735 100644 --- a/tests/gamestonk_terminal/stocks/options/txt/test_barchart_view/test_print_options_data.txt +++ b/tests/gamestonk_terminal/stocks/options/txt/test_barchart_view/test_print_options_data.txt @@ -1,14 +1,14 @@ 0 1 -0 Implied Volatility 17.57% ( +0.10%) -1 Historical Volatility 14.46% -2 IV Percentile 16% -3 IV Rank 15.63% -4 IV High 35.42% on 01/27/21 +0 Implied Volatility 24.74% ( +0.29%) +1 Historical Volatility 36.78% +2 IV Percentile 87% +3 IV Rank 48.64% +4 IV High 35.81% on 03/07/22 5 IV Low 14.26% on 07/28/21 -6 Put/Call Vol Ratio 0.92 -7 Today's Volume 2,545 -8 Volume Avg (30-Day) 6,827 +6 Put/Call Vol Ratio 0.22 +7 Today's Volume 8,113 +8 Volume Avg (30-Day) 6,820 9 Put/Call OI Ratio 0.89 -10 Today's Open Interest 61,858 -11 Open Int (30-Day) 88,017 +10 Today's Open Interest 99,043 +11 Open Int (30-Day) 101,255 diff --git a/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[False].txt b/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[False].txt index e4f9868440d..59efdab43a6 100644 --- a/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[False].txt +++ b/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[False].txt @@ -1,12 +1,9 @@ - Date Open High Low Close Change Volume Open Interest Change Since -0 2021-02-05 0.57 14.79 0.01 0.03 -0.96000 6580.0 2017.0 0.00000 -1 2021-02-04 13.70 14.00 0.75 0.75 -0.94741 5945.0 2386.0 -0.96000 -2 2021-02-03 40.45 40.45 13.50 14.26 -0.39831 2124.0 1074.0 -0.99790 -3 2021-02-02 64.61 76.20 18.50 23.70 -0.82193 1991.0 555.0 -0.99873 -4 2021-02-01 232.60 232.60 133.09 133.09 -0.27313 107.0 441.0 -0.99977 -5 2021-01-29 232.15 296.20 183.10 183.10 0.40145 37.0 445.0 -0.99984 -6 2021-01-28 225.40 385.55 62.75 130.65 -0.48945 49.0 450.0 -0.99977 -7 2021-01-27 210.20 285.50 70.80 255.90 2.64686 309.0 461.0 -0.99988 -8 2021-01-26 27.50 75.79 21.00 70.17 2.50850 1763.0 572.0 -0.99957 -9 2021-01-25 20.70 85.70 13.90 20.00 0.00000 1834.0 552.0 -0.99850 + + Open High Low Close Change Volume Open Interest Change Since +Date +2021-02-05 0.57 14.79 0.01 0.03 -0.96000 6580.0 2017.0 0.00000 +2021-02-04 13.70 14.00 0.75 0.75 -0.94741 5945.0 2386.0 -0.96000 +2021-02-03 40.45 40.45 13.50 14.26 -0.39831 2124.0 1074.0 -0.99790 +2021-02-02 64.61 76.20 18.50 23.70 -0.82193 1991.0 555.0 -0.99873 +2021-02-01 232.60 232.60 133.09 133.09 -0.27313 107.0 441.0 -0.99977 diff --git a/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[True].txt b/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[True].txt index e4f9868440d..59efdab43a6 100644 --- a/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[True].txt +++ b/tests/gamestonk_terminal/stocks/options/txt/test_chartexchange_view/test_display_raw[True].txt @@ -1,12 +1,9 @@ - Date Open High Low Close Change Volume Open Interest Change Since -0 2021-02-05 0.57 14.79 0.01 0.03 -0.96000 6580.0 2017.0 0.00000 -1 2021-02-04 13.70 14.00 0.75 0.75 -0.94741 5945.0 2386.0 -0.96000 -2 2021-02-03 40.45 40.45 13.50 14.26 -0.39831 2124.0 1074.0 -0.99790 -3 2021-02-02 64.61 76.20 18.50 23.70 -0.82193 1991.0 555.0 -0.99873 -4 2021-02-01 232.60 232.60 133.09 133.09 -0.27313 107.0 441.0 -0.99977 -5 2021-01-29 232.15 296.20 183.10 183.10 0.40145 37.0 445.0 -0.99984 -6 2021-01-28 225.40 385.55 62.75 130.65 -0.48945 49.0 450.0 -0.99977 -7 2021-01-27 210.20 285.50 70.80 255.90 2.64686 309.0 461.0 -0.99988 -8 2021-01-26 27.50 75.79 21.00 70.17 2.50850 1763.0 572.0 -0.99957 -9 2021-01-25 20.70 85.70 13.90 20.00 0.00000 1834.0 552.0 -0.99850 + + Open High Low Close Change Volume Open Interest Change Since +Date +2021-02-05 0.57 14.79 0.01 0.03 -0.96000 6580.0 2017.0 0.00000 +2021-02-04 13.70 14.00 0.75 0.75 -0.94741 5945.0 2386.0 -0.96000 +2021-02-03 40.45 40.45 13.50 14.26 -0.39831 2124.0 1074.0 -0.99790 +2021-02-02 64.61 76.20 18.50 23.70 -0.82193 1991.0 555.0 -0.99873 +2021-02-01 232.60 232.60 133.09 133.09 -0.27313 107.0 441.0 -0.99977 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_industry.yaml b/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_industry.yaml index 14429e13fab..7255de3f127 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_industry.yaml +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_industry.yaml @@ -163,13 +163,13 @@ interactions: Content-Type: - text/plain; charset=utf-8 Date: - - Fri, 28 Jan 2022 01:16:34 GMT + - Sat, 19 Mar 2022 08:34:27 GMT ETag: - W/"466e67a37ae6cf1bb8fc62cac85aeb67bff609b8d47fd6e4a473303e47333d33" Expires: - - Fri, 28 Jan 2022 01:21:34 GMT + - Sat, 19 Mar 2022 08:39:27 GMT Source-Age: - - '285' + - '5' Strict-Transport-Security: - max-age=31536000 Vary: @@ -183,15 +183,15 @@ interactions: X-Content-Type-Options: - nosniff X-Fastly-Request-ID: - - 09c59ecc62abe25cc041f6af9f9470967b6246b2 + - 59d4fbf62a9e05792c6d1063cec0096e702ddfec X-Frame-Options: - deny X-GitHub-Request-Id: - - B274:7295:4349EE:50581E:61F342D4 + - 73F6:E65A:55CB47:5A2372:6235958E X-Served-By: - - cache-cdg20753-CDG + - cache-ams21059-AMS X-Timer: - - S1643332594.130495,VS0,VE0 + - S1647678868.546234,VS0,VE0 X-XSS-Protection: - 1; mode=block 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2022 08:34:28 GMT ETag: - - W/"fdbcc1570d2560b830aeb1ab4645a1c4a0faaf27dc252c8ea8b9fb97f1f74e0a" + - W/"ee1a348c4ccac5d687395b540cef765059a5bdd6dad25d783c7b2c14243132d1" Expires: - - Fri, 28 Jan 2022 01:21:34 GMT + - Sat, 19 Mar 2022 08:39:28 GMT Source-Age: - - '283' + - '0' Strict-Transport-Security: - max-age=31536000 Vary: @@ -503,21 +503,21 @@ interactions: Via: - 1.1 varnish X-Cache: - - HIT + - MISS X-Cache-Hits: - - '1' + - '0' X-Content-Type-Options: - nosniff X-Fastly-Request-ID: - - bec8eee325f0fd7a545a3b851ed8b9ff7deffa89 + - 1bcead1e856b3ba667e776c1c8b2a364d0368740 X-Frame-Options: - deny X-GitHub-Request-Id: - - E2F4:85DB:1A207DE:1B26854:61F342D7 + - 34FA:2B72:13948A9:1489772:62359593 X-Served-By: - - cache-cdg20762-CDG + - cache-ams21054-AMS X-Timer: - - S1643332594.254007,VS0,VE0 + - S1647678868.842176,VS0,VE170 X-XSS-Protection: - 1; mode=block status: @@ -566,13 +566,13 @@ interactions: Content-Type: - text/plain; charset=utf-8 Date: - - Fri, 28 Jan 2022 01:16:34 GMT 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a/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_sector.yaml b/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_sector.yaml index 9caa32230d3..e392d2ae8dd 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_sector.yaml +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/cassettes/test_financedatabase_model/test_get_companies_per_country_in_sector.yaml @@ -35,13 +35,13 @@ interactions: Content-Type: - text/plain; charset=utf-8 Date: - - Fri, 21 Jan 2022 03:56:13 GMT + - Sat, 19 Mar 2022 08:34:26 GMT ETag: - W/"cb6f8d56bb62dfe2136b3c45ad72c8d525221cab357ffcc0aa5c8385a4700c91" Expires: - - Fri, 21 Jan 2022 04:01:13 GMT + - Sat, 19 Mar 2022 08:39:26 GMT Source-Age: - - '114' + - '4' Strict-Transport-Security: - max-age=31536000 Vary: 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s-max-age=1800 Connection: @@ -1092,7 +1092,7 @@ interactions: Content-Type: - text/html; charset=utf-8 Date: - - Mon, 21 Feb 2022 18:31:31 GMT + - Sat, 19 Mar 2022 08:34:50 GMT Expect-CT: - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" Server: @@ -1106,7 +1106,7 @@ interactions: alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 etag: - - W/"5bd8-yuXQFpiyvgiwq/DJcpqY+Y4pcN0" + - W/"5dd5-dcoTWpEUS0+3S/Y862JbSPKPT90" strict-transport-security: - max-age=31536000; includeSubDomains; preload x-frame-options: @@ -1118,7 +1118,7 @@ interactions: x-vercel-cache: - MISS x-vercel-id: - - fra1::iad1::j8fwg-1645468290706-94ffa9350b98 + - fra1::iad1::p9h54-1647678890214-a507c337948f status: code: 404 message: Not Found @@ -1132,157 +1132,157 @@ interactions: Connection: - keep-alive User-Agent: - - Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.10; rv:86.1) Gecko/20100101 Firefox/86.1 + - Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.10; rv:83.0) 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b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_change_type_dataframes.csv @@ -1,3 +1,3 @@ -,2010,2011,2012 -Item 1,10.0,1.0,10.0 -Item 2,20.0,2000.0, +,2010,2011,2012 +Item 1,10.0,1.0,10.0 +Item 2,20.0,2000.0, diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks0-re-sa_keys0-annual-IS].csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks0-re-sa_keys0-annual-IS].csv index 627339401e3..7839ab2e05d 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks0-re-sa_keys0-annual-IS].csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks0-re-sa_keys0-annual-IS].csv @@ -1,13 +1,13 @@ -0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 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+Revenue,156508000000.0,170910000000.0,182795000000.0,233715000000.0,215639000000.0,229234000000.0,265595000000.0,260174000000.0,274515000000.0,365817000000.0 +Cost of Revenue,87846000000.0,106606000000.0,112258000000.0,140089000000.0,131376000000.0,141048000000.0,163756000000.0,161782000000.0,169559000000.0,212981000000.0 +Gross Profit,68662000000.0,64304000000.0,70537000000.0,93626000000.0,84263000000.0,88186000000.0,101839000000.0,98392000000.0,104956000000.0,152836000000.0 +"Selling, General & Admin",10040000000.0,10830000000.0,11993000000.0,14329000000.0,14194000000.0,15261000000.0,16705000000.0,18245000000.0,19916000000.0,21973000000.0 +Research & Development,3381000000.0,4475000000.0,6041000000.0,8067000000.0,10045000000.0,11581000000.0,14236000000.0,16217000000.0,18752000000.0,21914000000.0 +Other Operating Expenses,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Operating Income,55241000000.0,48999000000.0,52503000000.0,71230000000.0,60024000000.0,61344000000.0,70898000000.0,63930000000.0,66288000000.0,108949000000.0 +Interest Expense / Income,0.0,136000000.0,384000000.0,733000000.0,1456000000.0,2323000000.0,3240000000.0,3576000000.0,2873000000.0,2645000000.0 +Other Expense / Income,-522000000.0,-1292000000.0,-1364000000.0,-2018000000.0,-2804000000.0,-5068000000.0,-5245000000.0,-5383000000.0,-3676000000.0,-2903000000.0 +Income Tax,14030000000.0,13118000000.0,13973000000.0,19121000000.0,15685000000.0,15738000000.0,13372000000.0,10481000000.0,9680000000.0,14527000000.0 +Net Income,41733000000.0,37037000000.0,39510000000.0,53394000000.0,45687000000.0,48351000000.0,59531000000.0,55256000000.0,57411000000.0,94680000000.0 +Preferred Dividends,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks1-rec-sa_keys1-quarterly-BS].csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks1-rec-sa_keys1-quarterly-BS].csv index f46fa5103ec..2f37ff8b2ef 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks1-rec-sa_keys1-quarterly-BS].csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks1-rec-sa_keys1-quarterly-BS].csv @@ -1,28 +1,28 @@ -0,2012-09-29,2012-12-29,2013-03-30,2013-06-29,2013-09-28,2013-12-28,2014-03-29,2014-06-28,2014-09-27,2014-12-27,2015-03-28,2015-06-27,2015-09-26,2015-12-26,2016-03-26,2016-06-25,2016-09-24,2016-12-31,2017-04-01,2017-07-01,2017-09-30,2017-12-30,2018-03-31,2018-06-30,2018-09-29,2018-12-29,2019-03-30,2019-06-29,2019-09-28,2019-12-28,2020-03-28,2020-06-27,2020-09-26,2020-12-26,2021-03-27,2021-06-26,2021-09-25,2021-12-25 -Cash & Equivalents,10746000000.0,16154000000.0,12053000000.0,11248000000.0,14259000000.0,14077000000.0,18949000000.0,12977000000.0,13844000000.0,19478000000.0,14489000000.0,15319000000.0,21120000000.0,16689000000.0,21514000000.0,18237000000.0,20484000000.0,16371000000.0,15157000000.0,18571000000.0,20289000000.0,27491000000.0,45059000000.0,31971000000.0,25913000000.0,44771000000.0,37988000000.0,50530000000.0,48844000000.0,39771000000.0,40174000000.0,33383000000.0,38016000000.0,36010000000.0,38466000000.0,34050000000.0,34940000000.0,37119000000.0 -Short-Term Investments,18383000000.0,23666000000.0,27084000000.0,31358000000.0,26287000000.0,26634000000.0,22401000000.0,24828000000.0,11233000000.0,12985000000.0,18607000000.0,19384000000.0,20481000000.0,21385000000.0,33769000000.0,43519000000.0,46671000000.0,44081000000.0,51944000000.0,58188000000.0,53892000000.0,49662000000.0,42881000000.0,38999000000.0,40388000000.0,41656000000.0,42104000000.0,44084000000.0,51713000000.0,67391000000.0,53877000000.0,59642000000.0,52927000000.0,40816000000.0,31368000000.0,27646000000.0,27699000000.0,26794000000.0 -Cash & Cash Equivalents,29129000000.0,39820000000.0,39137000000.0,42606000000.0,40546000000.0,40711000000.0,41350000000.0,37805000000.0,25077000000.0,32463000000.0,33096000000.0,34703000000.0,41601000000.0,38074000000.0,55283000000.0,61756000000.0,67155000000.0,60452000000.0,67101000000.0,76759000000.0,74181000000.0,77153000000.0,87940000000.0,70970000000.0,66301000000.0,86427000000.0,80092000000.0,94614000000.0,100557000000.0,107162000000.0,94051000000.0,93025000000.0,90943000000.0,76826000000.0,69834000000.0,61696000000.0,62639000000.0,63913000000.0 -Receivables,18692000000.0,21534000000.0,13336000000.0,13453000000.0,20641000000.0,25198000000.0,15820000000.0,16841000000.0,27219000000.0,29976000000.0,18164000000.0,19907000000.0,30343000000.0,24621000000.0,19824000000.0,19042000000.0,29299000000.0,27977000000.0,20612000000.0,22632000000.0,35673000000.0,50899000000.0,22408000000.0,26367000000.0,48995000000.0,36981000000.0,26278000000.0,26474000000.0,45804000000.0,39946000000.0,30677000000.0,32075000000.0,37445000000.0,58620000000.0,33036000000.0,33908000000.0,51506000000.0,65253000000.0 -Inventory,791000000.0,1455000000.0,1245000000.0,1697000000.0,1764000000.0,2122000000.0,1829000000.0,1594000000.0,2111000000.0,2283000000.0,2396000000.0,2042000000.0,2349000000.0,2451000000.0,2281000000.0,1831000000.0,2132000000.0,2712000000.0,2910000000.0,3146000000.0,4855000000.0,4421000000.0,7662000000.0,5936000000.0,3956000000.0,4988000000.0,4884000000.0,3355000000.0,4106000000.0,4097000000.0,3334000000.0,3978000000.0,4061000000.0,4973000000.0,5219000000.0,5178000000.0,6580000000.0,5876000000.0 -Other Current Assets,9041000000.0,9539000000.0,9619000000.0,10463000000.0,10335000000.0,12316000000.0,11542000000.0,11709000000.0,14124000000.0,18681000000.0,14235000000.0,14301000000.0,15085000000.0,11073000000.0,10204000000.0,11132000000.0,8283000000.0,12191000000.0,11367000000.0,10338000000.0,13936000000.0,11337000000.0,12043000000.0,12488000000.0,12087000000.0,12432000000.0,12092000000.0,10530000000.0,12352000000.0,12026000000.0,15691000000.0,10987000000.0,11264000000.0,13687000000.0,13376000000.0,13641000000.0,14111000000.0,18112000000.0 -Total Current Assets,57653000000.0,72348000000.0,63337000000.0,68219000000.0,73286000000.0,80347000000.0,70541000000.0,67949000000.0,68531000000.0,83403000000.0,67891000000.0,70953000000.0,89378000000.0,76219000000.0,87592000000.0,93761000000.0,106869000000.0,103332000000.0,101990000000.0,112875000000.0,128645000000.0,143810000000.0,130053000000.0,115761000000.0,131339000000.0,140828000000.0,123346000000.0,134973000000.0,162819000000.0,163231000000.0,143753000000.0,140065000000.0,143713000000.0,154106000000.0,121465000000.0,114423000000.0,134836000000.0,153154000000.0 -"Property, Plant & Equipment",15452000000.0,15422000000.0,15026000000.0,16327000000.0,16597000000.0,15488000000.0,15120000000.0,17585000000.0,20624000000.0,20392000000.0,20151000000.0,21149000000.0,22471000000.0,22300000000.0,23203000000.0,25448000000.0,27010000000.0,26510000000.0,27163000000.0,29286000000.0,33783000000.0,33679000000.0,35077000000.0,38117000000.0,41304000000.0,39597000000.0,38746000000.0,37636000000.0,37378000000.0,37031000000.0,35889000000.0,35687000000.0,36766000000.0,37933000000.0,37815000000.0,38615000000.0,39440000000.0,39245000000.0 -Long-Term Investments,92122000000.0,97292000000.0,105550000000.0,104014000000.0,106215000000.0,118131000000.0,109239000000.0,126685000000.0,130162000000.0,145492000000.0,160443000000.0,168145000000.0,164065000000.0,177665000000.0,177645000000.0,169764000000.0,170430000000.0,185638000000.0,189740000000.0,184757000000.0,194714000000.0,207944000000.0,179286000000.0,172773000000.0,170799000000.0,158608000000.0,145319000000.0,115996000000.0,105341000000.0,99899000000.0,98793000000.0,100592000000.0,100887000000.0,118745000000.0,134539000000.0,131948000000.0,127877000000.0,138683000000.0 -Goodwill and Intangibles,5359000000.0,5843000000.0,5536000000.0,5875000000.0,5756000000.0,6127000000.0,5983000000.0,6141000000.0,8758000000.0,8999000000.0,8772000000.0,8823000000.0,9009000000.0,9126000000.0,9092000000.0,8767000000.0,8620000000.0,8271000000.0,8090000000.0,8105000000.0,0.0,8038000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Other Long-Term Assets,5478000000.0,5183000000.0,5294000000.0,5421000000.0,5146000000.0,5091000000.0,5106000000.0,4160000000.0,3764000000.0,3608000000.0,3937000000.0,4081000000.0,5422000000.0,7974000000.0,7745000000.0,7862000000.0,8757000000.0,7390000000.0,7549000000.0,10150000000.0,18177000000.0,13323000000.0,23086000000.0,22546000000.0,22283000000.0,34686000000.0,34587000000.0,33634000000.0,32978000000.0,40457000000.0,41965000000.0,41000000000.0,42522000000.0,43270000000.0,43339000000.0,44854000000.0,48849000000.0,50109000000.0 -Total Long-Term Assets,118411000000.0,123740000000.0,131406000000.0,131637000000.0,133714000000.0,144837000000.0,135448000000.0,154571000000.0,163308000000.0,178491000000.0,193303000000.0,202198000000.0,200967000000.0,217065000000.0,217685000000.0,211841000000.0,214817000000.0,227809000000.0,232542000000.0,232298000000.0,246674000000.0,262984000000.0,237449000000.0,233436000000.0,234386000000.0,232891000000.0,218652000000.0,187266000000.0,175697000000.0,177387000000.0,176647000000.0,177279000000.0,180175000000.0,199948000000.0,215693000000.0,215417000000.0,216166000000.0,228037000000.0 -Total Assets,176064000000.0,196088000000.0,194743000000.0,199856000000.0,207000000000.0,225184000000.0,205989000000.0,222520000000.0,231839000000.0,261894000000.0,261194000000.0,273151000000.0,290345000000.0,293284000000.0,305277000000.0,305602000000.0,321686000000.0,331141000000.0,334532000000.0,345173000000.0,375319000000.0,406794000000.0,367502000000.0,349197000000.0,365725000000.0,373719000000.0,341998000000.0,322239000000.0,338516000000.0,340618000000.0,320400000000.0,317344000000.0,323888000000.0,354054000000.0,337158000000.0,329840000000.0,351002000000.0,381191000000.0 -Accounts Payable,21175000000.0,26398000000.0,14912000000.0,15516000000.0,22367000000.0,29588000000.0,18914000000.0,20535000000.0,30196000000.0,38001000000.0,23159000000.0,26474000000.0,35490000000.0,33312000000.0,25098000000.0,26318000000.0,37294000000.0,38510000000.0,28573000000.0,31915000000.0,44242000000.0,62985000000.0,34311000000.0,38489000000.0,55888000000.0,44293000000.0,30443000000.0,29115000000.0,46236000000.0,45111000000.0,32421000000.0,35325000000.0,42296000000.0,63846000000.0,40127000000.0,40409000000.0,54763000000.0,74362000000.0 -Deferred Revenue,8601000000.0,10212000000.0,10142000000.0,10005000000.0,10060000000.0,11428000000.0,11474000000.0,11454000000.0,11522000000.0,12467000000.0,12515000000.0,12562000000.0,12564000000.0,12535000000.0,12783000000.0,11416000000.0,11010000000.0,11052000000.0,10789000000.0,10592000000.0,10384000000.0,11175000000.0,10862000000.0,10281000000.0,5966000000.0,5546000000.0,5532000000.0,5434000000.0,5522000000.0,5573000000.0,5928000000.0,6313000000.0,6643000000.0,7395000000.0,7595000000.0,7681000000.0,7612000000.0,7876000000.0 -Current Debt,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2010000000.0,6308000000.0,3899000000.0,3799000000.0,6999000000.0,10999000000.0,9759000000.0,10498000000.0,15996000000.0,11605000000.0,13992000000.0,13991000000.0,18475000000.0,18473000000.0,18478000000.0,20478000000.0,17472000000.0,20748000000.0,21741000000.0,22429000000.0,23482000000.0,16240000000.0,15214000000.0,20421000000.0,18675000000.0,13769000000.0,12762000000.0,13003000000.0,16039000000.0,15613000000.0,16169000000.0 -Other Current Liabilities,8766000000.0,10269000000.0,10454000000.0,10798000000.0,11231000000.0,12753000000.0,12820000000.0,12206000000.0,15422000000.0,19244000000.0,19256000000.0,19250000000.0,21557000000.0,20486000000.0,19886000000.0,17756000000.0,19097000000.0,20576000000.0,19989000000.0,20320000000.0,27715000000.0,23150000000.0,23669000000.0,22306000000.0,33327000000.0,36703000000.0,35368000000.0,31673000000.0,37720000000.0,36263000000.0,37324000000.0,35005000000.0,42684000000.0,48504000000.0,45660000000.0,43625000000.0,47493000000.0,49167000000.0 -Total Current Liabilities,38542000000.0,46879000000.0,35508000000.0,36319000000.0,43658000000.0,53769000000.0,43208000000.0,46205000000.0,63448000000.0,73611000000.0,58729000000.0,65285000000.0,80610000000.0,76092000000.0,68265000000.0,71486000000.0,79006000000.0,84130000000.0,73342000000.0,81302000000.0,100814000000.0,115788000000.0,89320000000.0,88548000000.0,115929000000.0,108283000000.0,93772000000.0,89704000000.0,105718000000.0,102161000000.0,96094000000.0,95318000000.0,105392000000.0,132507000000.0,106385000000.0,107754000000.0,125481000000.0,147574000000.0 -Long-Term Debt,0.0,0.0,0.0,16958000000.0,16960000000.0,16961000000.0,16962000000.0,29030000000.0,28987000000.0,32504000000.0,40072000000.0,47419000000.0,53329000000.0,53204000000.0,69374000000.0,68939000000.0,75427000000.0,73557000000.0,84531000000.0,89864000000.0,97207000000.0,103922000000.0,101362000000.0,97128000000.0,93735000000.0,92989000000.0,90201000000.0,84936000000.0,91807000000.0,93078000000.0,89086000000.0,94048000000.0,98667000000.0,99281000000.0,108642000000.0,105752000000.0,109106000000.0,106629000000.0 -Other Long-Term Liabilities,19312000000.0,21863000000.0,23745000000.0,23225000000.0,22833000000.0,24770000000.0,25640000000.0,26345000000.0,27857000000.0,32451000000.0,33387000000.0,34770000000.0,37051000000.0,35721000000.0,37181000000.0,38636000000.0,39004000000.0,41064000000.0,42577000000.0,41582000000.0,43251000000.0,46885000000.0,49942000000.0,48572000000.0,48914000000.0,54555000000.0,52165000000.0,51143000000.0,50503000000.0,55848000000.0,56795000000.0,55696000000.0,54490000000.0,56042000000.0,52953000000.0,52054000000.0,53325000000.0,55056000000.0 -Total Long-Term Liabilities,19312000000.0,21863000000.0,23745000000.0,40183000000.0,39793000000.0,41731000000.0,42602000000.0,55375000000.0,56844000000.0,64955000000.0,73459000000.0,82189000000.0,90380000000.0,88925000000.0,106555000000.0,107575000000.0,114431000000.0,114621000000.0,127108000000.0,131446000000.0,140458000000.0,150807000000.0,151304000000.0,145700000000.0,142649000000.0,147544000000.0,142366000000.0,136079000000.0,142310000000.0,148926000000.0,145881000000.0,149744000000.0,153157000000.0,155323000000.0,161595000000.0,157806000000.0,162431000000.0,161685000000.0 -Total Liabilities,57854000000.0,68742000000.0,59253000000.0,76502000000.0,83451000000.0,95500000000.0,85810000000.0,101580000000.0,120292000000.0,138566000000.0,132188000000.0,147474000000.0,170990000000.0,165017000000.0,174820000000.0,179061000000.0,193437000000.0,198751000000.0,200450000000.0,212748000000.0,241272000000.0,266595000000.0,240624000000.0,234248000000.0,258578000000.0,255827000000.0,236138000000.0,225783000000.0,248028000000.0,251087000000.0,241975000000.0,245062000000.0,258549000000.0,287830000000.0,267980000000.0,265560000000.0,287912000000.0,309259000000.0 -Common Stock,16422000000.0,17167000000.0,17954000000.0,19024000000.0,19764000000.0,20559000000.0,21496000000.0,22139000000.0,23313000000.0,24187000000.0,25376000000.0,26327000000.0,27416000000.0,28253000000.0,29484000000.0,30106000000.0,31251000000.0,32144000000.0,33579000000.0,34445000000.0,35867000000.0,36447000000.0,38044000000.0,38624000000.0,40201000000.0,40970000000.0,42801000000.0,43371000000.0,45174000000.0,45972000000.0,48032000000.0,48696000000.0,50779000000.0,51744000000.0,54203000000.0,54989000000.0,57365000000.0,58424000000.0 -Retained Earnings,101289000000.0,109567000000.0,116572000000.0,104564000000.0,104256000000.0,109431000000.0,98934000000.0,98715000000.0,87152000000.0,97178000000.0,100920000000.0,98252000000.0,92284000000.0,101494000000.0,102021000000.0,96542000000.0,96364000000.0,100001000000.0,100925000000.0,98525000000.0,98330000000.0,104593000000.0,91898000000.0,79436000000.0,70400000000.0,80510000000.0,64558000000.0,53724000000.0,45898000000.0,43977000000.0,33182000000.0,24136000000.0,14966000000.0,14301000000.0,15261000000.0,9233000000.0,5562000000.0,14435000000.0 -Comprehensive Income,499000000.0,612000000.0,964000000.0,-234000000.0,-471000000.0,-306000000.0,-251000000.0,86000000.0,1082000000.0,1963000000.0,2710000000.0,1098000000.0,-345000000.0,-1480000000.0,-1048000000.0,-107000000.0,634000000.0,245000000.0,-422000000.0,-545000000.0,-150000000.0,-841000000.0,-3064000000.0,-3111000000.0,-3454000000.0,-3588000000.0,-1499000000.0,-639000000.0,-584000000.0,-418000000.0,-2789000000.0,-550000000.0,-406000000.0,179000000.0,-286000000.0,58000000.0,163000000.0,-927000000.0 -Shareholders' Equity,118210000000.0,127346000000.0,135490000000.0,123354000000.0,123549000000.0,129684000000.0,120179000000.0,120940000000.0,111547000000.0,123328000000.0,129006000000.0,125677000000.0,119355000000.0,128267000000.0,130457000000.0,126541000000.0,128249000000.0,132390000000.0,134082000000.0,132425000000.0,134047000000.0,140199000000.0,126878000000.0,114949000000.0,107147000000.0,117892000000.0,105860000000.0,96456000000.0,90488000000.0,89531000000.0,78425000000.0,72282000000.0,65339000000.0,66224000000.0,69178000000.0,64280000000.0,63090000000.0,71932000000.0 -Total Liabilities and Equity,176064000000.0,196088000000.0,194743000000.0,199856000000.0,207000000000.0,225184000000.0,205989000000.0,222520000000.0,231839000000.0,261894000000.0,261194000000.0,273151000000.0,290345000000.0,293284000000.0,305277000000.0,305602000000.0,321686000000.0,331141000000.0,334532000000.0,345173000000.0,375319000000.0,406794000000.0,367502000000.0,349197000000.0,365725000000.0,373719000000.0,341998000000.0,322239000000.0,338516000000.0,340618000000.0,320400000000.0,317344000000.0,323888000000.0,354054000000.0,337158000000.0,329840000000.0,351002000000.0,381191000000.0 +0,2012-09-29,2012-12-29,2013-03-30,2013-06-29,2013-09-28,2013-12-28,2014-03-29,2014-06-28,2014-09-27,2014-12-27,2015-03-28,2015-06-27,2015-09-26,2015-12-26,2016-03-26,2016-06-25,2016-09-24,2016-12-31,2017-04-01,2017-07-01,2017-09-30,2017-12-30,2018-03-31,2018-06-30,2018-09-29,2018-12-29,2019-03-30,2019-06-29,2019-09-28,2019-12-28,2020-03-28,2020-06-27,2020-09-26,2020-12-26,2021-03-27,2021-06-26,2021-09-25,2021-12-25 +Cash & Equivalents,10746000000.0,16154000000.0,12053000000.0,11248000000.0,14259000000.0,14077000000.0,18949000000.0,12977000000.0,13844000000.0,19478000000.0,14489000000.0,15319000000.0,21120000000.0,16689000000.0,21514000000.0,18237000000.0,20484000000.0,16371000000.0,15157000000.0,18571000000.0,20289000000.0,27491000000.0,45059000000.0,31971000000.0,25913000000.0,44771000000.0,37988000000.0,50530000000.0,48844000000.0,39771000000.0,40174000000.0,33383000000.0,38016000000.0,36010000000.0,38466000000.0,34050000000.0,34940000000.0,37119000000.0 +Short-Term Investments,18383000000.0,23666000000.0,27084000000.0,31358000000.0,26287000000.0,26634000000.0,22401000000.0,24828000000.0,11233000000.0,12985000000.0,18607000000.0,19384000000.0,20481000000.0,21385000000.0,33769000000.0,43519000000.0,46671000000.0,44081000000.0,51944000000.0,58188000000.0,53892000000.0,49662000000.0,42881000000.0,38999000000.0,40388000000.0,41656000000.0,42104000000.0,44084000000.0,51713000000.0,67391000000.0,53877000000.0,59642000000.0,52927000000.0,40816000000.0,31368000000.0,27646000000.0,27699000000.0,26794000000.0 +Cash & Cash Equivalents,29129000000.0,39820000000.0,39137000000.0,42606000000.0,40546000000.0,40711000000.0,41350000000.0,37805000000.0,25077000000.0,32463000000.0,33096000000.0,34703000000.0,41601000000.0,38074000000.0,55283000000.0,61756000000.0,67155000000.0,60452000000.0,67101000000.0,76759000000.0,74181000000.0,77153000000.0,87940000000.0,70970000000.0,66301000000.0,86427000000.0,80092000000.0,94614000000.0,100557000000.0,107162000000.0,94051000000.0,93025000000.0,90943000000.0,76826000000.0,69834000000.0,61696000000.0,62639000000.0,63913000000.0 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Assets,9041000000.0,9539000000.0,9619000000.0,10463000000.0,10335000000.0,12316000000.0,11542000000.0,11709000000.0,14124000000.0,18681000000.0,14235000000.0,14301000000.0,15085000000.0,11073000000.0,10204000000.0,11132000000.0,8283000000.0,12191000000.0,11367000000.0,10338000000.0,13936000000.0,11337000000.0,12043000000.0,12488000000.0,12087000000.0,12432000000.0,12092000000.0,10530000000.0,12352000000.0,12026000000.0,15691000000.0,10987000000.0,11264000000.0,13687000000.0,13376000000.0,13641000000.0,14111000000.0,18112000000.0 +Total Current Assets,57653000000.0,72348000000.0,63337000000.0,68219000000.0,73286000000.0,80347000000.0,70541000000.0,67949000000.0,68531000000.0,83403000000.0,67891000000.0,70953000000.0,89378000000.0,76219000000.0,87592000000.0,93761000000.0,106869000000.0,103332000000.0,101990000000.0,112875000000.0,128645000000.0,143810000000.0,130053000000.0,115761000000.0,131339000000.0,140828000000.0,123346000000.0,134973000000.0,162819000000.0,163231000000.0,143753000000.0,140065000000.0,143713000000.0,154106000000.0,121465000000.0,114423000000.0,134836000000.0,153154000000.0 +"Property, Plant & Equipment",15452000000.0,15422000000.0,15026000000.0,16327000000.0,16597000000.0,15488000000.0,15120000000.0,17585000000.0,20624000000.0,20392000000.0,20151000000.0,21149000000.0,22471000000.0,22300000000.0,23203000000.0,25448000000.0,27010000000.0,26510000000.0,27163000000.0,29286000000.0,33783000000.0,33679000000.0,35077000000.0,38117000000.0,41304000000.0,39597000000.0,38746000000.0,37636000000.0,37378000000.0,37031000000.0,35889000000.0,35687000000.0,36766000000.0,37933000000.0,37815000000.0,38615000000.0,39440000000.0,39245000000.0 +Long-Term Investments,92122000000.0,97292000000.0,105550000000.0,104014000000.0,106215000000.0,118131000000.0,109239000000.0,126685000000.0,130162000000.0,145492000000.0,160443000000.0,168145000000.0,164065000000.0,177665000000.0,177645000000.0,169764000000.0,170430000000.0,185638000000.0,189740000000.0,184757000000.0,194714000000.0,207944000000.0,179286000000.0,172773000000.0,170799000000.0,158608000000.0,145319000000.0,115996000000.0,105341000000.0,99899000000.0,98793000000.0,100592000000.0,100887000000.0,118745000000.0,134539000000.0,131948000000.0,127877000000.0,138683000000.0 +Goodwill and Intangibles,5359000000.0,5843000000.0,5536000000.0,5875000000.0,5756000000.0,6127000000.0,5983000000.0,6141000000.0,8758000000.0,8999000000.0,8772000000.0,8823000000.0,9009000000.0,9126000000.0,9092000000.0,8767000000.0,8620000000.0,8271000000.0,8090000000.0,8105000000.0,0.0,8038000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Long-Term Assets,5478000000.0,5183000000.0,5294000000.0,5421000000.0,5146000000.0,5091000000.0,5106000000.0,4160000000.0,3764000000.0,3608000000.0,3937000000.0,4081000000.0,5422000000.0,7974000000.0,7745000000.0,7862000000.0,8757000000.0,7390000000.0,7549000000.0,10150000000.0,18177000000.0,13323000000.0,23086000000.0,22546000000.0,22283000000.0,34686000000.0,34587000000.0,33634000000.0,32978000000.0,40457000000.0,41965000000.0,41000000000.0,42522000000.0,43270000000.0,43339000000.0,44854000000.0,48849000000.0,50109000000.0 +Total Long-Term Assets,118411000000.0,123740000000.0,131406000000.0,131637000000.0,133714000000.0,144837000000.0,135448000000.0,154571000000.0,163308000000.0,178491000000.0,193303000000.0,202198000000.0,200967000000.0,217065000000.0,217685000000.0,211841000000.0,214817000000.0,227809000000.0,232542000000.0,232298000000.0,246674000000.0,262984000000.0,237449000000.0,233436000000.0,234386000000.0,232891000000.0,218652000000.0,187266000000.0,175697000000.0,177387000000.0,176647000000.0,177279000000.0,180175000000.0,199948000000.0,215693000000.0,215417000000.0,216166000000.0,228037000000.0 +Total Assets,176064000000.0,196088000000.0,194743000000.0,199856000000.0,207000000000.0,225184000000.0,205989000000.0,222520000000.0,231839000000.0,261894000000.0,261194000000.0,273151000000.0,290345000000.0,293284000000.0,305277000000.0,305602000000.0,321686000000.0,331141000000.0,334532000000.0,345173000000.0,375319000000.0,406794000000.0,367502000000.0,349197000000.0,365725000000.0,373719000000.0,341998000000.0,322239000000.0,338516000000.0,340618000000.0,320400000000.0,317344000000.0,323888000000.0,354054000000.0,337158000000.0,329840000000.0,351002000000.0,381191000000.0 +Accounts Payable,21175000000.0,26398000000.0,14912000000.0,15516000000.0,22367000000.0,29588000000.0,18914000000.0,20535000000.0,30196000000.0,38001000000.0,23159000000.0,26474000000.0,35490000000.0,33312000000.0,25098000000.0,26318000000.0,37294000000.0,38510000000.0,28573000000.0,31915000000.0,44242000000.0,62985000000.0,34311000000.0,38489000000.0,55888000000.0,44293000000.0,30443000000.0,29115000000.0,46236000000.0,45111000000.0,32421000000.0,35325000000.0,42296000000.0,63846000000.0,40127000000.0,40409000000.0,54763000000.0,74362000000.0 +Deferred Revenue,8601000000.0,10212000000.0,10142000000.0,10005000000.0,10060000000.0,11428000000.0,11474000000.0,11454000000.0,11522000000.0,12467000000.0,12515000000.0,12562000000.0,12564000000.0,12535000000.0,12783000000.0,11416000000.0,11010000000.0,11052000000.0,10789000000.0,10592000000.0,10384000000.0,11175000000.0,10862000000.0,10281000000.0,5966000000.0,5546000000.0,5532000000.0,5434000000.0,5522000000.0,5573000000.0,5928000000.0,6313000000.0,6643000000.0,7395000000.0,7595000000.0,7681000000.0,7612000000.0,7876000000.0 +Current Debt,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2010000000.0,6308000000.0,3899000000.0,3799000000.0,6999000000.0,10999000000.0,9759000000.0,10498000000.0,15996000000.0,11605000000.0,13992000000.0,13991000000.0,18475000000.0,18473000000.0,18478000000.0,20478000000.0,17472000000.0,20748000000.0,21741000000.0,22429000000.0,23482000000.0,16240000000.0,15214000000.0,20421000000.0,18675000000.0,13769000000.0,12762000000.0,13003000000.0,16039000000.0,15613000000.0,16169000000.0 +Other Current Liabilities,8766000000.0,10269000000.0,10454000000.0,10798000000.0,11231000000.0,12753000000.0,12820000000.0,12206000000.0,15422000000.0,19244000000.0,19256000000.0,19250000000.0,21557000000.0,20486000000.0,19886000000.0,17756000000.0,19097000000.0,20576000000.0,19989000000.0,20320000000.0,27715000000.0,23150000000.0,23669000000.0,22306000000.0,33327000000.0,36703000000.0,35368000000.0,31673000000.0,37720000000.0,36263000000.0,37324000000.0,35005000000.0,42684000000.0,48504000000.0,45660000000.0,43625000000.0,47493000000.0,49167000000.0 +Total Current Liabilities,38542000000.0,46879000000.0,35508000000.0,36319000000.0,43658000000.0,53769000000.0,43208000000.0,46205000000.0,63448000000.0,73611000000.0,58729000000.0,65285000000.0,80610000000.0,76092000000.0,68265000000.0,71486000000.0,79006000000.0,84130000000.0,73342000000.0,81302000000.0,100814000000.0,115788000000.0,89320000000.0,88548000000.0,115929000000.0,108283000000.0,93772000000.0,89704000000.0,105718000000.0,102161000000.0,96094000000.0,95318000000.0,105392000000.0,132507000000.0,106385000000.0,107754000000.0,125481000000.0,147574000000.0 +Long-Term Debt,0.0,0.0,0.0,16958000000.0,16960000000.0,16961000000.0,16962000000.0,29030000000.0,28987000000.0,32504000000.0,40072000000.0,47419000000.0,53329000000.0,53204000000.0,69374000000.0,68939000000.0,75427000000.0,73557000000.0,84531000000.0,89864000000.0,97207000000.0,103922000000.0,101362000000.0,97128000000.0,93735000000.0,92989000000.0,90201000000.0,84936000000.0,91807000000.0,93078000000.0,89086000000.0,94048000000.0,98667000000.0,99281000000.0,108642000000.0,105752000000.0,109106000000.0,106629000000.0 +Other Long-Term Liabilities,19312000000.0,21863000000.0,23745000000.0,23225000000.0,22833000000.0,24770000000.0,25640000000.0,26345000000.0,27857000000.0,32451000000.0,33387000000.0,34770000000.0,37051000000.0,35721000000.0,37181000000.0,38636000000.0,39004000000.0,41064000000.0,42577000000.0,41582000000.0,43251000000.0,46885000000.0,49942000000.0,48572000000.0,48914000000.0,54555000000.0,52165000000.0,51143000000.0,50503000000.0,55848000000.0,56795000000.0,55696000000.0,54490000000.0,56042000000.0,52953000000.0,52054000000.0,53325000000.0,55056000000.0 +Total Long-Term Liabilities,19312000000.0,21863000000.0,23745000000.0,40183000000.0,39793000000.0,41731000000.0,42602000000.0,55375000000.0,56844000000.0,64955000000.0,73459000000.0,82189000000.0,90380000000.0,88925000000.0,106555000000.0,107575000000.0,114431000000.0,114621000000.0,127108000000.0,131446000000.0,140458000000.0,150807000000.0,151304000000.0,145700000000.0,142649000000.0,147544000000.0,142366000000.0,136079000000.0,142310000000.0,148926000000.0,145881000000.0,149744000000.0,153157000000.0,155323000000.0,161595000000.0,157806000000.0,162431000000.0,161685000000.0 +Total Liabilities,57854000000.0,68742000000.0,59253000000.0,76502000000.0,83451000000.0,95500000000.0,85810000000.0,101580000000.0,120292000000.0,138566000000.0,132188000000.0,147474000000.0,170990000000.0,165017000000.0,174820000000.0,179061000000.0,193437000000.0,198751000000.0,200450000000.0,212748000000.0,241272000000.0,266595000000.0,240624000000.0,234248000000.0,258578000000.0,255827000000.0,236138000000.0,225783000000.0,248028000000.0,251087000000.0,241975000000.0,245062000000.0,258549000000.0,287830000000.0,267980000000.0,265560000000.0,287912000000.0,309259000000.0 +Common Stock,16422000000.0,17167000000.0,17954000000.0,19024000000.0,19764000000.0,20559000000.0,21496000000.0,22139000000.0,23313000000.0,24187000000.0,25376000000.0,26327000000.0,27416000000.0,28253000000.0,29484000000.0,30106000000.0,31251000000.0,32144000000.0,33579000000.0,34445000000.0,35867000000.0,36447000000.0,38044000000.0,38624000000.0,40201000000.0,40970000000.0,42801000000.0,43371000000.0,45174000000.0,45972000000.0,48032000000.0,48696000000.0,50779000000.0,51744000000.0,54203000000.0,54989000000.0,57365000000.0,58424000000.0 +Retained Earnings,101289000000.0,109567000000.0,116572000000.0,104564000000.0,104256000000.0,109431000000.0,98934000000.0,98715000000.0,87152000000.0,97178000000.0,100920000000.0,98252000000.0,92284000000.0,101494000000.0,102021000000.0,96542000000.0,96364000000.0,100001000000.0,100925000000.0,98525000000.0,98330000000.0,104593000000.0,91898000000.0,79436000000.0,70400000000.0,80510000000.0,64558000000.0,53724000000.0,45898000000.0,43977000000.0,33182000000.0,24136000000.0,14966000000.0,14301000000.0,15261000000.0,9233000000.0,5562000000.0,14435000000.0 +Comprehensive Income,499000000.0,612000000.0,964000000.0,-234000000.0,-471000000.0,-306000000.0,-251000000.0,86000000.0,1082000000.0,1963000000.0,2710000000.0,1098000000.0,-345000000.0,-1480000000.0,-1048000000.0,-107000000.0,634000000.0,245000000.0,-422000000.0,-545000000.0,-150000000.0,-841000000.0,-3064000000.0,-3111000000.0,-3454000000.0,-3588000000.0,-1499000000.0,-639000000.0,-584000000.0,-418000000.0,-2789000000.0,-550000000.0,-406000000.0,179000000.0,-286000000.0,58000000.0,163000000.0,-927000000.0 +Shareholders' Equity,118210000000.0,127346000000.0,135490000000.0,123354000000.0,123549000000.0,129684000000.0,120179000000.0,120940000000.0,111547000000.0,123328000000.0,129006000000.0,125677000000.0,119355000000.0,128267000000.0,130457000000.0,126541000000.0,128249000000.0,132390000000.0,134082000000.0,132425000000.0,134047000000.0,140199000000.0,126878000000.0,114949000000.0,107147000000.0,117892000000.0,105860000000.0,96456000000.0,90488000000.0,89531000000.0,78425000000.0,72282000000.0,65339000000.0,66224000000.0,69178000000.0,64280000000.0,63090000000.0,71932000000.0 +Total Liabilities and Equity,176064000000.0,196088000000.0,194743000000.0,199856000000.0,207000000000.0,225184000000.0,205989000000.0,222520000000.0,231839000000.0,261894000000.0,261194000000.0,273151000000.0,290345000000.0,293284000000.0,305277000000.0,305602000000.0,321686000000.0,331141000000.0,334532000000.0,345173000000.0,375319000000.0,406794000000.0,367502000000.0,349197000000.0,365725000000.0,373719000000.0,341998000000.0,322239000000.0,338516000000.0,340618000000.0,320400000000.0,317344000000.0,323888000000.0,354054000000.0,337158000000.0,329840000000.0,351002000000.0,381191000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF].csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF].csv index d12fa829d2f..748aff840cc 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF].csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF].csv @@ -1,17 +1,17 @@ -0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Net Income,53000000.0,1500000000.0,2940000000.0,3688000000.0,10217000000.0,15934000000.0,22112000000.0,18485000000.0,29146000000.0,39370000000.0 -Depreciation & Amortization,649000000.0,1011000000.0,1243000000.0,1945000000.0,2342000000.0,3025000000.0,4315000000.0,5741000000.0,6862000000.0,7967000000.0 -Share-Based Compensation,1572000000.0,899000000.0,3639000000.0,4681000000.0,3218000000.0,3723000000.0,4152000000.0,4836000000.0,6536000000.0,9164000000.0 -Other Operating Activities,-662000000.0,812000000.0,-496000000.0,6000000.0,331000000.0,1534000000.0,-1305000000.0,7252000000.0,-3797000000.0,1182000000.0 -Operating Cash Flow,1612000000.0,4222000000.0,7326000000.0,10320000000.0,16108000000.0,24216000000.0,29274000000.0,36314000000.0,38747000000.0,57683000000.0 -Capital Expenditures,-1235000000.0,-1362000000.0,-1831000000.0,-2523000000.0,-4491000000.0,-6733000000.0,-13915000000.0,-15102000000.0,-15115000000.0,-18567000000.0 -Acquisitions,-911000000.0,-368000000.0,-4975000000.0,-313000000.0,-123000000.0,-122000000.0,-137000000.0,-508000000.0,-388000000.0,-851000000.0 -Change in Investments,-4874000000.0,-882000000.0,1243000000.0,-6700000000.0,-7186000000.0,-13250000000.0,2449000000.0,-4254000000.0,-14520000000.0,12132000000.0 -Other Investing Activities,-4000000.0,-12000000.0,-350000000.0,102000000.0,8000000.0,-13000000.0,0.0,0.0,-36000000.0,-284000000.0 -Investing Cash Flow,-7024000000.0,-2624000000.0,-5913000000.0,-9434000000.0,-11792000000.0,-20118000000.0,-11603000000.0,-19864000000.0,-30059000000.0,-7570000000.0 -Dividends Paid,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Share Issuance / Repurchase,6777000000.0,1504000000.0,0.0,0.0,0.0,-1976000000.0,-12879000000.0,-4202000000.0,-6272000000.0,-44537000000.0 -Debt Issued / Paid,1335000000.0,-1891000000.0,-243000000.0,-119000000.0,-312000000.0,0.0,500000000.0,-775000000.0,-580000000.0,-663000000.0 -Other Financing Activities,-1829000000.0,-280000000.0,-55000000.0,-20000000.0,2000000.0,-3259000000.0,-3193000000.0,-2322000000.0,-3440000000.0,-5528000000.0 -Financing Cash Flow,6283000000.0,-667000000.0,-298000000.0,-139000000.0,-310000000.0,-5235000000.0,-15572000000.0,-7299000000.0,-10292000000.0,-50728000000.0 -Net Cash Flow,872000000.0,939000000.0,992000000.0,592000000.0,3943000000.0,-905000000.0,1920000000.0,9155000000.0,-1325000000.0,-1089000000.0 +0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +Net Income,53000000.0,1500000000.0,2940000000.0,3688000000.0,10217000000.0,15934000000.0,22112000000.0,18485000000.0,29146000000.0,39370000000.0 +Depreciation & Amortization,649000000.0,1011000000.0,1243000000.0,1945000000.0,2342000000.0,3025000000.0,4315000000.0,5741000000.0,6862000000.0,7967000000.0 +Share-Based Compensation,1572000000.0,899000000.0,3639000000.0,4681000000.0,3218000000.0,3723000000.0,4152000000.0,4836000000.0,6536000000.0,9164000000.0 +Other Operating Activities,-662000000.0,812000000.0,-496000000.0,6000000.0,331000000.0,1534000000.0,-1305000000.0,7252000000.0,-3797000000.0,1182000000.0 +Operating Cash Flow,1612000000.0,4222000000.0,7326000000.0,10320000000.0,16108000000.0,24216000000.0,29274000000.0,36314000000.0,38747000000.0,57683000000.0 +Capital Expenditures,-1235000000.0,-1362000000.0,-1831000000.0,-2523000000.0,-4491000000.0,-6733000000.0,-13915000000.0,-15102000000.0,-15115000000.0,-18567000000.0 +Acquisitions,-911000000.0,-368000000.0,-4975000000.0,-313000000.0,-123000000.0,-122000000.0,-137000000.0,-508000000.0,-388000000.0,-851000000.0 +Change in Investments,-4874000000.0,-882000000.0,1243000000.0,-6700000000.0,-7186000000.0,-13250000000.0,2449000000.0,-4254000000.0,-14520000000.0,12132000000.0 +Other Investing Activities,-4000000.0,-12000000.0,-350000000.0,102000000.0,8000000.0,-13000000.0,0.0,0.0,-36000000.0,-284000000.0 +Investing Cash Flow,-7024000000.0,-2624000000.0,-5913000000.0,-9434000000.0,-11792000000.0,-20118000000.0,-11603000000.0,-19864000000.0,-30059000000.0,-7570000000.0 +Dividends Paid,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Share Issuance / Repurchase,6777000000.0,1504000000.0,0.0,0.0,0.0,-1976000000.0,-12879000000.0,-4202000000.0,-6272000000.0,-44537000000.0 +Debt Issued / Paid,1335000000.0,-1891000000.0,-243000000.0,-119000000.0,-312000000.0,0.0,500000000.0,-775000000.0,-580000000.0,-663000000.0 +Other Financing Activities,-1829000000.0,-280000000.0,-55000000.0,-20000000.0,2000000.0,-3259000000.0,-3193000000.0,-2322000000.0,-3440000000.0,-5528000000.0 +Financing Cash Flow,6283000000.0,-667000000.0,-298000000.0,-139000000.0,-310000000.0,-5235000000.0,-15572000000.0,-7299000000.0,-10292000000.0,-50728000000.0 +Net Cash Flow,872000000.0,939000000.0,992000000.0,592000000.0,3943000000.0,-905000000.0,1920000000.0,9155000000.0,-1325000000.0,-1089000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_1.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_1.csv index d364b417e2f..ff0f723ac1e 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_1.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_1.csv @@ -1,17 +1,17 @@ -0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Net Income,-396210000.0,-74010000.0,-294040000.0,-888660000.0,-674910000.0,-1962000000.0,-976000000.0,-862000000.0,721000000.0,5519000000.0 -Depreciation & Amortization,28830000.0,106080000.0,231930000.0,422590000.0,947100000.0,1636000000.0,1901000000.0,2154000000.0,2322000000.0,2911000000.0 -Share-Based Compensation,50150000.0,80740000.0,156500000.0,198000000.0,334230000.0,467000000.0,749000000.0,898000000.0,1734000000.0,2121000000.0 -Other Operating Activities,53430000.0,152000000.0,-151720000.0,-256430000.0,-730240000.0,-202000000.0,424000000.0,215000000.0,1166000000.0,946000000.0 -Operating Cash Flow,-263820000.0,264800000.0,-57340000.0,-524500000.0,-123830000.0,-61000000.0,2098000000.0,2405000000.0,5943000000.0,11497000000.0 -Capital Expenditures,-239230000.0,-264220000.00000003,-969890000.0,-1634850000.0,-1440470000.0,-4081000000.0,-2319000000.0,-1432000000.0,-3232000000.0,-6514000000.0 -Acquisitions,0.0,0.0,0.0,-12260000.0,342720000.0,-115000000.0,-18000000.0,-45000000.0,-13000000.0,0.0 -Change in Investments,25010000.0,0.0,-16710000.0,0.0,16670000.000000002,0.0,0.0,0.0,0.0,-1360000000.0 -Other Investing Activities,7290000.0,14810000.0,-3850000.0,-26440000.0,0.0,0.0,0.0,41000000.0,113000000.0,6000000.0 -Investing Cash Flow,-206930000.0,-249420000.0,-990440000.0,-1673550000.0,-1081090000.0,-4196000000.0,-2337000000.0,-1436000000.0,-3132000000.0,-7868000000.0 -Dividends Paid,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Share Issuance / Repurchase,246380000.0,630630000.0,489620000.0,856610000.0,1865550000.0,712000000.0,296000000.0,1285000000.0,12686000000.0,707000000.0 -Debt Issued / Paid,173250000.0,199240000.0,2292090000.0,683940000.0,1718190000.0,3672000000.0,74000000.0,761000000.0,-2494000000.0,-5741000000.0 -Other Financing Activities,0.0,-194440000.0,-638580000.0,-17030000.0,160240000.0,31000000.0,204000000.0,-517000000.0,-219000000.0,-169000000.0 -Financing Cash Flow,419640000.0,635420000.0,2143130000.0,1523520000.0,3743980000.0,4415000000.0,574000000.0,1529000000.0,9973000000.0,-5203000000.0 -Net Cash Flow,-53380000.0,644000000.0,1059819999.9999999,-708810000.0,2532510000.0,198000000.0,312000000.0,2506000000.0,13118000000.0,-1757000000.0 +0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +Net Income,-396210000.0,-74010000.0,-294040000.0,-888660000.0,-674910000.0,-1962000000.0,-976000000.0,-862000000.0,721000000.0,5519000000.0 +Depreciation & Amortization,28830000.0,106080000.0,231930000.0,422590000.0,947100000.0,1636000000.0,1901000000.0,2154000000.0,2322000000.0,2911000000.0 +Share-Based Compensation,50150000.0,80740000.0,156500000.0,198000000.0,334230000.0,467000000.0,749000000.0,898000000.0,1734000000.0,2121000000.0 +Other Operating Activities,53430000.0,152000000.0,-151720000.0,-256430000.0,-730240000.0,-202000000.0,424000000.0,215000000.0,1166000000.0,946000000.0 +Operating Cash Flow,-263820000.0,264800000.0,-57340000.0,-524500000.0,-123830000.0,-61000000.0,2098000000.0,2405000000.0,5943000000.0,11497000000.0 +Capital Expenditures,-239230000.0,-264220000.00000003,-969890000.0,-1634850000.0,-1440470000.0,-4081000000.0,-2319000000.0,-1432000000.0,-3232000000.0,-6514000000.0 +Acquisitions,0.0,0.0,0.0,-12260000.0,342720000.0,-115000000.0,-18000000.0,-45000000.0,-13000000.0,0.0 +Change in Investments,25010000.0,0.0,-16710000.0,0.0,16670000.000000002,0.0,0.0,0.0,0.0,-1360000000.0 +Other Investing Activities,7290000.0,14810000.0,-3850000.0,-26440000.0,0.0,0.0,0.0,41000000.0,113000000.0,6000000.0 +Investing Cash Flow,-206930000.0,-249420000.0,-990440000.0,-1673550000.0,-1081090000.0,-4196000000.0,-2337000000.0,-1436000000.0,-3132000000.0,-7868000000.0 +Dividends Paid,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Share Issuance / Repurchase,246380000.0,630630000.0,489620000.0,856610000.0,1865550000.0,712000000.0,296000000.0,1285000000.0,12686000000.0,707000000.0 +Debt Issued / Paid,173250000.0,199240000.0,2292090000.0,683940000.0,1718190000.0,3672000000.0,74000000.0,761000000.0,-2494000000.0,-5741000000.0 +Other Financing Activities,0.0,-194440000.0,-638580000.0,-17030000.0,160240000.0,31000000.0,204000000.0,-517000000.0,-219000000.0,-169000000.0 +Financing Cash Flow,419640000.0,635420000.0,2143130000.0,1523520000.0,3743980000.0,4415000000.0,574000000.0,1529000000.0,9973000000.0,-5203000000.0 +Net Cash Flow,-53380000.0,644000000.0,1059819999.9999999,-708810000.0,2532510000.0,198000000.0,312000000.0,2506000000.0,13118000000.0,-1757000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_2.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_2.csv index 99fa3b71c26..b494c0f832c 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_2.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks2-ncf-sa_keys2-annual-CF]_2.csv @@ -1,17 +1,17 @@ -0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Net Income,16978000000.0,21863000000.0,22074000000.0,12193000000.0,20539000000.0,25489000000.0,16571000000.0,39240000000.0,44281000000.0,61271000000.0 -Depreciation & Amortization,2967000000.0,3755000000.0,5212000000.0,5957000000.0,6622000000.0,8778000000.0,10261000000.0,11682000000.0,12796000000.0,11686000000.0 -Share-Based Compensation,2151000000.0,2197000000.0,2446000000.0,2574000000.0,2668000000.0,3266000000.0,3940000000.0,4652000000.0,5289000000.0,6118000000.0 -Other Operating Activities,9530000000.0,1018000000.0,2770000000.0,8944000000.0,3496000000.0,1974000000.0,13112000000.0,-3389000000.0,-1691000000.0,-2335000000.0 -Operating Cash Flow,31626000000.0,28833000000.0,32502000000.0,29668000000.0,33325000000.0,39507000000.0,43884000000.0,52185000000.0,60675000000.0,76740000000.0 -Capital Expenditures,-2305000000.0,-4257000000.0,-5485000000.0,-5944000000.0,-8343000000.0,-8129000000.0,-11632000000.0,-13925000000.0,-15441000000.0,-20622000000.0 -Acquisitions,-10112000000.0,-1584000000.0,-5937000000.0,-3723000000.0,-1393000000.0,-25944000000.0,-888000000.0,-2388000000.0,-2521000000.0,-8909000000.0 -Change in Investments,-11975000000.0,-17802000000.0,-7324000000.0,-12868000000.0,-14417000000.0,-12511000000.0,6557000000.0,540000000.0,6980000000.0,2876000000.0 -Other Investing Activities,-394000000.0,-168000000.0,-87000000.0,-466000000.0,203000000.0,-197000000.0,-98000000.0,0.0,-1241000000.0,-922000000.0 -Investing Cash Flow,-24786000000.0,-23811000000.0,-18833000000.0,-23001000000.0,-23950000000.0,-46781000000.0,-6061000000.0,-15773000000.0,-12223000000.0,-27577000000.0 -Dividends Paid,-6385000000.0,-7455000000.0,-8879000000.0,-9882000000.0,-11006000000.0,-11845000000.0,-12699000000.0,-13811000000.0,-15137000000.0,-16521000000.0 -Share Issuance / Repurchase,-3116000000.0,-4429000000.0,-6709000000.0,-13809000000.0,-15301000000.0,-11016000000.0,-9719000000.0,-18401000000.0,-21625000000.0,-25692000000.0 -Debt Issued / Paid,0.0,3537000000.0,6962000000.0,13661000000.0,18283000000.0,31459000000.0,-10201000000.0,-4000000000.0,-8935000000.0,-5504000000.0 -Other Financing Activities,93000000.0,199000000.0,-39000000.0,362000000.0,-369000000.0,-190000000.0,-971000000.0,-675000000.0,-334000000.0,-769000000.0 -Financing Cash Flow,-9408000000.0,-8148000000.0,-8665000000.0,-9668000000.0,-8393000000.0,8408000000.0,-33590000000.0,-36887000000.0,-46031000000.0,-48486000000.0 -Net Cash Flow,-2672000000.0,-3134000000.0,4865000000.0,-3074000000.0,915000000.0,1153000000.0,4283000000.0,-590000000.0,2220000000.0,648000000.0 +0,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +Net Income,16978000000.0,21863000000.0,22074000000.0,12193000000.0,20539000000.0,25489000000.0,16571000000.0,39240000000.0,44281000000.0,61271000000.0 +Depreciation & Amortization,2967000000.0,3755000000.0,5212000000.0,5957000000.0,6622000000.0,8778000000.0,10261000000.0,11682000000.0,12796000000.0,11686000000.0 +Share-Based Compensation,2151000000.0,2197000000.0,2446000000.0,2574000000.0,2668000000.0,3266000000.0,3940000000.0,4652000000.0,5289000000.0,6118000000.0 +Other Operating Activities,9530000000.0,1018000000.0,2770000000.0,8944000000.0,3496000000.0,1974000000.0,13112000000.0,-3389000000.0,-1691000000.0,-2335000000.0 +Operating Cash Flow,31626000000.0,28833000000.0,32502000000.0,29668000000.0,33325000000.0,39507000000.0,43884000000.0,52185000000.0,60675000000.0,76740000000.0 +Capital Expenditures,-2305000000.0,-4257000000.0,-5485000000.0,-5944000000.0,-8343000000.0,-8129000000.0,-11632000000.0,-13925000000.0,-15441000000.0,-20622000000.0 +Acquisitions,-10112000000.0,-1584000000.0,-5937000000.0,-3723000000.0,-1393000000.0,-25944000000.0,-888000000.0,-2388000000.0,-2521000000.0,-8909000000.0 +Change in Investments,-11975000000.0,-17802000000.0,-7324000000.0,-12868000000.0,-14417000000.0,-12511000000.0,6557000000.0,540000000.0,6980000000.0,2876000000.0 +Other Investing Activities,-394000000.0,-168000000.0,-87000000.0,-466000000.0,203000000.0,-197000000.0,-98000000.0,0.0,-1241000000.0,-922000000.0 +Investing Cash Flow,-24786000000.0,-23811000000.0,-18833000000.0,-23001000000.0,-23950000000.0,-46781000000.0,-6061000000.0,-15773000000.0,-12223000000.0,-27577000000.0 +Dividends Paid,-6385000000.0,-7455000000.0,-8879000000.0,-9882000000.0,-11006000000.0,-11845000000.0,-12699000000.0,-13811000000.0,-15137000000.0,-16521000000.0 +Share Issuance / Repurchase,-3116000000.0,-4429000000.0,-6709000000.0,-13809000000.0,-15301000000.0,-11016000000.0,-9719000000.0,-18401000000.0,-21625000000.0,-25692000000.0 +Debt Issued / Paid,0.0,3537000000.0,6962000000.0,13661000000.0,18283000000.0,31459000000.0,-10201000000.0,-4000000000.0,-8935000000.0,-5504000000.0 +Other Financing Activities,93000000.0,199000000.0,-39000000.0,362000000.0,-369000000.0,-190000000.0,-971000000.0,-675000000.0,-334000000.0,-769000000.0 +Financing Cash Flow,-9408000000.0,-8148000000.0,-8665000000.0,-9668000000.0,-8393000000.0,8408000000.0,-33590000000.0,-36887000000.0,-46031000000.0,-48486000000.0 +Net Cash Flow,-2672000000.0,-3134000000.0,4865000000.0,-3074000000.0,915000000.0,1153000000.0,4283000000.0,-590000000.0,2220000000.0,648000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS].csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS].csv index 657a3957642..016a09b7457 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS].csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS].csv @@ -1,13 +1,13 @@ -0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 -Revenue,1262000000.0,1585000000.0,1458000000.0,1813000000.0,2016000000.0,2586000000.0,2502000000.0,2910000000.0,3203000000.0,3851000000.0,3543000000.0,4042000000.0,4501000000.0,5841000000.0,5382000000.0,6436000000.0,7011000000.0,8809000000.0,8032000000.0,9321000000.0,10328000000.0,12972000000.0,11966000000.0,13231000000.0,13727000000.0,16914000000.0,15077000000.0,16886000000.0,17652000000.0,21082000000.0,17737000000.0,18687000000.0,21470000000.0,28072000000.0,26171000000.0,29077000000.0,29010000000.0,33671000000.0 -Cost of Revenue,322000000.0,397000000.0,413000000.0,465000000.0,507000000.0,491000000.0,462000000.0,473000000.0,565000000.0,652000000.0,654000000.0,668000000.0,720000000.0,824000000.0,838000000.0,917000000.0,987000000.0,1047000000.0,1159000000.0,1237000000.0,1448000000.0,1611000000.0,1927000000.0,2214000000.0,2418000000.0,2796000000.0,2816000000.0,3307000000.0,3155000000.0,3491000000.0,3459000000.0,3829000000.0,4194000000.0,5210000000.0,5131000000.0,5399000000.0,5771000000.0,6348000000.0 -Gross Profit,940000000.0,1188000000.0,1045000000.0,1348000000.0,1509000000.0,2095000000.0,2040000000.0,2437000000.0,2638000000.0,3199000000.0,2889000000.0,3374000000.0,3781000000.0,5017000000.0,4544000000.0,5519000000.0,6024000000.0,7762000000.0,6873000000.0,8084000000.0,8880000000.0,11361000000.0,10039000000.0,11017000000.0,11309000000.0,14118000000.0,12261000000.0,13579000000.0,14497000000.0,17591000000.0,14278000000.0,14858000000.0,17276000000.0,22862000000.0,21040000000.0,23678000000.0,23239000000.0,27323000000.0 -"Selling, General & Admin",319000000.0,368000000.0,379000000.0,442000000.0,404000000.0,554000000.0,510000000.0,555000000.0,633000000.0,955000000.0,894000000.0,931000000.0,1051000000.0,1143000000.0,1191000000.0,1314000000.0,1365000000.0,1632000000.0,1712000000.0,1764000000.0,1706000000.0,2060000000.0,2352000000.0,2631000000.0,2871000000.0,3443000000.0,6084000000.0,5638000000.0,3764000000.0,4855000000.0,4370000000.0,4433000000.0,4473000000.0,4880000000.0,4465000000.0,5215000000.0,6500000000.0,7692000000.0 -Research & Development,244000000.0,297000000.0,293000000.0,344000000.0,369000000.0,409000000.0,455000000.0,492000000.0,608000000.0,1111000000.0,1062000000.0,1170000000.0,1271000000.0,1314000000.0,1343000000.0,1471000000.0,1542000000.0,1563000000.0,1834000000.0,1919000000.0,2052000000.0,1949000000.0,2238000000.0,2523000000.0,2657000000.0,2855000000.0,2860000000.0,3315000000.0,3548000000.0,3878000000.0,4015000000.0,4462000000.0,4763000000.0,5207000000.0,5197000000.0,6096000000.0,6316000000.0,7046000000.0 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Income,-6000000.0,53000000.0,20000000.0,17000000.0,10000000.0,2000000.0,0.0,4000000.0,61000000.0,19000000.0,1000000.0,0.0,27000000.0,3000000.0,-56000000.0,-20000000.0,-47000000.0,34000000.0,-81000000.0,-87000000.0,-114000000.0,-110000000.0,-161000000.0,-5000000.0,-131000000.0,-151000000.0,-165000000.0,-206000000.0,-144000000.0,-311000000.0,32000000.0,-168000000.0,-93000000.0,-280000000.0,-125000000.0,-146000000.0,-142000000.0,-118000000.0 -Income Tax,431000000.0,441000000.0,134000000.0,212000000.0,301000000.0,607000000.0,433000000.0,595000000.0,530000000.0,413000000.0,420000000.0,554000000.0,536000000.0,996000000.0,328000000.0,471000000.0,537000000.0,964000000.0,344000000.0,594000000.0,529000000.0,3193000000.0,622000000.0,762000000.0,775000000.0,1089000000.0,1053000000.0,2216000000.0,1238000000.0,1820000000.0,959000000.0,953000000.0,287000000.0,1836000000.0,2006000000.0,2119000000.0,1371000000.0,2418000000.0 -Net Income,-59000000.0,64000000.0,219000000.0,333000000.0,425000000.0,523000000.0,642000000.0,791000000.0,806000000.0,701000000.0,512000000.0,719000000.0,896000000.0,1561000000.0,1738000000.0,2283000000.0,2627000000.0,3569000000.0,3064000000.0,3894000000.0,4707000000.0,4269000000.0,4988000000.0,5106000000.0,5137000000.0,6882000000.0,2429000000.0,2616000000.0,6091000000.0,7349000000.0,4902000000.0,5178000000.0,7846000000.0,11219000000.0,9497000000.0,10394000000.0,9194000000.0,10285000000.0 -Preferred Dividends,0.0,21000000.0,2000000.0,2000000.0,3000000.0,3000000.0,3000000.0,3000000.0,4000000.0,5000000.0,3000000.0,4000000.0,5000000.0,7000000.0,6000000.0,7000000.0,7000000.0,9000000.0,5000000.0,4000000.0,3000000.0,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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Revenue,322000000.0,397000000.0,413000000.0,465000000.0,507000000.0,491000000.0,462000000.0,473000000.0,565000000.0,652000000.0,654000000.0,668000000.0,720000000.0,824000000.0,838000000.0,917000000.0,987000000.0,1047000000.0,1159000000.0,1237000000.0,1448000000.0,1611000000.0,1927000000.0,2214000000.0,2418000000.0,2796000000.0,2816000000.0,3307000000.0,3155000000.0,3491000000.0,3459000000.0,3829000000.0,4194000000.0,5210000000.0,5131000000.0,5399000000.0,5771000000.0,6348000000.0 +Gross Profit,940000000.0,1188000000.0,1045000000.0,1348000000.0,1509000000.0,2095000000.0,2040000000.0,2437000000.0,2638000000.0,3199000000.0,2889000000.0,3374000000.0,3781000000.0,5017000000.0,4544000000.0,5519000000.0,6024000000.0,7762000000.0,6873000000.0,8084000000.0,8880000000.0,11361000000.0,10039000000.0,11017000000.0,11309000000.0,14118000000.0,12261000000.0,13579000000.0,14497000000.0,17591000000.0,14278000000.0,14858000000.0,17276000000.0,22862000000.0,21040000000.0,23678000000.0,23239000000.0,27323000000.0 +"Selling, General & 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b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS]_1.csv index aff614586e2..906dbda5b2d 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS]_1.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks3-ni-sa_keys3-quarterly-IS]_1.csv @@ -1,13 +1,13 @@ -0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 -Revenue,50100000.0,306330000.0,561790000.0,405140000.0,431350000.0,615220000.0,620540000.0,769350000.0,851800000.0,956660000.0,939880000.0,954980000.0,936790000.0,1214380000.0,1147050000.0,1270020000.0,2298440000.0,2284630000.0,2696270000.0,2789560000.0,2984680000.0,3288500000.0,3408750000.0,4002230000.0,6824000000.0,7226000000.0,4541000000.0,6350000000.0,6303000000.0,7384000000.0,5985000000.0,6036000000.0,8771000000.0,10744000000.0,10389000000.0,11958000000.0,13757000000.0,17719000000.0 -Cost of Revenue,58870000.0,282480000.0,465470000.0,304660000.0,328480000.0,458630000.0,465410000.0,556350000.0,599950000.0,694960000.0,679810000.0,741610000.0,705290000.0,995820000.0,894580000.0,995240000.0,1661700000.0,1849350000.0,2028320000.0,2122940000.0,2535540000.0,2849200000.0,2952230000.0,3383300000.0,5300000000.0,5783000000.0,3975000000.0,5429000000.0,5112000000.0,5993000000.0,4751000000.0,4769000000.0,6708000000.0,8678000000.0,8174000000.0,9074000000.0,10097000000.0,12872000000.0 -Gross 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Development,61900000.0,68830000.0,54860000.0,52310000.0,56350000.0,68450000.0,81540000.0,107720000.0,135870000.0,139570000.0,167150000.0,181710000.0,178790000.0,190240000.0,182480000.0,191660000.0,214300000.0,245960000.0,322040000.0,369770000.0,331620000.0,354560000.0,367100000.0,386130000.0,351000000.0,356000000.0,340000000.0,324000000.0,334000000.0,345000000.0,324000000.0,279000000.0,366000000.0,522000000.0,666000000.0,576000000.0,611000000.0,740000000.0 -Other Operating Expenses,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,103430000.0,27000000.0,5000000.0,44000000.0,117000000.0,0.0,-12000000.0,0.0,0.0,0.0,0.0,-101000000.0,23000000.0,51000000.0,0.0 -Operating 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Profit,11840000000.0,15764000000.0,15702000000.0,14158000000.0,13384000000.0,16197000000.0,14425000000.0,15749000000.0,14928000000.0,16334000000.0,14568000000.0,14712000000.0,13172000000.0,13924000000.0,12809000000.0,18469000000.0,14084000000.0,15925000000.0,15152000000.0,17149000000.0,16260000000.0,17854000000.0,17550000000.0,20343000000.0,19179000000.0,20048000000.0,20401000000.0,23305000000.0,22649000000.0,24548000000.0,24046000000.0,25694000000.0,26152000000.0,28882000000.0,28661000000.0,32161000000.0,31671000000.0,34768000000.0 +"Selling, General & 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Development,2460000000.0,2528000000.0,2640000000.0,2783000000.0,2767000000.0,2748000000.0,2743000000.0,3123000000.0,3065000000.0,2903000000.0,2984000000.0,3094000000.0,2962000000.0,2900000000.0,2980000000.0,3146000000.0,3106000000.0,3062000000.0,3355000000.0,3514000000.0,3574000000.0,3504000000.0,3715000000.0,3933000000.0,3977000000.0,4070000000.0,4316000000.0,4513000000.0,4565000000.0,4603000000.0,4887000000.0,5214000000.0,4926000000.0,4899000000.0,5204000000.0,5687000000.0,5599000000.0,5758000000.0 +Other Operating Expenses,0.0,0.0,0.0,0.0,0.0,0.0,0.0,127000000.0,1140000000.0,243000000.0,190000000.0,8438000000.0,0.0,0.0,0.0,1110000000.0,0.0,0.0,0.0,306000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Operating Income,5308000000.0,7771000000.0,7612000000.0,6073000000.0,6334000000.0,7969000000.0,6974000000.0,6482000000.0,5844000000.0,7776000000.0,6594000000.0,-2053000000.0,5793000000.0,6026000000.0,5283000000.0,8976000000.0,6715000000.0,7905000000.0,6723000000.0,7682000000.0,7708000000.0,8679000000.0,8292000000.0,10379000000.0,9955000000.0,10258000000.0,10341000000.0,12405000000.0,12686000000.0,13891000000.0,12975000000.0,13407000000.0,15876000000.0,17897000000.0,17048000000.0,19095000000.0,20238000000.0,22247000000.0 +Interest Expense / Income,95000000.0,105000000.0,109000000.0,120000000.0,118000000.0,135000000.0,175000000.0,169000000.0,161000000.0,162000000.0,211000000.0,247000000.0,249000000.0,309000000.0,340000000.0,345000000.0,437000000.0,521000000.0,609000000.0,655000000.0,672000000.0,698000000.0,691000000.0,672000000.0,674000000.0,672000000.0,671000000.0,669000000.0,637000000.0,654000000.0,614000000.0,686000000.0,589000000.0,571000000.0,633000000.0,553000000.0,539000000.0,525000000.0 +Other Expense / Income,-321000000.0,-104000000.0,-100000000.0,-192000000.0,-192000000.0,-44000000.0,-158000000.0,-264000000.0,-213000000.0,-236000000.0,-134000000.0,-544000000.0,31000000.0,-138000000.0,-93000000.0,-604000000.0,-549000000.0,-638000000.0,-980000000.0,-931000000.0,-948000000.0,-1188000000.0,-1040000000.0,-973000000.0,-940000000.0,-799000000.0,-816000000.0,-860000000.0,-637000000.0,-848000000.0,-482000000.0,-701000000.0,-837000000.0,-1011000000.0,-821000000.0,-863000000.0,-825000000.0,-793000000.0 +Income Tax,1068000000.0,1393000000.0,1548000000.0,1180000000.0,1164000000.0,1320000000.0,1297000000.0,1965000000.0,1356000000.0,1987000000.0,1532000000.0,1439000000.0,611000000.0,837000000.0,1280000000.0,2372000000.0,1160000000.0,1755000000.0,1608000000.0,-111000000.0,1408000000.0,15471000000.0,1217000000.0,1807000000.0,1397000000.0,1965000000.0,1677000000.0,-591000000.0,2008000000.0,2436000000.0,2091000000.0,2220000000.0,2231000000.0,2874000000.0,1779000000.0,2947000000.0,19000000.0,3750000000.0 +Net Income,4466000000.0,6377000000.0,6055000000.0,4965000000.0,5244000000.0,6558000000.0,5660000000.0,4612000000.0,4540000000.0,5863000000.0,4985000000.0,-3195000000.0,4902000000.0,5018000000.0,3756000000.0,6863000000.0,5667000000.0,6267000000.0,5486000000.0,8069000000.0,6576000000.0,-6302000000.0,7424000000.0,8873000000.0,8824000000.0,8420000000.0,8809000000.0,13187000000.0,10678000000.0,11649000000.0,10752000000.0,11202000000.0,13893000000.0,15463000000.0,15457000000.0,16458000000.0,20505000000.0,18765000000.0 +Preferred Dividends,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS].csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS].csv index 4a99e6a010b..bbab322dd96 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS].csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS].csv @@ -1,28 +1,28 @@ -0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 -Cash & Equivalents,2478000000.0,2384000000.0,2325000000.0,3001000000.0,3100000000.0,3323000000.0,2998000000.0,4384000000.0,8999000000.0,4315000000.0,3419000000.0,5123000000.0,4308000000.0,4907000000.0,6456000000.0,5108000000.0,6038000000.0,8903000000.0,7104000000.0,6252000000.0,7201000000.0,8079000000.0,12082000000.0,11552000000.0,9637000000.0,10019000000.0,11076000000.0,13877000000.0,15979000000.0,19079000000.0,23618000000.0,21045000000.0,11617000000.0,17576000000.0,19513000000.0,16186000000.0,14496000000.0,16601000000.0 -Short-Term Investments,7974000000.0,7242000000.0,7147000000.0,7251000000.0,6228000000.0,8126000000.0,9631000000.0,9572000000.0,5251000000.0,6884000000.0,8994000000.0,9002000000.0,11526000000.0,13527000000.0,14165000000.0,18185000000.0,20102000000.0,20546000000.0,25202000000.0,29200000000.0,31088000000.0,33632000000.0,31874000000.0,30757000000.0,31569000000.0,31095000000.0,34167000000.0,34719000000.0,36290000000.0,35776000000.0,36671000000.0,37195000000.0,44003000000.0,44378000000.0,44706000000.0,47894000000.0,43579000000.0,31397000000.0 -Cash & Cash Equivalents,10452000000.0,9626000000.0,9472000000.0,10252000000.0,9328000000.0,11449000000.0,12629000000.0,13956000000.0,14250000000.0,11199000000.0,12413000000.0,14125000000.0,15834000000.0,18434000000.0,20621000000.0,23293000000.0,26140000000.0,29449000000.0,32306000000.0,35452000000.0,38289000000.0,41711000000.0,43956000000.0,42309000000.0,41206000000.0,41114000000.0,45243000000.0,48596000000.0,52269000000.0,54855000000.0,60289000000.0,58240000000.0,55620000000.0,61954000000.0,64219000000.0,64080000000.0,58075000000.0,47998000000.0 -Receivables,635000000.0,719000000.0,659000000.0,775000000.0,872000000.0,1109000000.0,1006000000.0,1190000000.0,1363000000.0,1678000000.0,1508000000.0,1815000000.0,2010000000.0,2559000000.0,2348000000.0,2801000000.0,3070000000.0,3993000000.0,3415000000.0,3897000000.0,4424000000.0,5832000000.0,5115000000.0,5590000000.0,6058000000.0,7587000000.0,6475000000.0,7513000000.0,7673000000.0,9518000000.0,7289000000.0,7483000000.0,8024000000.0,11335000000.0,10276000000.0,11698000000.0,12088000000.0,14039000000.0 -Inventory,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Other Current Assets,1198000000.0,922000000.0,911000000.0,394000000.0,349000000.0,512000000.0,425000000.0,411000000.0,502000000.0,513000000.0,1083000000.0,1011000000.0,1295000000.0,659000000.0,843000000.0,916000000.0,1118000000.0,959000000.0,1209000000.0,1455000000.0,1490000000.0,1020000000.0,1341000000.0,1934000000.0,1883000000.0,1779000000.0,1582000000.0,1852000000.0,2137000000.0,1852000000.0,1771000000.0,2407000000.0,2155000000.0,2381000000.0,2827000000.0,4919000000.0,5258000000.0,4629000000.0 -Total Current Assets,12285000000.0,11267000000.0,11042000000.0,11421000000.0,10549000000.0,13070000000.0,14060000000.0,15557000000.0,16115000000.0,13390000000.0,15004000000.0,16951000000.0,19139000000.0,21652000000.0,23812000000.0,27010000000.0,30328000000.0,34401000000.0,36930000000.0,40804000000.0,44203000000.0,48563000000.0,50412000000.0,49833000000.0,49147000000.0,50480000000.0,53300000000.0,57961000000.0,62079000000.0,66225000000.0,69349000000.0,68130000000.0,65799000000.0,75670000000.0,77322000000.0,80697000000.0,75421000000.0,66666000000.0 -"Property, Plant & Equipment",2289000000.0,2391000000.0,2533000000.0,2577000000.0,2685000000.0,2882000000.0,3074000000.0,3334000000.0,3703000000.0,3967000000.0,4619000000.0,4955000000.0,5335000000.0,5687000000.0,6467000000.0,7104000000.0,7899000000.0,8591000000.0,9462000000.0,10628000000.0,12158000000.0,13721000000.0,16211000000.0,18357000000.0,21112000000.0,24683000000.0,34092000000.0,37271000000.0,40687000000.0,44783000000.0,46486000000.0,48435000000.0,51730000000.0,54981000000.0,57922000000.0,61434000000.0,64789000000.0,69964000000.0 -Long-Term Investments,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,86000000.0,0.0,0.0,6164000000.0,6234000000.0,6342000000.0,6393000000.0,6758000000.0,6775000000.0 -Goodwill and Intangibles,1423000000.0,1388000000.0,1501000000.0,1631000000.0,1609000000.0,1722000000.0,1682000000.0,1672000000.0,3929000000.0,21910000000.0,21779000000.0,21630000000.0,21461000000.0,21272000000.0,21096000000.0,20922000000.0,20787000000.0,20657000000.0,20486000000.0,20315000000.0,20263000000.0,20105000000.0,20003000000.0,19836000000.0,19755000000.0,19595000000.0,19483000000.0,19328000000.0,19191000000.0,19609000000.0,19649000000.0,19888000000.0,19775000000.0,19673000000.0,19561000000.0,19733000000.0,19430000000.0,19831000000.0 -Other Long-Term Assets,41000000.0,57000000.0,87000000.0,95000000.0,90000000.0,221000000.0,212000000.0,206000000.0,441000000.0,699000000.0,605000000.0,594000000.0,534000000.0,796000000.0,700000000.0,703000000.0,660000000.0,1312000000.0,1836000000.0,2096000000.0,2374000000.0,2135000000.0,2319000000.0,2265000000.0,2438000000.0,2576000000.0,2602000000.0,2446000000.0,2461000000.0,2673000000.0,2887000000.0,3238000000.0,2969000000.0,2758000000.0,2376000000.0,2352000000.0,3187000000.0,2751000000.0 -Total Long-Term Assets,3753000000.0,3836000000.0,4121000000.0,4303000000.0,4384000000.0,4825000000.0,4968000000.0,5212000000.0,8073000000.0,26576000000.0,27003000000.0,27179000000.0,27330000000.0,27755000000.0,28263000000.0,28729000000.0,29346000000.0,30560000000.0,31784000000.0,33039000000.0,34795000000.0,35961000000.0,38533000000.0,40458000000.0,43305000000.0,46854000000.0,56177000000.0,59045000000.0,62339000000.0,67151000000.0,69022000000.0,71561000000.0,80638000000.0,83646000000.0,86201000000.0,89912000000.0,94164000000.0,99321000000.0 -Total Assets,16038000000.0,15103000000.0,15163000000.0,15724000000.0,14933000000.0,17895000000.0,19028000000.0,20769000000.0,24188000000.0,39966000000.0,42007000000.0,44130000000.0,46469000000.0,49407000000.0,52075000000.0,55739000000.0,59674000000.0,64961000000.0,68714000000.0,73843000000.0,78998000000.0,84524000000.0,88945000000.0,90291000000.0,92452000000.0,97334000000.0,109477000000.0,117006000000.0,124418000000.0,133376000000.0,138371000000.0,139691000000.0,146437000000.0,159316000000.0,163523000000.0,170609000000.0,169585000000.0,165987000000.0 -Accounts Payable,214000000.0,234000000.0,265000000.0,227000000.0,207000000.0,268000000.0,273000000.0,322000000.0,328000000.0,378000000.0,318000000.0,321000000.0,390000000.0,413000000.0,365000000.0,362000000.0,499000000.0,582000000.0,448000000.0,601000000.0,697000000.0,770000000.0,989000000.0,859000000.0,1092000000.0,1361000000.0,1141000000.0,1215000000.0,1450000000.0,2249000000.0,1541000000.0,1649000000.0,1906000000.0,2424000000.0,1884000000.0,1922000000.0,3104000000.0,5135000000.0 -Deferred Revenue,85000000.0,30000000.0,30000000.0,32000000.0,36000000.0,38000000.0,38000000.0,53000000.0,48000000.0,66000000.0,55000000.0,49000000.0,47000000.0,56000000.0,55000000.0,79000000.0,78000000.0,90000000.0,80000000.0,88000000.0,105000000.0,98000000.0,94000000.0,91000000.0,115000000.0,147000000.0,142000000.0,198000000.0,225000000.0,269000000.0,247000000.0,264000000.0,379000000.0,382000000.0,382000000.0,391000000.0,464000000.0,561000000.0 -Current Debt,372000000.0,365000000.0,338000000.0,316000000.0,288000000.0,239000000.0,201000000.0,173000000.0,149000000.0,114000000.0,73000000.0,39000000.0,17000000.0,7000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,645000000.0,688000000.0,776000000.0,800000000.0,835000000.0,899000000.0,975000000.0,1023000000.0,1040000000.0,1051000000.0,1086000000.0,1127000000.0 -Other Current Liabilities,409000000.0,423000000.0,430000000.0,505000000.0,453000000.0,555000000.0,525000000.0,666000000.0,709000000.0,866000000.0,1300000000.0,1472000000.0,1338000000.0,1449000000.0,1389000000.0,1770000000.0,2018000000.0,2203000000.0,2400000000.0,2626000000.0,2503000000.0,2892000000.0,4003000000.0,3720000000.0,4255000000.0,5509000000.0,7980000000.0,10878000000.0,10877000000.0,11735000000.0,12446000000.0,8496000000.0,8684000000.0,11152000000.0,9411000000.0,11510000000.0,13158000000.0,14312000000.0 -Total Current Liabilities,1080000000.0,1052000000.0,1063000000.0,1080000000.0,984000000.0,1100000000.0,1037000000.0,1214000000.0,1234000000.0,1424000000.0,1746000000.0,1881000000.0,1792000000.0,1925000000.0,1809000000.0,2211000000.0,2595000000.0,2875000000.0,2928000000.0,3315000000.0,3305000000.0,3760000000.0,5086000000.0,4670000000.0,5462000000.0,7017000000.0,9908000000.0,12979000000.0,13328000000.0,15053000000.0,15069000000.0,11308000000.0,11944000000.0,14981000000.0,12717000000.0,14874000000.0,17812000000.0,21135000000.0 -Long-Term Debt,530000000.0,1991000000.0,1920000000.0,1851000000.0,287000000.0,237000000.0,191000000.0,153000000.0,129000000.0,119000000.0,113000000.0,110000000.0,109000000.0,107000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6565000000.0,7122000000.0,8356000000.0,9524000000.0,9509000000.0,9633000000.0,9641000000.0,9631000000.0,10574000000.0,10956000000.0,11554000000.0,12746000000.0 -Other Long-Term Liabilities,254000000.0,305000000.0,356000000.0,444000000.0,614000000.0,1088000000.0,1063000000.0,1056000000.0,1587000000.0,2327000000.0,2647000000.0,2687000000.0,3148000000.0,3157000000.0,3116000000.0,3145000000.0,2964000000.0,2892000000.0,3598000000.0,4047000000.0,4485000000.0,6417000000.0,6239000000.0,6239000000.0,6648000000.0,6190000000.0,6488000000.0,8143000000.0,8735000000.0,7745000000.0,8489000000.0,8303000000.0,7121000000.0,6414000000.0,6575000000.0,6552000000.0,6859000000.0,7227000000.0 -Total Long-Term Liabilities,784000000.0,2296000000.0,2276000000.0,2295000000.0,901000000.0,1325000000.0,1254000000.0,1209000000.0,1716000000.0,2446000000.0,2760000000.0,2797000000.0,3257000000.0,3264000000.0,3116000000.0,3145000000.0,2964000000.0,2892000000.0,3598000000.0,4047000000.0,4485000000.0,6417000000.0,6239000000.0,6239000000.0,6648000000.0,6190000000.0,13053000000.0,15265000000.0,17091000000.0,17269000000.0,17998000000.0,17936000000.0,16762000000.0,16045000000.0,17149000000.0,17508000000.0,18413000000.0,19973000000.0 -Total Liabilities,1864000000.0,3348000000.0,3339000000.0,3375000000.0,1885000000.0,2425000000.0,2291000000.0,2423000000.0,2950000000.0,3870000000.0,4506000000.0,4678000000.0,5049000000.0,5189000000.0,4925000000.0,5356000000.0,5559000000.0,5767000000.0,6526000000.0,7362000000.0,7790000000.0,10177000000.0,11325000000.0,10909000000.0,12110000000.0,13207000000.0,22961000000.0,28244000000.0,30419000000.0,32322000000.0,33067000000.0,29244000000.0,28706000000.0,31026000000.0,29866000000.0,32382000000.0,36225000000.0,41108000000.0 -Common Stock,12585000000.0,10094000000.0,9961000000.0,10167000000.0,10399000000.0,12297000000.0,12921000000.0,13759000000.0,15949000000.0,30225000000.0,31337000000.0,32479000000.0,33574000000.0,34886000000.0,36129000000.0,37405000000.0,38756000000.0,38227000000.0,38639000000.0,39291000000.0,40199000000.0,40584000000.0,41134000000.0,41832000000.0,42352000000.0,42906000000.0,43533000000.0,44277000000.0,45059000000.0,45851000000.0,46688000000.0,47805000000.0,48910000000.0,50018000000.0,51160000000.0,52845000000.0,54334000000.0,55811000000.0 -Retained Earnings,1595000000.0,1659000000.0,1878000000.0,2211000000.0,2636000000.0,3159000000.0,3801000000.0,4592000000.0,5398000000.0,6099000000.0,6611000000.0,7330000000.0,8226000000.0,9787000000.0,11297000000.0,13352000000.0,15731000000.0,21670000000.0,24175000000.0,27560000000.0,31209000000.0,33990000000.0,36780000000.0,38237000000.0,38767000000.0,41981000000.0,43764000000.0,44968000000.0,49789000000.0,55692000000.0,59160000000.0,62784000000.0,68513000000.0,77345000000.0,82343000000.0,85097000000.0,79233000000.0,69761000000.0 -Comprehensive Income,-6000000.0,2000000.0,-15000000.0,-29000000.0,13000000.0,14000000.0,15000000.0,-5000000.0,-109000000.0,-228000000.0,-447000000.0,-357000000.0,-380000000.0,-455000000.0,-276000000.0,-374000000.0,-372000000.0,-703000000.0,-626000000.0,-370000000.0,-200000000.0,-227000000.0,-294000000.0,-687000000.0,-777000000.0,-760000000.0,-781000000.0,-483000000.0,-849000000.0,-489000000.0,-544000000.0,-142000000.0,308000000.0,927000000.0,154000000.0,285000000.0,-207000000.0,-693000000.0 -Shareholders' Equity,14174000000.0,11755000000.0,11824000000.0,12349000000.0,13048000000.0,15470000000.0,16737000000.0,18346000000.0,21238000000.0,36096000000.0,37501000000.0,39452000000.0,41420000000.0,44218000000.0,47150000000.0,50383000000.0,54115000000.0,59194000000.0,62188000000.0,66481000000.0,71208000000.0,74347000000.0,77620000000.0,79382000000.0,80342000000.0,84127000000.0,86516000000.0,88762000000.0,93999000000.0,101054000000.0,105304000000.0,110447000000.0,117731000000.0,128290000000.0,133657000000.0,138227000000.0,133360000000.0,124879000000.0 -Total Liabilities and Equity,16038000000.0,15103000000.0,15163000000.0,15724000000.0,14933000000.0,17895000000.0,19028000000.0,20769000000.0,24188000000.0,39966000000.0,42007000000.0,44130000000.0,46469000000.0,49407000000.0,52075000000.0,55739000000.0,59674000000.0,64961000000.0,68714000000.0,73843000000.0,78998000000.0,84524000000.0,88945000000.0,90291000000.0,92452000000.0,97334000000.0,109477000000.0,117006000000.0,124418000000.0,133376000000.0,138371000000.0,139691000000.0,146437000000.0,159316000000.0,163523000000.0,170609000000.0,169585000000.0,165987000000.0 +0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 +Cash & Equivalents,2478000000.0,2384000000.0,2325000000.0,3001000000.0,3100000000.0,3323000000.0,2998000000.0,4384000000.0,8999000000.0,4315000000.0,3419000000.0,5123000000.0,4308000000.0,4907000000.0,6456000000.0,5108000000.0,6038000000.0,8903000000.0,7104000000.0,6252000000.0,7201000000.0,8079000000.0,12082000000.0,11552000000.0,9637000000.0,10019000000.0,11076000000.0,13877000000.0,15979000000.0,19079000000.0,23618000000.0,21045000000.0,11617000000.0,17576000000.0,19513000000.0,16186000000.0,14496000000.0,16601000000.0 +Short-Term Investments,7974000000.0,7242000000.0,7147000000.0,7251000000.0,6228000000.0,8126000000.0,9631000000.0,9572000000.0,5251000000.0,6884000000.0,8994000000.0,9002000000.0,11526000000.0,13527000000.0,14165000000.0,18185000000.0,20102000000.0,20546000000.0,25202000000.0,29200000000.0,31088000000.0,33632000000.0,31874000000.0,30757000000.0,31569000000.0,31095000000.0,34167000000.0,34719000000.0,36290000000.0,35776000000.0,36671000000.0,37195000000.0,44003000000.0,44378000000.0,44706000000.0,47894000000.0,43579000000.0,31397000000.0 +Cash & Cash Equivalents,10452000000.0,9626000000.0,9472000000.0,10252000000.0,9328000000.0,11449000000.0,12629000000.0,13956000000.0,14250000000.0,11199000000.0,12413000000.0,14125000000.0,15834000000.0,18434000000.0,20621000000.0,23293000000.0,26140000000.0,29449000000.0,32306000000.0,35452000000.0,38289000000.0,41711000000.0,43956000000.0,42309000000.0,41206000000.0,41114000000.0,45243000000.0,48596000000.0,52269000000.0,54855000000.0,60289000000.0,58240000000.0,55620000000.0,61954000000.0,64219000000.0,64080000000.0,58075000000.0,47998000000.0 +Receivables,635000000.0,719000000.0,659000000.0,775000000.0,872000000.0,1109000000.0,1006000000.0,1190000000.0,1363000000.0,1678000000.0,1508000000.0,1815000000.0,2010000000.0,2559000000.0,2348000000.0,2801000000.0,3070000000.0,3993000000.0,3415000000.0,3897000000.0,4424000000.0,5832000000.0,5115000000.0,5590000000.0,6058000000.0,7587000000.0,6475000000.0,7513000000.0,7673000000.0,9518000000.0,7289000000.0,7483000000.0,8024000000.0,11335000000.0,10276000000.0,11698000000.0,12088000000.0,14039000000.0 +Inventory,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Current Assets,1198000000.0,922000000.0,911000000.0,394000000.0,349000000.0,512000000.0,425000000.0,411000000.0,502000000.0,513000000.0,1083000000.0,1011000000.0,1295000000.0,659000000.0,843000000.0,916000000.0,1118000000.0,959000000.0,1209000000.0,1455000000.0,1490000000.0,1020000000.0,1341000000.0,1934000000.0,1883000000.0,1779000000.0,1582000000.0,1852000000.0,2137000000.0,1852000000.0,1771000000.0,2407000000.0,2155000000.0,2381000000.0,2827000000.0,4919000000.0,5258000000.0,4629000000.0 +Total Current Assets,12285000000.0,11267000000.0,11042000000.0,11421000000.0,10549000000.0,13070000000.0,14060000000.0,15557000000.0,16115000000.0,13390000000.0,15004000000.0,16951000000.0,19139000000.0,21652000000.0,23812000000.0,27010000000.0,30328000000.0,34401000000.0,36930000000.0,40804000000.0,44203000000.0,48563000000.0,50412000000.0,49833000000.0,49147000000.0,50480000000.0,53300000000.0,57961000000.0,62079000000.0,66225000000.0,69349000000.0,68130000000.0,65799000000.0,75670000000.0,77322000000.0,80697000000.0,75421000000.0,66666000000.0 +"Property, Plant & Equipment",2289000000.0,2391000000.0,2533000000.0,2577000000.0,2685000000.0,2882000000.0,3074000000.0,3334000000.0,3703000000.0,3967000000.0,4619000000.0,4955000000.0,5335000000.0,5687000000.0,6467000000.0,7104000000.0,7899000000.0,8591000000.0,9462000000.0,10628000000.0,12158000000.0,13721000000.0,16211000000.0,18357000000.0,21112000000.0,24683000000.0,34092000000.0,37271000000.0,40687000000.0,44783000000.0,46486000000.0,48435000000.0,51730000000.0,54981000000.0,57922000000.0,61434000000.0,64789000000.0,69964000000.0 +Long-Term Investments,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,86000000.0,0.0,0.0,6164000000.0,6234000000.0,6342000000.0,6393000000.0,6758000000.0,6775000000.0 +Goodwill and Intangibles,1423000000.0,1388000000.0,1501000000.0,1631000000.0,1609000000.0,1722000000.0,1682000000.0,1672000000.0,3929000000.0,21910000000.0,21779000000.0,21630000000.0,21461000000.0,21272000000.0,21096000000.0,20922000000.0,20787000000.0,20657000000.0,20486000000.0,20315000000.0,20263000000.0,20105000000.0,20003000000.0,19836000000.0,19755000000.0,19595000000.0,19483000000.0,19328000000.0,19191000000.0,19609000000.0,19649000000.0,19888000000.0,19775000000.0,19673000000.0,19561000000.0,19733000000.0,19430000000.0,19831000000.0 +Other Long-Term Assets,41000000.0,57000000.0,87000000.0,95000000.0,90000000.0,221000000.0,212000000.0,206000000.0,441000000.0,699000000.0,605000000.0,594000000.0,534000000.0,796000000.0,700000000.0,703000000.0,660000000.0,1312000000.0,1836000000.0,2096000000.0,2374000000.0,2135000000.0,2319000000.0,2265000000.0,2438000000.0,2576000000.0,2602000000.0,2446000000.0,2461000000.0,2673000000.0,2887000000.0,3238000000.0,2969000000.0,2758000000.0,2376000000.0,2352000000.0,3187000000.0,2751000000.0 +Total Long-Term Assets,3753000000.0,3836000000.0,4121000000.0,4303000000.0,4384000000.0,4825000000.0,4968000000.0,5212000000.0,8073000000.0,26576000000.0,27003000000.0,27179000000.0,27330000000.0,27755000000.0,28263000000.0,28729000000.0,29346000000.0,30560000000.0,31784000000.0,33039000000.0,34795000000.0,35961000000.0,38533000000.0,40458000000.0,43305000000.0,46854000000.0,56177000000.0,59045000000.0,62339000000.0,67151000000.0,69022000000.0,71561000000.0,80638000000.0,83646000000.0,86201000000.0,89912000000.0,94164000000.0,99321000000.0 +Total Assets,16038000000.0,15103000000.0,15163000000.0,15724000000.0,14933000000.0,17895000000.0,19028000000.0,20769000000.0,24188000000.0,39966000000.0,42007000000.0,44130000000.0,46469000000.0,49407000000.0,52075000000.0,55739000000.0,59674000000.0,64961000000.0,68714000000.0,73843000000.0,78998000000.0,84524000000.0,88945000000.0,90291000000.0,92452000000.0,97334000000.0,109477000000.0,117006000000.0,124418000000.0,133376000000.0,138371000000.0,139691000000.0,146437000000.0,159316000000.0,163523000000.0,170609000000.0,169585000000.0,165987000000.0 +Accounts Payable,214000000.0,234000000.0,265000000.0,227000000.0,207000000.0,268000000.0,273000000.0,322000000.0,328000000.0,378000000.0,318000000.0,321000000.0,390000000.0,413000000.0,365000000.0,362000000.0,499000000.0,582000000.0,448000000.0,601000000.0,697000000.0,770000000.0,989000000.0,859000000.0,1092000000.0,1361000000.0,1141000000.0,1215000000.0,1450000000.0,2249000000.0,1541000000.0,1649000000.0,1906000000.0,2424000000.0,1884000000.0,1922000000.0,3104000000.0,5135000000.0 +Deferred Revenue,85000000.0,30000000.0,30000000.0,32000000.0,36000000.0,38000000.0,38000000.0,53000000.0,48000000.0,66000000.0,55000000.0,49000000.0,47000000.0,56000000.0,55000000.0,79000000.0,78000000.0,90000000.0,80000000.0,88000000.0,105000000.0,98000000.0,94000000.0,91000000.0,115000000.0,147000000.0,142000000.0,198000000.0,225000000.0,269000000.0,247000000.0,264000000.0,379000000.0,382000000.0,382000000.0,391000000.0,464000000.0,561000000.0 +Current Debt,372000000.0,365000000.0,338000000.0,316000000.0,288000000.0,239000000.0,201000000.0,173000000.0,149000000.0,114000000.0,73000000.0,39000000.0,17000000.0,7000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,645000000.0,688000000.0,776000000.0,800000000.0,835000000.0,899000000.0,975000000.0,1023000000.0,1040000000.0,1051000000.0,1086000000.0,1127000000.0 +Other Current Liabilities,409000000.0,423000000.0,430000000.0,505000000.0,453000000.0,555000000.0,525000000.0,666000000.0,709000000.0,866000000.0,1300000000.0,1472000000.0,1338000000.0,1449000000.0,1389000000.0,1770000000.0,2018000000.0,2203000000.0,2400000000.0,2626000000.0,2503000000.0,2892000000.0,4003000000.0,3720000000.0,4255000000.0,5509000000.0,7980000000.0,10878000000.0,10877000000.0,11735000000.0,12446000000.0,8496000000.0,8684000000.0,11152000000.0,9411000000.0,11510000000.0,13158000000.0,14312000000.0 +Total Current Liabilities,1080000000.0,1052000000.0,1063000000.0,1080000000.0,984000000.0,1100000000.0,1037000000.0,1214000000.0,1234000000.0,1424000000.0,1746000000.0,1881000000.0,1792000000.0,1925000000.0,1809000000.0,2211000000.0,2595000000.0,2875000000.0,2928000000.0,3315000000.0,3305000000.0,3760000000.0,5086000000.0,4670000000.0,5462000000.0,7017000000.0,9908000000.0,12979000000.0,13328000000.0,15053000000.0,15069000000.0,11308000000.0,11944000000.0,14981000000.0,12717000000.0,14874000000.0,17812000000.0,21135000000.0 +Long-Term Debt,530000000.0,1991000000.0,1920000000.0,1851000000.0,287000000.0,237000000.0,191000000.0,153000000.0,129000000.0,119000000.0,113000000.0,110000000.0,109000000.0,107000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6565000000.0,7122000000.0,8356000000.0,9524000000.0,9509000000.0,9633000000.0,9641000000.0,9631000000.0,10574000000.0,10956000000.0,11554000000.0,12746000000.0 +Other Long-Term Liabilities,254000000.0,305000000.0,356000000.0,444000000.0,614000000.0,1088000000.0,1063000000.0,1056000000.0,1587000000.0,2327000000.0,2647000000.0,2687000000.0,3148000000.0,3157000000.0,3116000000.0,3145000000.0,2964000000.0,2892000000.0,3598000000.0,4047000000.0,4485000000.0,6417000000.0,6239000000.0,6239000000.0,6648000000.0,6190000000.0,6488000000.0,8143000000.0,8735000000.0,7745000000.0,8489000000.0,8303000000.0,7121000000.0,6414000000.0,6575000000.0,6552000000.0,6859000000.0,7227000000.0 +Total Long-Term Liabilities,784000000.0,2296000000.0,2276000000.0,2295000000.0,901000000.0,1325000000.0,1254000000.0,1209000000.0,1716000000.0,2446000000.0,2760000000.0,2797000000.0,3257000000.0,3264000000.0,3116000000.0,3145000000.0,2964000000.0,2892000000.0,3598000000.0,4047000000.0,4485000000.0,6417000000.0,6239000000.0,6239000000.0,6648000000.0,6190000000.0,13053000000.0,15265000000.0,17091000000.0,17269000000.0,17998000000.0,17936000000.0,16762000000.0,16045000000.0,17149000000.0,17508000000.0,18413000000.0,19973000000.0 +Total Liabilities,1864000000.0,3348000000.0,3339000000.0,3375000000.0,1885000000.0,2425000000.0,2291000000.0,2423000000.0,2950000000.0,3870000000.0,4506000000.0,4678000000.0,5049000000.0,5189000000.0,4925000000.0,5356000000.0,5559000000.0,5767000000.0,6526000000.0,7362000000.0,7790000000.0,10177000000.0,11325000000.0,10909000000.0,12110000000.0,13207000000.0,22961000000.0,28244000000.0,30419000000.0,32322000000.0,33067000000.0,29244000000.0,28706000000.0,31026000000.0,29866000000.0,32382000000.0,36225000000.0,41108000000.0 +Common Stock,12585000000.0,10094000000.0,9961000000.0,10167000000.0,10399000000.0,12297000000.0,12921000000.0,13759000000.0,15949000000.0,30225000000.0,31337000000.0,32479000000.0,33574000000.0,34886000000.0,36129000000.0,37405000000.0,38756000000.0,38227000000.0,38639000000.0,39291000000.0,40199000000.0,40584000000.0,41134000000.0,41832000000.0,42352000000.0,42906000000.0,43533000000.0,44277000000.0,45059000000.0,45851000000.0,46688000000.0,47805000000.0,48910000000.0,50018000000.0,51160000000.0,52845000000.0,54334000000.0,55811000000.0 +Retained Earnings,1595000000.0,1659000000.0,1878000000.0,2211000000.0,2636000000.0,3159000000.0,3801000000.0,4592000000.0,5398000000.0,6099000000.0,6611000000.0,7330000000.0,8226000000.0,9787000000.0,11297000000.0,13352000000.0,15731000000.0,21670000000.0,24175000000.0,27560000000.0,31209000000.0,33990000000.0,36780000000.0,38237000000.0,38767000000.0,41981000000.0,43764000000.0,44968000000.0,49789000000.0,55692000000.0,59160000000.0,62784000000.0,68513000000.0,77345000000.0,82343000000.0,85097000000.0,79233000000.0,69761000000.0 +Comprehensive Income,-6000000.0,2000000.0,-15000000.0,-29000000.0,13000000.0,14000000.0,15000000.0,-5000000.0,-109000000.0,-228000000.0,-447000000.0,-357000000.0,-380000000.0,-455000000.0,-276000000.0,-374000000.0,-372000000.0,-703000000.0,-626000000.0,-370000000.0,-200000000.0,-227000000.0,-294000000.0,-687000000.0,-777000000.0,-760000000.0,-781000000.0,-483000000.0,-849000000.0,-489000000.0,-544000000.0,-142000000.0,308000000.0,927000000.0,154000000.0,285000000.0,-207000000.0,-693000000.0 +Shareholders' Equity,14174000000.0,11755000000.0,11824000000.0,12349000000.0,13048000000.0,15470000000.0,16737000000.0,18346000000.0,21238000000.0,36096000000.0,37501000000.0,39452000000.0,41420000000.0,44218000000.0,47150000000.0,50383000000.0,54115000000.0,59194000000.0,62188000000.0,66481000000.0,71208000000.0,74347000000.0,77620000000.0,79382000000.0,80342000000.0,84127000000.0,86516000000.0,88762000000.0,93999000000.0,101054000000.0,105304000000.0,110447000000.0,117731000000.0,128290000000.0,133657000000.0,138227000000.0,133360000000.0,124879000000.0 +Total Liabilities and Equity,16038000000.0,15103000000.0,15163000000.0,15724000000.0,14933000000.0,17895000000.0,19028000000.0,20769000000.0,24188000000.0,39966000000.0,42007000000.0,44130000000.0,46469000000.0,49407000000.0,52075000000.0,55739000000.0,59674000000.0,64961000000.0,68714000000.0,73843000000.0,78998000000.0,84524000000.0,88945000000.0,90291000000.0,92452000000.0,97334000000.0,109477000000.0,117006000000.0,124418000000.0,133376000000.0,138371000000.0,139691000000.0,146437000000.0,159316000000.0,163523000000.0,170609000000.0,169585000000.0,165987000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_1.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_1.csv index 5981e48a63c..d11dd4dc09b 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_1.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_1.csv @@ -1,28 +1,28 @@ -0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 -Cash & Equivalents,113240000.0,226140000.0,236180000.0,754480000.0,804490000.0,852320000.0,2402060000.0,2682160000.0,2379830000.0,1917090000.0,1523920000.0,1170450000.0,1452390000.0,1219540000.0,1489570000.0,3317920000.0,3175250000.0,3498740000.0,4095540000.0,3154290000.0,3668210000.0,3523240000.0,2785870000.0,2383250000.0,3126130000.0,3879000000.0,2329120000.0,5082750000.0,5571000000.0,6268000000.0,8080000000.0,8615000000.0,14531000000.0,19384000000.0,17141000000.0,16229000000.0,16065000000.0,17576000000.0 -Short-Term Investments,0.0,0.0,0.0,0.0,0.0,3010000.0,189110000.0,11710000.0,17330000.0,17950000.0,20690000.0,20590000.0,25220000.0,0.0,23980000.0,24530000.0,23710000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30000000.0,131000000.0 -Cash & Cash Equivalents,113240000.0,226140000.0,236180000.0,754480000.0,804490000.0,855340000.0,2591170000.0,2693870000.0,2397160000.0,1935030000.0,1544620000.0,1191040000.0,1477610000.0,1219540000.0,1513550000.0,3342450000.0,3198960000.0,3498740000.0,4095540000.0,3154290000.0,3668210000.0,3523240000.0,2785870000.0,2383250000.0,3126130000.0,3879000000.0,2329120000.0,5082750000.0,5571000000.0,6268000000.0,8080000000.0,8615000000.0,14531000000.0,19384000000.0,17141000000.0,16229000000.0,16095000000.0,17707000000.0 -Receivables,9160000.0,26840000.0,46140000.0,113540000.0,47580000.0,49110000.0,72380000.0,96610000.0,156890000.0,226600000.0,200050000.0,138650000.0,119960000.0,168970000.0,318060000.0,178590000.0,326900000.0,499140000.0,440350000.0,453540000.0,607730000.0,515380000.0,652850000.0,569870000.0,1155000000.0,949000000.0,1046950000.0,1147100000.0,1128000000.0,1324000000.0,1274000000.0,1485000000.0,1757000000.0,1886000000.0,1890000000.0,2129000000.0,1962000000.0,1913000000.0 -Inventory,159050000.0,268500000.0,237620000.0,254890000.0,347550000.0,340360000.0,450730000.0,596930000.0,752490000.0,953680000.0,1054839999.9999999,1212280000.0,1293720000.0,1277840000.0,1301960000.0,1609610000.0,1604570000.0,2067449999.9999998,2220340000.0,2438110000.0,2471380000.0,2263540000.0,2565830000.0,3324640000.0,3314130000.0,3113000000.0,3836850000.0,3382360000.0,3581000000.0,3552000000.0,4494000000.0,4018000000.0,4218000000.0,4101000000.0,4132000000.0,4733000000.0,5199000000.0,5757000000.0 -Other Current Assets,3090000.0,3280000.0,6060000.0,6630000.0,19150000.0,21140000.0,41770000.0,54290000.0,56380000.0,64760000.00000001,121910000.0,86660000.0,107500000.0,115670000.0,105970000.0,73060000.0,41980000.0,194470000.0,271670000.0,313500000.0,321410000.0,268370000.0,379380000.0,422030000.0,325230000.0,366000000.0,464910000.0,569750000.0,660000000.0,959000000.0,1045000000.0,1218000000.0,1238000000.0,1346000000.0,1542000000.0,1602000000.0,1746000000.0,1723000000.0 -Total Current Assets,284540000.0,524770000.0,525990000.0,1129540000.0,1218770000.0,1265940000.0,3156050000.0,3441690000.0,3362910000.0,3180070000.0,2921420000.0,2628620000.0,2998800000.0,2782010000.0,3239540000.0,5203710000.0,5172410000.0,6259800000.0,7027890000.0,6359440000.0,7068730000.0,6570520000.0,6383920000.0,6699800000.0,7920490000.0,8307000000.0,7677820000.0,10181950000.0,10940000000.0,12103000000.0,14893000000.0,15336000000.0,21744000000.0,26717000000.0,24705000000.0,24693000000.0,25002000000.0,27100000000.0 -"Property, Plant & Equipment",498040000.0,562300000.0,591060000.0,727050000.0,923310000.0,1120920000.0,1301120000.0,1567050000.0,2022070000.0,2596010000.0,3136250000.0,3766260000.0,4464540000.0,5194740000.0,5837220000.0,6526980000.0,7258350000.0,15036920000.0,16555140000.0,18218550000.0,19516600000.0,20491620000.0,19180720000.0,19591430000.0,19733970000.0,19691000000.0,19318100000.0,19600980000.0,19845000000.0,20199000000.0,20468000000.0,20876000000.0,21990000000.0,23375000000.0,24844000000.0,27030000000.0,29248000000.0,31176000000.0 -Long-Term Investments,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31520000.0,0.0,0.0,0.0,774470000.0,816570000.0,831110000.0,872420000.0,898370000.0,883600000.0,834830000.0,819730000.0,820000000.0,766860000.0,765860000.0,653000000.0,0.0,0.0,0.0,0.0,0.0,1331000000.0,1311000000.0,1260000000.0,1260000000.0 -Goodwill and Intangibles,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12820000.0,0.0,0.0,0.0,376150000.0,429590000.0,424610000.0,417470000.0,421740000.0,407710000.0,364690000.0,356700000.0,350000000.0,347880000.0,480830000.0,537000000.0,537000000.0,516000000.0,508000000.0,521000000.0,520000000.0,505000000.0,486000000.0,470000000.0,457000000.0 -Other Long-Term Assets,26600000.0,27120000.0,26730000.0,31260000.0,24140000.0,30070000.0,43250000.0,45720000.0,52550000.0,54580000.0,62360000.0,73300000.0,84170000.0,46860000.0,114940000.0,138270000.0,161640000.0,216750000.0,224540000.0,209990000.0,231850000.0,273120000.0,415480000.0,419250000.0,431820000.0,572000000.0,801870000.0,842980000.0,820000000.0,1470000000.0,1373000000.0,1415000000.0,1436000000.0,1536000000.0,1587000000.0,1626000000.0,1854000000.0,2138000000.0 -Total Long-Term Assets,524640000.0,589420000.0,617790000.0,758300000.0,947440000.0,1150990000.0,1344360000.0,1612770000.0,2074620000.0,2650590000.0,3198610000.0,3839560000.0,4548700000.0,5285930000.0,5952160000.0,6665250000.0,7419990000.0,16404279999.999998,18025840000.0,19684260000.0,21038340000.0,22084850000.0,20887510000.0,21210200000.0,21342220000.0,21433000000.0,21234700000.0,21690650000.0,21855000000.0,22206000000.0,22357000000.0,22799000000.0,23947000000.0,25431000000.0,28267000000.0,30453000000.0,32832000000.0,35031000000.0 -Total Assets,809180000.0,1114190000.0,1143780000.0,1887840000.0,2166210000.0,2416930000.0,4500410000.0,5054460000.0,5437530000.0,5830670000.0,6120030000.0,6468190000.0,7547500000.0,8067940000.0,9191700000.0,11868950000.0,12592400000.0,22664080000.0,25053730000.0,26043710000.0,28107070000.0,28655370000.0,27271430000.0,27910000000.0,29262710000.0,29740000000.0,28912520000.0,31872600000.0,32795000000.0,34309000000.0,37250000000.0,38135000000.0,45691000000.0,52148000000.0,52972000000.0,55146000000.0,57834000000.0,62131000000.0 -Accounts Payable,162030000.0,303380000.0,304200000.0,262230000.00000003,302440000.0,303970000.0,375780000.0,443550000.0,649360000.0,777950000.0,732330000.0,771640000.0,824860000.0,916150000.0,1013490000.0,1114880000.0,1606280000.0,1860340000.0,2075330000.0,2359320000.0,2385780000.0,2390250000.0,2603500000.0,3030490000.0,3596980000.0,3405000000.0,3248830000.0,3133590000.0,3468000000.0,3771000000.0,3970000000.0,3638000000.0,4958000000.0,6051000000.0,6648000000.0,7558000000.0,8260000000.0,10025000000.0 -Deferred Revenue,5380000.0,4970000.0,9020000.0,98070000.0,195040000.0,273060000.0,323560000.0,374420000.0,415890000.0,483920000.0,539320000.0,623160000.0,710380000.0,870070000.0,1013620000.0,1092070000.0,1207100000.0,1614920000.0,1796570000.0,1948980000.0,2034600000.0,2193050000.0,1354720000.0,1372140000.0,1521050000.0,1621000000.0,1920150000.0,2065340000.0000002,2185000000.0,2370000000.0,2385000000.0,2328000000.0,2491000000.0,2742000000.0,2886000000.0,3011000000.0,3166000000.0,3499000000.0 -Current Debt,54030000.0,55210000.0,56290000.0,5700000.0,588470000.0,7900000.0,598270000.0,603410000.0,607220000.0,611100000.0,630330000.0,638280000.0,651810000.0,627930000.0,635290000.0,626830000.0,260769999.99999997,1150150000.0,1003310000.0,816530000.0,424220000.0,896550000.0,1998030000.0,2103190000.0,2106540000.0,2568000000.0,1705710000.0,1791090000.0,2030000000.0,1785000000.0,3217000000.0,3679000000.0,3126000000.0,2132000000.0,1819000000.0,1530000000.0,1716000000.0,1589000000.0 -Other Current Liabilities,164370000.0,175560000.0,166100000.0,120550000.0,83510000.0,90230000.0,115860000.0,154470000.0,167310000.0,234200000.0,290390000.0,351320000.0,366720000.0,396890000.0,525309999.99999994,932630000.0,1008230000.0,1201600000.0,1370220000.0,1421530000.0,1624330000.0,2194820000.0,2694120000.0,2635540000.0,2550760000.0,2399000000.0,2368110000.0,2598770000.0,2463000000.0,2741000000.0,2414000000.0,2625000000.0,2727000000.0,3323000000.0,3524000000.0,4272000000.0,4909000000.0,4592000000.0 -Total Current Liabilities,385810000.0,539110000.0,535620000.0,486550000.0,1169460000.0,675160000.0,1413470000.0,1575850000.0,1839780000.0,2107170000.0,2192380000.0,2384390000.0,2553770000.0,2811040000.0,3187700000.0,3766400000.0,4082390000.0,5827010000.0,6245440000.0,6546360000.0,6468940000.0,7674670000.0,8650360000.0,9141360000.0,9775320000.0,9993000000.0,9242800000.0,9588770000.0,10146000000.0,10667000000.0,11986000000.0,12270000000.0,13302000000.0,14248000000.0,14877000000.0,16371000000.0,18051000000.0,19705000000.0 -Long-Term Debt,420490000.0,411460000.0,399250000.0,587990000.0,88430000.0,598970000.0,1602480000.0,1847210000.0,1861600000.0,1876980000.0,1954210000.0,2049620000.0,2027580000.0,2068380000.0,2526960000.0,2657150000.0,2454690000.0,5978280000.0,7166240000.0,7127090000.0,9584550000.0,9418390000.0,8763730000.0,9513390000.0,9672610000.0,9404000000.0,9787950000.0,11234400000.0,11313000000.0,11634000000.0,10726000000.0,10460000000.0,10607000000.0,9607000000.0,9053000000.0,7871000000.0,6438000000.0,5245000000.0 -Other Long-Term Liabilities,30760000.0,38920000.0,40330000.0,183880000.0,266660000.00000003,475680000.0,502460000.0,612990000.0,715900000.0,876610000.0,1093160000.0,1267970000.0,1605310000.0,2057540000.0,2464060000.0,2887960000.0,3363560000.0,4944880000.0,5473680000.0,5786450000.0,5875560000.0,5929920000.0,4136930000.0000005,3988140000.0,3961210000.0,4030000000.0,3843870000.0,3898960000.0,3854000000.0,3898000000.0,3806000000.0,4024000000.0,4234000000.0,4563000000.0,4577000000.0,4654000000.0,4851000000.0,5598000000.0 -Total Long-Term Liabilities,451250000.0,450380000.0,439570000.0,771870000.0,355090000.0,1074650000.0,2104940000.0,2460200000.0,2577500000.0,2753600000.0,3047380000.0,3317590000.0,3632890000.0,4125920000.0,4991010000.0,5545110000.0,5818250000.0,10923160000.0,12639910000.0,12913550000.0,15460120000.0,15348310000.0,12900660000.0,13501530000.0,13633820000.0,13434000000.0,13631820000.0,15133360000.0,15167000000.0,15532000000.0,14532000000.0,14484000000.0,14841000000.0,14170000000.0,13630000000.0,12525000000.0,11289000000.0,10843000000.0 -Total Liabilities,837050000.0,989490000.0,975200000.0,1258420000.0,1524550000.0,1749810000.0,3518410000.0,4036050000.0,4417280000.0,4860760000.0,5239760000.0,5701980000.0,6186660000.0,6936950000.0,8178710000.0,9311510000.0,9900640000.0,16750169999.999998,18885350000.0,19459900000.0,21929060000.0,23022980000.0,21551020000.0,22642890000.0,23409140000.0,23427000000.0,22874620000.0,24722140000.0,25313000000.0,26199000000.0,26518000000.0,26754000000.0,28143000000.0,28418000000.0,28507000000.0,28896000000.0,29340000000.0,30548000000.0 -Common Stock,947800000.0,1190310000.0,1222940000.0,1714280000.0,1687520000.0,1806740000.0,2101480000.0,2203660000.0,2284140000.0,2345390000.0,2429800000.0,2502810000.0,3340570000.0,3409580000.0,3561390000.0,5383880000.0,5531080000.0,7773890000.0,8351680000.0,8774380000.0,8989190000.0,9178190000.0,9419070000.0,9656710000.0,9957880000.0,10249000000.0,10563920000.0,12052640000.0,12348000000.0,12737000000.0,15390000000.0,15895000000.0,21575000000.0,27261000000.0,27624000000.0,28206000000.0,28923000000.0,29804000000.0 -Retained Earnings,-975670000.0,-1065609999.9999999,-1054359999.9999999,-1084860000.0,-1123360000.0,-1139620000.0,-1189420000.0,-1251330000.0,-1326040000.0,-1433660000.0,-1587840000.0,-1772070000.0,-2001930000.0,-2322320000.0,-2604590000.0,-2897780000.0,-2875900000.0,-2997240000.0,-3343190000.0,-3679580000.0,-4298960000.0,-4974300000.0,-5051290000.0,-5768830000.0,-5457320000.0,-5318000000.0,-5923310000.0,-6331640000.0,-6188000000.0,-6083000000.0,-6104000000.0,-6000000000.0,-5669000000.0,-5399000000.0,-4750000000.0,-3608000000.0,-1990000000.0,331000000.0 -Comprehensive Income,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-20000.0,-15970000.0,-14800000.0,-23990000.0,-3560000.0,13570000.0,34190000.0,25310000.0,-23740000.0,-20770000.0,10960000.0,21250000.0,33350000.0,82920000.0,18550000.0,8270000.0,-8000000.0,-35020000.0,-5610000.0,-120000000.0,-36000000.0,-113000000.0,-40000000.0,125000000.0,363000000.0,143000000.0,206000000.0,120000000.0,54000000.0 -Shareholders' Equity,-27880000.0,124700000.0,168580000.0,629430000.0,564170000.0,667120000.0,912060000.0,952330000.0,958090000.0,911710000.0,826000000.0,715930000.0,1314660000.0,1083700000.0,970370000.0,2520290000.0,2680490000.0,4752910000.0,4987720000.0,5105750000.0,4711480000.0,4237240000.0,4450700000.0,3906420000.0,4508840000.0,4923000000.0,4605600000.0,5715390000.0,6040000000.0,6618000000.0,9173000000.0,9855000000.0,16031000000.0,22225000000.0,23017000000.0,24804000000.0,27053000000.0,30189000000.0 -Total Liabilities and Equity,809180000.0,1114190000.0,1143780000.0,1887840000.0,2088710000.0,2416930000.0,4430470000.0,4988380000.0,5375370000.0,5772470000.0,6065750000.0,6417910000.0,7501320000.0,8020650000.0,9149080000.0,11831810000.0,12581130000.0,21503080000.0,23873070000.0,24565650000.0,26640540000.0,27260220000.0,26001720000.0,26549310000.0,27917980000.0,28350000000.0,27480210000.0,30437530000.0,31353000000.0,32817000000.0,35691000000.0,36609000000.0,44174000000.0,50643000000.0,51524000000.0,53700000000.0,56393000000.0,60737000000.0 +0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 +Cash & Equivalents,113240000.0,226140000.0,236180000.0,754480000.0,804490000.0,852320000.0,2402060000.0,2682160000.0,2379830000.0,1917090000.0,1523920000.0,1170450000.0,1452390000.0,1219540000.0,1489570000.0,3317920000.0,3175250000.0,3498740000.0,4095540000.0,3154290000.0,3668210000.0,3523240000.0,2785870000.0,2383250000.0,3126130000.0,3879000000.0,2329120000.0,5082750000.0,5571000000.0,6268000000.0,8080000000.0,8615000000.0,14531000000.0,19384000000.0,17141000000.0,16229000000.0,16065000000.0,17576000000.0 +Short-Term Investments,0.0,0.0,0.0,0.0,0.0,3010000.0,189110000.0,11710000.0,17330000.0,17950000.0,20690000.0,20590000.0,25220000.0,0.0,23980000.0,24530000.0,23710000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30000000.0,131000000.0 +Cash & Cash Equivalents,113240000.0,226140000.0,236180000.0,754480000.0,804490000.0,855340000.0,2591170000.0,2693870000.0,2397160000.0,1935030000.0,1544620000.0,1191040000.0,1477610000.0,1219540000.0,1513550000.0,3342450000.0,3198960000.0,3498740000.0,4095540000.0,3154290000.0,3668210000.0,3523240000.0,2785870000.0,2383250000.0,3126130000.0,3879000000.0,2329120000.0,5082750000.0,5571000000.0,6268000000.0,8080000000.0,8615000000.0,14531000000.0,19384000000.0,17141000000.0,16229000000.0,16095000000.0,17707000000.0 +Receivables,9160000.0,26840000.0,46140000.0,113540000.0,47580000.0,49110000.0,72380000.0,96610000.0,156890000.0,226600000.0,200050000.0,138650000.0,119960000.0,168970000.0,318060000.0,178590000.0,326900000.0,499140000.0,440350000.0,453540000.0,607730000.0,515380000.0,652850000.0,569870000.0,1155000000.0,949000000.0,1046950000.0,1147100000.0,1128000000.0,1324000000.0,1274000000.0,1485000000.0,1757000000.0,1886000000.0,1890000000.0,2129000000.0,1962000000.0,1913000000.0 +Inventory,159050000.0,268500000.0,237620000.0,254890000.0,347550000.0,340360000.0,450730000.0,596930000.0,752490000.0,953680000.0,1054839999.9999999,1212280000.0,1293720000.0,1277840000.0,1301960000.0,1609610000.0,1604570000.0,2067449999.9999998,2220340000.0,2438110000.0,2471380000.0,2263540000.0,2565830000.0,3324640000.0,3314130000.0,3113000000.0,3836850000.0,3382360000.0,3581000000.0,3552000000.0,4494000000.0,4018000000.0,4218000000.0,4101000000.0,4132000000.0,4733000000.0,5199000000.0,5757000000.0 +Other Current Assets,3090000.0,3280000.0,6060000.0,6630000.0,19150000.0,21140000.0,41770000.0,54290000.0,56380000.0,64760000.00000001,121910000.0,86660000.0,107500000.0,115670000.0,105970000.0,73060000.0,41980000.0,194470000.0,271670000.0,313500000.0,321410000.0,268370000.0,379380000.0,422030000.0,325230000.0,366000000.0,464910000.0,569750000.0,660000000.0,959000000.0,1045000000.0,1218000000.0,1238000000.0,1346000000.0,1542000000.0,1602000000.0,1746000000.0,1723000000.0 +Total Current Assets,284540000.0,524770000.0,525990000.0,1129540000.0,1218770000.0,1265940000.0,3156050000.0,3441690000.0,3362910000.0,3180070000.0,2921420000.0,2628620000.0,2998800000.0,2782010000.0,3239540000.0,5203710000.0,5172410000.0,6259800000.0,7027890000.0,6359440000.0,7068730000.0,6570520000.0,6383920000.0,6699800000.0,7920490000.0,8307000000.0,7677820000.0,10181950000.0,10940000000.0,12103000000.0,14893000000.0,15336000000.0,21744000000.0,26717000000.0,24705000000.0,24693000000.0,25002000000.0,27100000000.0 +"Property, Plant & Equipment",498040000.0,562300000.0,591060000.0,727050000.0,923310000.0,1120920000.0,1301120000.0,1567050000.0,2022070000.0,2596010000.0,3136250000.0,3766260000.0,4464540000.0,5194740000.0,5837220000.0,6526980000.0,7258350000.0,15036920000.0,16555140000.0,18218550000.0,19516600000.0,20491620000.0,19180720000.0,19591430000.0,19733970000.0,19691000000.0,19318100000.0,19600980000.0,19845000000.0,20199000000.0,20468000000.0,20876000000.0,21990000000.0,23375000000.0,24844000000.0,27030000000.0,29248000000.0,31176000000.0 +Long-Term Investments,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,31520000.0,0.0,0.0,0.0,774470000.0,816570000.0,831110000.0,872420000.0,898370000.0,883600000.0,834830000.0,819730000.0,820000000.0,766860000.0,765860000.0,653000000.0,0.0,0.0,0.0,0.0,0.0,1331000000.0,1311000000.0,1260000000.0,1260000000.0 +Goodwill and Intangibles,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12820000.0,0.0,0.0,0.0,376150000.0,429590000.0,424610000.0,417470000.0,421740000.0,407710000.0,364690000.0,356700000.0,350000000.0,347880000.0,480830000.0,537000000.0,537000000.0,516000000.0,508000000.0,521000000.0,520000000.0,505000000.0,486000000.0,470000000.0,457000000.0 +Other Long-Term Assets,26600000.0,27120000.0,26730000.0,31260000.0,24140000.0,30070000.0,43250000.0,45720000.0,52550000.0,54580000.0,62360000.0,73300000.0,84170000.0,46860000.0,114940000.0,138270000.0,161640000.0,216750000.0,224540000.0,209990000.0,231850000.0,273120000.0,415480000.0,419250000.0,431820000.0,572000000.0,801870000.0,842980000.0,820000000.0,1470000000.0,1373000000.0,1415000000.0,1436000000.0,1536000000.0,1587000000.0,1626000000.0,1854000000.0,2138000000.0 +Total Long-Term Assets,524640000.0,589420000.0,617790000.0,758300000.0,947440000.0,1150990000.0,1344360000.0,1612770000.0,2074620000.0,2650590000.0,3198610000.0,3839560000.0,4548700000.0,5285930000.0,5952160000.0,6665250000.0,7419990000.0,16404279999.999998,18025840000.0,19684260000.0,21038340000.0,22084850000.0,20887510000.0,21210200000.0,21342220000.0,21433000000.0,21234700000.0,21690650000.0,21855000000.0,22206000000.0,22357000000.0,22799000000.0,23947000000.0,25431000000.0,28267000000.0,30453000000.0,32832000000.0,35031000000.0 +Total Assets,809180000.0,1114190000.0,1143780000.0,1887840000.0,2166210000.0,2416930000.0,4500410000.0,5054460000.0,5437530000.0,5830670000.0,6120030000.0,6468190000.0,7547500000.0,8067940000.0,9191700000.0,11868950000.0,12592400000.0,22664080000.0,25053730000.0,26043710000.0,28107070000.0,28655370000.0,27271430000.0,27910000000.0,29262710000.0,29740000000.0,28912520000.0,31872600000.0,32795000000.0,34309000000.0,37250000000.0,38135000000.0,45691000000.0,52148000000.0,52972000000.0,55146000000.0,57834000000.0,62131000000.0 +Accounts Payable,162030000.0,303380000.0,304200000.0,262230000.00000003,302440000.0,303970000.0,375780000.0,443550000.0,649360000.0,777950000.0,732330000.0,771640000.0,824860000.0,916150000.0,1013490000.0,1114880000.0,1606280000.0,1860340000.0,2075330000.0,2359320000.0,2385780000.0,2390250000.0,2603500000.0,3030490000.0,3596980000.0,3405000000.0,3248830000.0,3133590000.0,3468000000.0,3771000000.0,3970000000.0,3638000000.0,4958000000.0,6051000000.0,6648000000.0,7558000000.0,8260000000.0,10025000000.0 +Deferred Revenue,5380000.0,4970000.0,9020000.0,98070000.0,195040000.0,273060000.0,323560000.0,374420000.0,415890000.0,483920000.0,539320000.0,623160000.0,710380000.0,870070000.0,1013620000.0,1092070000.0,1207100000.0,1614920000.0,1796570000.0,1948980000.0,2034600000.0,2193050000.0,1354720000.0,1372140000.0,1521050000.0,1621000000.0,1920150000.0,2065340000.0000002,2185000000.0,2370000000.0,2385000000.0,2328000000.0,2491000000.0,2742000000.0,2886000000.0,3011000000.0,3166000000.0,3499000000.0 +Current Debt,54030000.0,55210000.0,56290000.0,5700000.0,588470000.0,7900000.0,598270000.0,603410000.0,607220000.0,611100000.0,630330000.0,638280000.0,651810000.0,627930000.0,635290000.0,626830000.0,260769999.99999997,1150150000.0,1003310000.0,816530000.0,424220000.0,896550000.0,1998030000.0,2103190000.0,2106540000.0,2568000000.0,1705710000.0,1791090000.0,2030000000.0,1785000000.0,3217000000.0,3679000000.0,3126000000.0,2132000000.0,1819000000.0,1530000000.0,1716000000.0,1589000000.0 +Other Current Liabilities,164370000.0,175560000.0,166100000.0,120550000.0,83510000.0,90230000.0,115860000.0,154470000.0,167310000.0,234200000.0,290390000.0,351320000.0,366720000.0,396890000.0,525309999.99999994,932630000.0,1008230000.0,1201600000.0,1370220000.0,1421530000.0,1624330000.0,2194820000.0,2694120000.0,2635540000.0,2550760000.0,2399000000.0,2368110000.0,2598770000.0,2463000000.0,2741000000.0,2414000000.0,2625000000.0,2727000000.0,3323000000.0,3524000000.0,4272000000.0,4909000000.0,4592000000.0 +Total Current Liabilities,385810000.0,539110000.0,535620000.0,486550000.0,1169460000.0,675160000.0,1413470000.0,1575850000.0,1839780000.0,2107170000.0,2192380000.0,2384390000.0,2553770000.0,2811040000.0,3187700000.0,3766400000.0,4082390000.0,5827010000.0,6245440000.0,6546360000.0,6468940000.0,7674670000.0,8650360000.0,9141360000.0,9775320000.0,9993000000.0,9242800000.0,9588770000.0,10146000000.0,10667000000.0,11986000000.0,12270000000.0,13302000000.0,14248000000.0,14877000000.0,16371000000.0,18051000000.0,19705000000.0 +Long-Term Debt,420490000.0,411460000.0,399250000.0,587990000.0,88430000.0,598970000.0,1602480000.0,1847210000.0,1861600000.0,1876980000.0,1954210000.0,2049620000.0,2027580000.0,2068380000.0,2526960000.0,2657150000.0,2454690000.0,5978280000.0,7166240000.0,7127090000.0,9584550000.0,9418390000.0,8763730000.0,9513390000.0,9672610000.0,9404000000.0,9787950000.0,11234400000.0,11313000000.0,11634000000.0,10726000000.0,10460000000.0,10607000000.0,9607000000.0,9053000000.0,7871000000.0,6438000000.0,5245000000.0 +Other Long-Term Liabilities,30760000.0,38920000.0,40330000.0,183880000.0,266660000.00000003,475680000.0,502460000.0,612990000.0,715900000.0,876610000.0,1093160000.0,1267970000.0,1605310000.0,2057540000.0,2464060000.0,2887960000.0,3363560000.0,4944880000.0,5473680000.0,5786450000.0,5875560000.0,5929920000.0,4136930000.0000005,3988140000.0,3961210000.0,4030000000.0,3843870000.0,3898960000.0,3854000000.0,3898000000.0,3806000000.0,4024000000.0,4234000000.0,4563000000.0,4577000000.0,4654000000.0,4851000000.0,5598000000.0 +Total Long-Term Liabilities,451250000.0,450380000.0,439570000.0,771870000.0,355090000.0,1074650000.0,2104940000.0,2460200000.0,2577500000.0,2753600000.0,3047380000.0,3317590000.0,3632890000.0,4125920000.0,4991010000.0,5545110000.0,5818250000.0,10923160000.0,12639910000.0,12913550000.0,15460120000.0,15348310000.0,12900660000.0,13501530000.0,13633820000.0,13434000000.0,13631820000.0,15133360000.0,15167000000.0,15532000000.0,14532000000.0,14484000000.0,14841000000.0,14170000000.0,13630000000.0,12525000000.0,11289000000.0,10843000000.0 +Total Liabilities,837050000.0,989490000.0,975200000.0,1258420000.0,1524550000.0,1749810000.0,3518410000.0,4036050000.0,4417280000.0,4860760000.0,5239760000.0,5701980000.0,6186660000.0,6936950000.0,8178710000.0,9311510000.0,9900640000.0,16750169999.999998,18885350000.0,19459900000.0,21929060000.0,23022980000.0,21551020000.0,22642890000.0,23409140000.0,23427000000.0,22874620000.0,24722140000.0,25313000000.0,26199000000.0,26518000000.0,26754000000.0,28143000000.0,28418000000.0,28507000000.0,28896000000.0,29340000000.0,30548000000.0 +Common Stock,947800000.0,1190310000.0,1222940000.0,1714280000.0,1687520000.0,1806740000.0,2101480000.0,2203660000.0,2284140000.0,2345390000.0,2429800000.0,2502810000.0,3340570000.0,3409580000.0,3561390000.0,5383880000.0,5531080000.0,7773890000.0,8351680000.0,8774380000.0,8989190000.0,9178190000.0,9419070000.0,9656710000.0,9957880000.0,10249000000.0,10563920000.0,12052640000.0,12348000000.0,12737000000.0,15390000000.0,15895000000.0,21575000000.0,27261000000.0,27624000000.0,28206000000.0,28923000000.0,29804000000.0 +Retained Earnings,-975670000.0,-1065609999.9999999,-1054359999.9999999,-1084860000.0,-1123360000.0,-1139620000.0,-1189420000.0,-1251330000.0,-1326040000.0,-1433660000.0,-1587840000.0,-1772070000.0,-2001930000.0,-2322320000.0,-2604590000.0,-2897780000.0,-2875900000.0,-2997240000.0,-3343190000.0,-3679580000.0,-4298960000.0,-4974300000.0,-5051290000.0,-5768830000.0,-5457320000.0,-5318000000.0,-5923310000.0,-6331640000.0,-6188000000.0,-6083000000.0,-6104000000.0,-6000000000.0,-5669000000.0,-5399000000.0,-4750000000.0,-3608000000.0,-1990000000.0,331000000.0 +Comprehensive Income,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-20000.0,-15970000.0,-14800000.0,-23990000.0,-3560000.0,13570000.0,34190000.0,25310000.0,-23740000.0,-20770000.0,10960000.0,21250000.0,33350000.0,82920000.0,18550000.0,8270000.0,-8000000.0,-35020000.0,-5610000.0,-120000000.0,-36000000.0,-113000000.0,-40000000.0,125000000.0,363000000.0,143000000.0,206000000.0,120000000.0,54000000.0 +Shareholders' Equity,-27880000.0,124700000.0,168580000.0,629430000.0,564170000.0,667120000.0,912060000.0,952330000.0,958090000.0,911710000.0,826000000.0,715930000.0,1314660000.0,1083700000.0,970370000.0,2520290000.0,2680490000.0,4752910000.0,4987720000.0,5105750000.0,4711480000.0,4237240000.0,4450700000.0,3906420000.0,4508840000.0,4923000000.0,4605600000.0,5715390000.0,6040000000.0,6618000000.0,9173000000.0,9855000000.0,16031000000.0,22225000000.0,23017000000.0,24804000000.0,27053000000.0,30189000000.0 +Total Liabilities and Equity,809180000.0,1114190000.0,1143780000.0,1887840000.0,2088710000.0,2416930000.0,4430470000.0,4988380000.0,5375370000.0,5772470000.0,6065750000.0,6417910000.0,7501320000.0,8020650000.0,9149080000.0,11831810000.0,12581130000.0,21503080000.0,23873070000.0,24565650000.0,26640540000.0,27260220000.0,26001720000.0,26549310000.0,27917980000.0,28350000000.0,27480210000.0,30437530000.0,31353000000.0,32817000000.0,35691000000.0,36609000000.0,44174000000.0,50643000000.0,51524000000.0,53700000000.0,56393000000.0,60737000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_2.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_2.csv index 50885e2bc9c..205ec4c9a08 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_2.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_get_stocks_data[stocks4-tle-sa_keys4-trailing-BS]_2.csv @@ -1,28 +1,28 @@ -0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 -Cash & Equivalents,66644000000.0,68312000000.0,74483000000.0,77022000000.0,80672000000.0,83944000000.0,88425000000.0,85709000000.0,89193000000.0,90249000000.0,95438000000.0,96526000000.0,99355000000.0,102640000000.0,105552000000.0,113240000000.0,136932000000.0,122781000000.0,126018000000.0,132981000000.0,138471000000.0,142780000000.0,132270000000.0,133768000000.0,135880000000.0,127662000000.0,131618000000.0,133819000000.0,136636000000.0,134253000000.0,137626000000.0,136527000000.0,137977000000.0,131968000000.0,125407000000.0,130334000000.0,130615000000.0,125369000000.0 -Short-Term Investments,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -Cash & Cash Equivalents,66644000000.0,68312000000.0,74483000000.0,77022000000.0,80672000000.0,83944000000.0,88425000000.0,85709000000.0,89193000000.0,90249000000.0,95438000000.0,96526000000.0,99355000000.0,102640000000.0,105552000000.0,113240000000.0,136932000000.0,122781000000.0,126018000000.0,132981000000.0,138471000000.0,142780000000.0,132270000000.0,133768000000.0,135880000000.0,127662000000.0,131618000000.0,133819000000.0,136636000000.0,134253000000.0,137626000000.0,136527000000.0,137977000000.0,131968000000.0,125407000000.0,130334000000.0,130615000000.0,125369000000.0 -Receivables,9871000000.0,14317000000.0,11991000000.0,17486000000.0,11007000000.0,15986000000.0,13497000000.0,19544000000.0,12887000000.0,16186000000.0,12427000000.0,17908000000.0,11444000000.0,14507000000.0,12247000000.0,18277000000.0,11129000000.0,14343000000.0,12882000000.0,22431000000.0,14561000000.0,18428000000.0,17208000000.0,26481000000.0,17390000000.0,19680000000.0,19269000000.0,29524000000.0,19087000000.0,23525000000.0,22699000000.0,32011000000.0,22851000000.0,27312000000.0,26322000000.0,38043000000.0,27349000000.0,33520000000.0 -Inventory,1624000000.0,1661000000.0,2133000000.0,1938000000.0,2613000000.0,1594000000.0,1920000000.0,2660000000.0,3141000000.0,2053000000.0,2469000000.0,2902000000.0,3816000000.0,2702000000.0,2450000000.0,2251000000.0,3122000000.0,1961000000.0,1979000000.0,2181000000.0,3211000000.0,2003000000.0,2084000000.0,2662000000.0,3614000000.0,1961000000.0,1951000000.0,2063000000.0,2622000000.0,1823000000.0,1644000000.0,1895000000.0,2705000000.0,1924000000.0,2245000000.0,2636000000.0,3411000000.0,3019000000.0 -Other Current Assets,5912000000.0,5284000000.0,4917000000.0,5020000000.0,5158000000.0,5346000000.0,5164000000.0,6333000000.0,7218000000.0,7874000000.0,8064000000.0,5461000000.0,7041000000.0,7963000000.0,8172000000.0,5892000000.0,6726000000.0,5864000000.0,5434000000.0,5103000000.0,4788000000.0,4422000000.0,5097000000.0,6751000000.0,7311000000.0,7571000000.0,7049000000.0,10146000000.0,7551000000.0,7473000000.0,8536000000.0,11482000000.0,13544000000.0,12769000000.0,11640000000.0,13393000000.0,12951000000.0,12280000000.0 -Total Current Assets,84051000000.0,89574000000.0,93524000000.0,101466000000.0,99450000000.0,106870000000.0,109006000000.0,114246000000.0,112439000000.0,116362000000.0,118398000000.0,122797000000.0,121656000000.0,127812000000.0,128421000000.0,139660000000.0,157909000000.0,144949000000.0,146313000000.0,162696000000.0,161031000000.0,167633000000.0,156659000000.0,169662000000.0,164195000000.0,156874000000.0,159887000000.0,175552000000.0,165896000000.0,167074000000.0,170505000000.0,181915000000.0,177077000000.0,173973000000.0,165614000000.0,184406000000.0,174326000000.0,174188000000.0 -"Property, Plant & Equipment",8329000000.0,8698000000.0,9204000000.0,9991000000.0,10774000000.0,11567000000.0,11771000000.0,13011000000.0,13229000000.0,13607000000.0,14375000000.0,14731000000.0,15046000000.0,15789000000.0,16831000000.0,18356000000.0,19224000000.0,21379000000.0,21962000000.0,30289000000.0,31653000000.0,33053000000.0,34788000000.0,36146000000.0,38164000000.0,39523000000.0,40769000000.0,43856000000.0,46299000000.0,48961000000.0,49669000000.0,52904000000.0,56974000000.0,62035000000.0,65618000000.0,70803000000.0,75347000000.0,79568000000.0 -Long-Term Investments,10038000000.0,10707000000.0,11193000000.0,10844000000.0,11995000000.0,14607000000.0,14792000000.0,14597000000.0,13943000000.0,12665000000.0,12019000000.0,12053000000.0,11438000000.0,11514000000.0,11315000000.0,10431000000.0,10486000000.0,8912000000.0,7381000000.0,6023000000.0,5343000000.0,3961000000.0,2818000000.0,1862000000.0,2034000000.0,2274000000.0,2403000000.0,2649000000.0,2684000000.0,2755000000.0,2660000000.0,2965000000.0,3103000000.0,3794000000.0,5395000000.0,5984000000.0,6393000000.0,6994000000.0 -Goodwill and Intangibles,17889000000.0,18068000000.0,17922000000.0,17738000000.0,17649000000.0,17625000000.0,17652000000.0,27108000000.0,26774000000.0,29154000000.0,28691000000.0,21774000000.0,21887000000.0,22055000000.0,22407000000.0,21605000000.0,21429000000.0,45525000000.0,45215000000.0,45228000000.0,44987000000.0,44389000000.0,44126000000.0,43736000000.0,43434000000.0,50059000000.0,49964000000.0,49776000000.0,49621000000.0,49374000000.0,48919000000.0,50389000000.0,50813000000.0,50774000000.0,57825000000.0,57511000000.0,58249000000.0,58383000000.0 -Other Long-Term Assets,1569000000.0,1636000000.0,2262000000.0,2392000000.0,2480000000.0,2874000000.0,2898000000.0,3422000000.0,3271000000.0,3060000000.0,3200000000.0,3117000000.0,2869000000.0,2928000000.0,2895000000.0,3416000000.0,3476000000.0,3845000000.0,4146000000.0,6076000000.0,6083000000.0,6967000000.0,7106000000.0,7442000000.0,9792000000.0,10129000000.0,10258000000.0,14723000000.0,14455000000.0,14630000000.0,13696000000.0,13138000000.0,13034000000.0,13561000000.0,14427000000.0,15075000000.0,21103000000.0,21256000000.0 -Total Long-Term Assets,37825000000.0,39109000000.0,40581000000.0,40965000000.0,42898000000.0,46673000000.0,47113000000.0,58138000000.0,57217000000.0,58486000000.0,58285000000.0,51675000000.0,51240000000.0,52286000000.0,53448000000.0,53808000000.0,54615000000.0,79661000000.0,78704000000.0,87616000000.0,88066000000.0,88370000000.0,88838000000.0,89186000000.0,93424000000.0,101985000000.0,103394000000.0,111004000000.0,113059000000.0,115720000000.0,114944000000.0,119396000000.0,123924000000.0,130164000000.0,143265000000.0,149373000000.0,161092000000.0,166201000000.0 -Total Assets,121876000000.0,128683000000.0,134105000000.0,142431000000.0,142348000000.0,153543000000.0,156119000000.0,172384000000.0,169656000000.0,174848000000.0,176683000000.0,174472000000.0,172896000000.0,180098000000.0,181869000000.0,193468000000.0,212524000000.0,224610000000.0,225017000000.0,250312000000.0,249097000000.0,256003000000.0,245497000000.0,258848000000.0,257619000000.0,258859000000.0,263281000000.0,286556000000.0,278955000000.0,282794000000.0,285449000000.0,301311000000.0,301001000000.0,304137000000.0,308879000000.0,333779000000.0,335418000000.0,340389000000.0 -Accounts Payable,4046000000.0,4377000000.0,5096000000.0,5473000000.0,5423000000.0,6146000000.0,5377000000.0,7990000000.0,6960000000.0,7362000000.0,6786000000.0,6683000000.0,6784000000.0,7375000000.0,7132000000.0,7192000000.0,6506000000.0,7860000000.0,6418000000.0,7390000000.0,7069000000.0,7876000000.0,7623000000.0,8617000000.0,8511000000.0,7563000000.0,7544000000.0,9382000000.0,8574000000.0,8811000000.0,9246000000.0,12530000000.0,12509000000.0,12770000000.0,13412000000.0,15163000000.0,14832000000.0,15314000000.0 -Deferred Revenue,19587000000.0,19813000000.0,17905000000.0,22399000000.0,20214000000.0,19474000000.0,19512000000.0,25158000000.0,22538000000.0,21243000000.0,20198000000.0,25318000000.0,24387000000.0,25031000000.0,25893000000.0,33909000000.0,33588000000.0,34680000000.0,35733000000.0,26656000000.0,24904000000.0,23809000000.0,23955000000.0,32720000000.0,30242000000.0,28084000000.0,28135000000.0,37206000000.0,34026000000.0,31221000000.0,30397000000.0,39180000000.0,36305000000.0,33387000000.0,32714000000.0,44141000000.0,41015000000.0,36769000000.0 -Current Debt,2236000000.0,2241000000.0,2246000000.0,2999000000.0,3300000000.0,2300000000.0,2000000000.0,2000000000.0,5248000000.0,10048000000.0,4224000000.0,7484000000.0,10748000000.0,3750000000.0,5498000000.0,12904000000.0,14536000000.0,25664000000.0,7786000000.0,10121000000.0,9220000000.0,15912000000.0,3677000000.0,3998000000.0,6497000000.0,3516000000.0,6515000000.0,5516000000.0,3017000000.0,6247000000.0,3748000000.0,3749000000.0,6497000000.0,5387000000.0,8051000000.0,8072000000.0,3249000000.0,4998000000.0 -Other Current Liabilities,5533000000.0,5479000000.0,6682000000.0,6546000000.0,5686000000.0,5822000000.0,7014000000.0,10477000000.0,9948000000.0,8762000000.0,9540000000.0,10162000000.0,7480000000.0,6487000000.0,5831000000.0,5352000000.0,4180000000.0,2583000000.0,2068000000.0,11578000000.0,10422000000.0,10502000000.0,10878000000.0,13153000000.0,11027000000.0,11155000000.0,11667000000.0,17316000000.0,12501000000.0,13361000000.0,15316000000.0,16851000000.0,14745000000.0,15942000000.0,18016000000.0,21281000000.0,21432000000.0,20429000000.0 -Total Current Liabilities,31402000000.0,31910000000.0,31929000000.0,37417000000.0,34623000000.0,33742000000.0,33903000000.0,45625000000.0,44694000000.0,47415000000.0,40748000000.0,49647000000.0,49399000000.0,42643000000.0,44354000000.0,59357000000.0,58810000000.0,70787000000.0,52005000000.0,55745000000.0,51615000000.0,58099000000.0,46133000000.0,58488000000.0,56277000000.0,50318000000.0,53861000000.0,69420000000.0,58118000000.0,59640000000.0,58707000000.0,72310000000.0,70056000000.0,67486000000.0,72193000000.0,88657000000.0,80528000000.0,77510000000.0 -Long-Term Debt,9714000000.0,11947000000.0,11949000000.0,12601000000.0,12632000000.0,20676000000.0,20679000000.0,20645000000.0,18472000000.0,18260000000.0,27644000000.0,27808000000.0,27819000000.0,40679000000.0,40896000000.0,40557000000.0,60154000000.0,59306000000.0,76222000000.0,81445000000.0,82023000000.0,78988000000.0,79231000000.0,77810000000.0,75385000000.0,75336000000.0,72557000000.0,72850000000.0,73137000000.0,70533000000.0,70110000000.0,67249000000.0,64808000000.0,64011000000.0,59279000000.0,59703000000.0,60089000000.0,59034000000.0 -Other Long-Term Liabilities,11924000000.0,12250000000.0,13539000000.0,13469000000.0,13452000000.0,14025000000.0,14113000000.0,16330000000.0,16320000000.0,17294000000.0,18159000000.0,16934000000.0,18233000000.0,19996000000.0,21813000000.0,21557000000.0,23188000000.0,25708000000.0,27061000000.0,25411000000.0,25812000000.0,40556000000.0,40894000000.0,39832000000.0,39990000000.0,41077000000.0,41999000000.0,41956000000.0,41639000000.0,42512000000.0,42131000000.0,43448000000.0,42745000000.0,42404000000.0,42902000000.0,43431000000.0,42823000000.0,43835000000.0 -Total Long-Term Liabilities,21638000000.0,24197000000.0,25488000000.0,26070000000.0,26084000000.0,34701000000.0,34792000000.0,36975000000.0,34792000000.0,35554000000.0,45803000000.0,44742000000.0,46052000000.0,60675000000.0,62709000000.0,62114000000.0,83342000000.0,85014000000.0,103283000000.0,106856000000.0,107835000000.0,119544000000.0,120125000000.0,117642000000.0,115375000000.0,116413000000.0,114556000000.0,114806000000.0,114776000000.0,113045000000.0,112241000000.0,110697000000.0,107553000000.0,106415000000.0,102181000000.0,103134000000.0,102912000000.0,102869000000.0 -Total Liabilities,53040000000.0,56107000000.0,57417000000.0,63487000000.0,60707000000.0,68443000000.0,68695000000.0,82600000000.0,79486000000.0,82969000000.0,86551000000.0,94389000000.0,95451000000.0,103318000000.0,107063000000.0,121471000000.0,142152000000.0,155801000000.0,155288000000.0,162601000000.0,159450000000.0,177643000000.0,166258000000.0,176130000000.0,171652000000.0,166731000000.0,168417000000.0,184226000000.0,172894000000.0,172685000000.0,170948000000.0,183007000000.0,177609000000.0,173901000000.0,174374000000.0,191791000000.0,183440000000.0,180379000000.0 -Common Stock,66084000000.0,66334000000.0,66826000000.0,67306000000.0,67230000000.0,67476000000.0,67803000000.0,68366000000.0,68362000000.0,68765000000.0,68475000000.0,68465000000.0,68093000000.0,68279000000.0,68012000000.0,68178000000.0,67747000000.0,68177000000.0,68554000000.0,69315000000.0,69419000000.0,70192000000.0,70418000000.0,71223000000.0,71303000000.0,77556000000.0,77791000000.0,78520000000.0,78882000000.0,79625000000.0,79813000000.0,80552000000.0,81089000000.0,81896000000.0,82308000000.0,83111000000.0,83751000000.0,84528000000.0 -Retained Earnings,932000000.0,4236000000.0,7657000000.0,9895000000.0,11680000000.0,14347000000.0,16289000000.0,17710000000.0,18051000000.0,19731000000.0,18186000000.0,9096000000.0,7614000000.0,6728000000.0,4954000000.0,2282000000.0,944000000.0,120000000.0,531000000.0,17769000000.0,19702000000.0,8567000000.0,9974000000.0,13682000000.0,17279000000.0,16585000000.0,18338000000.0,24150000000.0,27240000000.0,30739000000.0,32012000000.0,34566000000.0,39193000000.0,44973000000.0,50735000000.0,57055000000.0,66944000000.0,75045000000.0 -Comprehensive Income,1820000000.0,2006000000.0,2205000000.0,1743000000.0,2731000000.0,3277000000.0,3332000000.0,3708000000.0,3757000000.0,3383000000.0,3471000000.0,2522000000.0,1738000000.0,1773000000.0,1840000000.0,1537000000.0,1681000000.0,512000000.0,644000000.0,627000000.0,526000000.0,-399000000.0,-1153000000.0,-2187000000.0,-2615000000.0,-2013000000.0,-1265000000.0,-340000000.0,-61000000.0,-255000000.0,2676000000.0,3186000000.0,3110000000.0,3367000000.0,1462000000.0,1822000000.0,1283000000.0,437000000.0 -Shareholders' Equity,68836000000.0,72576000000.0,76688000000.0,78944000000.0,81641000000.0,85100000000.0,87424000000.0,89784000000.0,90170000000.0,91879000000.0,90132000000.0,80083000000.0,77445000000.0,76780000000.0,74806000000.0,71997000000.0,70372000000.0,68809000000.0,69729000000.0,87711000000.0,89647000000.0,78360000000.0,79239000000.0,82718000000.0,85967000000.0,92128000000.0,94864000000.0,102330000000.0,106061000000.0,110109000000.0,114501000000.0,118304000000.0,123392000000.0,130236000000.0,134505000000.0,141988000000.0,151978000000.0,160010000000.0 -Total Liabilities and Equity,121876000000.0,128683000000.0,134105000000.0,142431000000.0,142348000000.0,153543000000.0,156119000000.0,172384000000.0,169656000000.0,174848000000.0,176683000000.0,174472000000.0,172896000000.0,180098000000.0,181869000000.0,193468000000.0,212524000000.0,224610000000.0,225017000000.0,250312000000.0,249097000000.0,256003000000.0,245497000000.0,258848000000.0,257619000000.0,258859000000.0,263281000000.0,286556000000.0,278955000000.0,282794000000.0,285449000000.0,301311000000.0,301001000000.0,304137000000.0,308879000000.0,333779000000.0,335418000000.0,340389000000.0 +0,2012-09-30,2012-12-31,2013-03-31,2013-06-30,2013-09-30,2013-12-31,2014-03-31,2014-06-30,2014-09-30,2014-12-31,2015-03-31,2015-06-30,2015-09-30,2015-12-31,2016-03-31,2016-06-30,2016-09-30,2016-12-31,2017-03-31,2017-06-30,2017-09-30,2017-12-31,2018-03-31,2018-06-30,2018-09-30,2018-12-31,2019-03-31,2019-06-30,2019-09-30,2019-12-31,2020-03-31,2020-06-30,2020-09-30,2020-12-31,2021-03-31,2021-06-30,2021-09-30,2021-12-31 +Cash & Equivalents,66644000000.0,68312000000.0,74483000000.0,77022000000.0,80672000000.0,83944000000.0,88425000000.0,85709000000.0,89193000000.0,90249000000.0,95438000000.0,96526000000.0,99355000000.0,102640000000.0,105552000000.0,113240000000.0,136932000000.0,122781000000.0,126018000000.0,132981000000.0,138471000000.0,142780000000.0,132270000000.0,133768000000.0,135880000000.0,127662000000.0,131618000000.0,133819000000.0,136636000000.0,134253000000.0,137626000000.0,136527000000.0,137977000000.0,131968000000.0,125407000000.0,130334000000.0,130615000000.0,125369000000.0 +Short-Term Investments,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Cash & Cash Equivalents,66644000000.0,68312000000.0,74483000000.0,77022000000.0,80672000000.0,83944000000.0,88425000000.0,85709000000.0,89193000000.0,90249000000.0,95438000000.0,96526000000.0,99355000000.0,102640000000.0,105552000000.0,113240000000.0,136932000000.0,122781000000.0,126018000000.0,132981000000.0,138471000000.0,142780000000.0,132270000000.0,133768000000.0,135880000000.0,127662000000.0,131618000000.0,133819000000.0,136636000000.0,134253000000.0,137626000000.0,136527000000.0,137977000000.0,131968000000.0,125407000000.0,130334000000.0,130615000000.0,125369000000.0 +Receivables,9871000000.0,14317000000.0,11991000000.0,17486000000.0,11007000000.0,15986000000.0,13497000000.0,19544000000.0,12887000000.0,16186000000.0,12427000000.0,17908000000.0,11444000000.0,14507000000.0,12247000000.0,18277000000.0,11129000000.0,14343000000.0,12882000000.0,22431000000.0,14561000000.0,18428000000.0,17208000000.0,26481000000.0,17390000000.0,19680000000.0,19269000000.0,29524000000.0,19087000000.0,23525000000.0,22699000000.0,32011000000.0,22851000000.0,27312000000.0,26322000000.0,38043000000.0,27349000000.0,33520000000.0 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Assets,84051000000.0,89574000000.0,93524000000.0,101466000000.0,99450000000.0,106870000000.0,109006000000.0,114246000000.0,112439000000.0,116362000000.0,118398000000.0,122797000000.0,121656000000.0,127812000000.0,128421000000.0,139660000000.0,157909000000.0,144949000000.0,146313000000.0,162696000000.0,161031000000.0,167633000000.0,156659000000.0,169662000000.0,164195000000.0,156874000000.0,159887000000.0,175552000000.0,165896000000.0,167074000000.0,170505000000.0,181915000000.0,177077000000.0,173973000000.0,165614000000.0,184406000000.0,174326000000.0,174188000000.0 +"Property, Plant & Equipment",8329000000.0,8698000000.0,9204000000.0,9991000000.0,10774000000.0,11567000000.0,11771000000.0,13011000000.0,13229000000.0,13607000000.0,14375000000.0,14731000000.0,15046000000.0,15789000000.0,16831000000.0,18356000000.0,19224000000.0,21379000000.0,21962000000.0,30289000000.0,31653000000.0,33053000000.0,34788000000.0,36146000000.0,38164000000.0,39523000000.0,40769000000.0,43856000000.0,46299000000.0,48961000000.0,49669000000.0,52904000000.0,56974000000.0,62035000000.0,65618000000.0,70803000000.0,75347000000.0,79568000000.0 +Long-Term Investments,10038000000.0,10707000000.0,11193000000.0,10844000000.0,11995000000.0,14607000000.0,14792000000.0,14597000000.0,13943000000.0,12665000000.0,12019000000.0,12053000000.0,11438000000.0,11514000000.0,11315000000.0,10431000000.0,10486000000.0,8912000000.0,7381000000.0,6023000000.0,5343000000.0,3961000000.0,2818000000.0,1862000000.0,2034000000.0,2274000000.0,2403000000.0,2649000000.0,2684000000.0,2755000000.0,2660000000.0,2965000000.0,3103000000.0,3794000000.0,5395000000.0,5984000000.0,6393000000.0,6994000000.0 +Goodwill and Intangibles,17889000000.0,18068000000.0,17922000000.0,17738000000.0,17649000000.0,17625000000.0,17652000000.0,27108000000.0,26774000000.0,29154000000.0,28691000000.0,21774000000.0,21887000000.0,22055000000.0,22407000000.0,21605000000.0,21429000000.0,45525000000.0,45215000000.0,45228000000.0,44987000000.0,44389000000.0,44126000000.0,43736000000.0,43434000000.0,50059000000.0,49964000000.0,49776000000.0,49621000000.0,49374000000.0,48919000000.0,50389000000.0,50813000000.0,50774000000.0,57825000000.0,57511000000.0,58249000000.0,58383000000.0 +Other Long-Term Assets,1569000000.0,1636000000.0,2262000000.0,2392000000.0,2480000000.0,2874000000.0,2898000000.0,3422000000.0,3271000000.0,3060000000.0,3200000000.0,3117000000.0,2869000000.0,2928000000.0,2895000000.0,3416000000.0,3476000000.0,3845000000.0,4146000000.0,6076000000.0,6083000000.0,6967000000.0,7106000000.0,7442000000.0,9792000000.0,10129000000.0,10258000000.0,14723000000.0,14455000000.0,14630000000.0,13696000000.0,13138000000.0,13034000000.0,13561000000.0,14427000000.0,15075000000.0,21103000000.0,21256000000.0 +Total Long-Term Assets,37825000000.0,39109000000.0,40581000000.0,40965000000.0,42898000000.0,46673000000.0,47113000000.0,58138000000.0,57217000000.0,58486000000.0,58285000000.0,51675000000.0,51240000000.0,52286000000.0,53448000000.0,53808000000.0,54615000000.0,79661000000.0,78704000000.0,87616000000.0,88066000000.0,88370000000.0,88838000000.0,89186000000.0,93424000000.0,101985000000.0,103394000000.0,111004000000.0,113059000000.0,115720000000.0,114944000000.0,119396000000.0,123924000000.0,130164000000.0,143265000000.0,149373000000.0,161092000000.0,166201000000.0 +Total Assets,121876000000.0,128683000000.0,134105000000.0,142431000000.0,142348000000.0,153543000000.0,156119000000.0,172384000000.0,169656000000.0,174848000000.0,176683000000.0,174472000000.0,172896000000.0,180098000000.0,181869000000.0,193468000000.0,212524000000.0,224610000000.0,225017000000.0,250312000000.0,249097000000.0,256003000000.0,245497000000.0,258848000000.0,257619000000.0,258859000000.0,263281000000.0,286556000000.0,278955000000.0,282794000000.0,285449000000.0,301311000000.0,301001000000.0,304137000000.0,308879000000.0,333779000000.0,335418000000.0,340389000000.0 +Accounts Payable,4046000000.0,4377000000.0,5096000000.0,5473000000.0,5423000000.0,6146000000.0,5377000000.0,7990000000.0,6960000000.0,7362000000.0,6786000000.0,6683000000.0,6784000000.0,7375000000.0,7132000000.0,7192000000.0,6506000000.0,7860000000.0,6418000000.0,7390000000.0,7069000000.0,7876000000.0,7623000000.0,8617000000.0,8511000000.0,7563000000.0,7544000000.0,9382000000.0,8574000000.0,8811000000.0,9246000000.0,12530000000.0,12509000000.0,12770000000.0,13412000000.0,15163000000.0,14832000000.0,15314000000.0 +Deferred Revenue,19587000000.0,19813000000.0,17905000000.0,22399000000.0,20214000000.0,19474000000.0,19512000000.0,25158000000.0,22538000000.0,21243000000.0,20198000000.0,25318000000.0,24387000000.0,25031000000.0,25893000000.0,33909000000.0,33588000000.0,34680000000.0,35733000000.0,26656000000.0,24904000000.0,23809000000.0,23955000000.0,32720000000.0,30242000000.0,28084000000.0,28135000000.0,37206000000.0,34026000000.0,31221000000.0,30397000000.0,39180000000.0,36305000000.0,33387000000.0,32714000000.0,44141000000.0,41015000000.0,36769000000.0 +Current Debt,2236000000.0,2241000000.0,2246000000.0,2999000000.0,3300000000.0,2300000000.0,2000000000.0,2000000000.0,5248000000.0,10048000000.0,4224000000.0,7484000000.0,10748000000.0,3750000000.0,5498000000.0,12904000000.0,14536000000.0,25664000000.0,7786000000.0,10121000000.0,9220000000.0,15912000000.0,3677000000.0,3998000000.0,6497000000.0,3516000000.0,6515000000.0,5516000000.0,3017000000.0,6247000000.0,3748000000.0,3749000000.0,6497000000.0,5387000000.0,8051000000.0,8072000000.0,3249000000.0,4998000000.0 +Other Current Liabilities,5533000000.0,5479000000.0,6682000000.0,6546000000.0,5686000000.0,5822000000.0,7014000000.0,10477000000.0,9948000000.0,8762000000.0,9540000000.0,10162000000.0,7480000000.0,6487000000.0,5831000000.0,5352000000.0,4180000000.0,2583000000.0,2068000000.0,11578000000.0,10422000000.0,10502000000.0,10878000000.0,13153000000.0,11027000000.0,11155000000.0,11667000000.0,17316000000.0,12501000000.0,13361000000.0,15316000000.0,16851000000.0,14745000000.0,15942000000.0,18016000000.0,21281000000.0,21432000000.0,20429000000.0 +Total Current Liabilities,31402000000.0,31910000000.0,31929000000.0,37417000000.0,34623000000.0,33742000000.0,33903000000.0,45625000000.0,44694000000.0,47415000000.0,40748000000.0,49647000000.0,49399000000.0,42643000000.0,44354000000.0,59357000000.0,58810000000.0,70787000000.0,52005000000.0,55745000000.0,51615000000.0,58099000000.0,46133000000.0,58488000000.0,56277000000.0,50318000000.0,53861000000.0,69420000000.0,58118000000.0,59640000000.0,58707000000.0,72310000000.0,70056000000.0,67486000000.0,72193000000.0,88657000000.0,80528000000.0,77510000000.0 +Long-Term Debt,9714000000.0,11947000000.0,11949000000.0,12601000000.0,12632000000.0,20676000000.0,20679000000.0,20645000000.0,18472000000.0,18260000000.0,27644000000.0,27808000000.0,27819000000.0,40679000000.0,40896000000.0,40557000000.0,60154000000.0,59306000000.0,76222000000.0,81445000000.0,82023000000.0,78988000000.0,79231000000.0,77810000000.0,75385000000.0,75336000000.0,72557000000.0,72850000000.0,73137000000.0,70533000000.0,70110000000.0,67249000000.0,64808000000.0,64011000000.0,59279000000.0,59703000000.0,60089000000.0,59034000000.0 +Other Long-Term Liabilities,11924000000.0,12250000000.0,13539000000.0,13469000000.0,13452000000.0,14025000000.0,14113000000.0,16330000000.0,16320000000.0,17294000000.0,18159000000.0,16934000000.0,18233000000.0,19996000000.0,21813000000.0,21557000000.0,23188000000.0,25708000000.0,27061000000.0,25411000000.0,25812000000.0,40556000000.0,40894000000.0,39832000000.0,39990000000.0,41077000000.0,41999000000.0,41956000000.0,41639000000.0,42512000000.0,42131000000.0,43448000000.0,42745000000.0,42404000000.0,42902000000.0,43431000000.0,42823000000.0,43835000000.0 +Total Long-Term Liabilities,21638000000.0,24197000000.0,25488000000.0,26070000000.0,26084000000.0,34701000000.0,34792000000.0,36975000000.0,34792000000.0,35554000000.0,45803000000.0,44742000000.0,46052000000.0,60675000000.0,62709000000.0,62114000000.0,83342000000.0,85014000000.0,103283000000.0,106856000000.0,107835000000.0,119544000000.0,120125000000.0,117642000000.0,115375000000.0,116413000000.0,114556000000.0,114806000000.0,114776000000.0,113045000000.0,112241000000.0,110697000000.0,107553000000.0,106415000000.0,102181000000.0,103134000000.0,102912000000.0,102869000000.0 +Total Liabilities,53040000000.0,56107000000.0,57417000000.0,63487000000.0,60707000000.0,68443000000.0,68695000000.0,82600000000.0,79486000000.0,82969000000.0,86551000000.0,94389000000.0,95451000000.0,103318000000.0,107063000000.0,121471000000.0,142152000000.0,155801000000.0,155288000000.0,162601000000.0,159450000000.0,177643000000.0,166258000000.0,176130000000.0,171652000000.0,166731000000.0,168417000000.0,184226000000.0,172894000000.0,172685000000.0,170948000000.0,183007000000.0,177609000000.0,173901000000.0,174374000000.0,191791000000.0,183440000000.0,180379000000.0 +Common Stock,66084000000.0,66334000000.0,66826000000.0,67306000000.0,67230000000.0,67476000000.0,67803000000.0,68366000000.0,68362000000.0,68765000000.0,68475000000.0,68465000000.0,68093000000.0,68279000000.0,68012000000.0,68178000000.0,67747000000.0,68177000000.0,68554000000.0,69315000000.0,69419000000.0,70192000000.0,70418000000.0,71223000000.0,71303000000.0,77556000000.0,77791000000.0,78520000000.0,78882000000.0,79625000000.0,79813000000.0,80552000000.0,81089000000.0,81896000000.0,82308000000.0,83111000000.0,83751000000.0,84528000000.0 +Retained Earnings,932000000.0,4236000000.0,7657000000.0,9895000000.0,11680000000.0,14347000000.0,16289000000.0,17710000000.0,18051000000.0,19731000000.0,18186000000.0,9096000000.0,7614000000.0,6728000000.0,4954000000.0,2282000000.0,944000000.0,120000000.0,531000000.0,17769000000.0,19702000000.0,8567000000.0,9974000000.0,13682000000.0,17279000000.0,16585000000.0,18338000000.0,24150000000.0,27240000000.0,30739000000.0,32012000000.0,34566000000.0,39193000000.0,44973000000.0,50735000000.0,57055000000.0,66944000000.0,75045000000.0 +Comprehensive Income,1820000000.0,2006000000.0,2205000000.0,1743000000.0,2731000000.0,3277000000.0,3332000000.0,3708000000.0,3757000000.0,3383000000.0,3471000000.0,2522000000.0,1738000000.0,1773000000.0,1840000000.0,1537000000.0,1681000000.0,512000000.0,644000000.0,627000000.0,526000000.0,-399000000.0,-1153000000.0,-2187000000.0,-2615000000.0,-2013000000.0,-1265000000.0,-340000000.0,-61000000.0,-255000000.0,2676000000.0,3186000000.0,3110000000.0,3367000000.0,1462000000.0,1822000000.0,1283000000.0,437000000.0 +Shareholders' Equity,68836000000.0,72576000000.0,76688000000.0,78944000000.0,81641000000.0,85100000000.0,87424000000.0,89784000000.0,90170000000.0,91879000000.0,90132000000.0,80083000000.0,77445000000.0,76780000000.0,74806000000.0,71997000000.0,70372000000.0,68809000000.0,69729000000.0,87711000000.0,89647000000.0,78360000000.0,79239000000.0,82718000000.0,85967000000.0,92128000000.0,94864000000.0,102330000000.0,106061000000.0,110109000000.0,114501000000.0,118304000000.0,123392000000.0,130236000000.0,134505000000.0,141988000000.0,151978000000.0,160010000000.0 +Total Liabilities and Equity,121876000000.0,128683000000.0,134105000000.0,142431000000.0,142348000000.0,153543000000.0,156119000000.0,172384000000.0,169656000000.0,174848000000.0,176683000000.0,174472000000.0,172896000000.0,180098000000.0,181869000000.0,193468000000.0,212524000000.0,224610000000.0,225017000000.0,250312000000.0,249097000000.0,256003000000.0,245497000000.0,258848000000.0,257619000000.0,258859000000.0,263281000000.0,286556000000.0,278955000000.0,282794000000.0,285449000000.0,301311000000.0,301001000000.0,304137000000.0,308879000000.0,333779000000.0,335418000000.0,340389000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes.csv index 46ed1e6ceee..5c0f6361854 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes.csv @@ -1,3 +1,3 @@ -,2010,2011,2012 -Item 1,,, -Item 2,,, +,2010,2011,2012 +Item 1,,, +Item 2,,, diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes_1.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes_1.csv index 46ed1e6ceee..5c0f6361854 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes_1.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_model/test_match_length_dataframes_1.csv @@ -1,3 +1,3 @@ -,2010,2011,2012 -Item 1,,, -Item 2,,, +,2010,2011,2012 +Item 1,,, +Item 2,,, diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_1.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_1.csv index b3cc6d91d72..089fedf69a8 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_1.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_1.csv @@ -1,13 +1,13 @@ 0,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Revenue,3412000000.0,4465000000.0,4263000000.0,4651000000.0,4861000000.0,5437000000.0,5765000000.0,5804000000.0,6394000000.0,6686000000.0, -Cost of Revenue,452000000.0,454000000.0,405000000.0,410000000.0,293000000.0,342000000.0,332000000.0,295000000.0,321000000.0,363000000.0, -Gross Profit,2960000000.0,4011000000.0,3858000000.0,4241000000.0,4568000000.0,5095000000.0,5433000000.0,5509000000.0,6073000000.0,6323000000.0, -"Selling, General & Admin",993000000.0,1106000000.0,1019000000.0,947000000.0,963000000.0,992000000.0,1095000000.0,1155000000.0,1222000000.0,1376000000.0, -Research & Development,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Operating Expenses,1983000000.0,2062000000.0,1981000000.0,1591000000.0,1505000000.0,1605000000.0,1683000000.0,1814000000.0,1886000000.0,2094000000.0, -Operating Income,-16000000.0,843000000.0,858000000.0,1703000000.0,2100000000.0,2498000000.0,2655000000.0,2540000000.0,2965000000.0,2853000000.0, -Interest Expense / Income,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Expense / Income,99000000.0,503000000.0,556000000.0,232000000.0,315000000.0,961000000.0,1145000000.0,918000000.0,1004000000.0,1440000000.0, -Income Tax,42000000.0,-856000000.0,-59000000.0,321000000.0,496000000.0,470000000.0,581000000.0,359000000.0,246000000.0,328000000.0, -Net Income,-157000000.0,1196000000.0,361000000.0,1150000000.0,1289000000.0,1067000000.0,929000000.0,1263000000.0,1715000000.0,1085000000.0, -Preferred Dividends,762000000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, +Revenue,,4465000000.0,4263000000.0,4651000000.0,4861000000.0,5437000000.0,5765000000.0,5804000000.0,6394000000.0,6686000000.0,8206000000.0 +Cost of Revenue,,454000000.0,405000000.0,410000000.0,293000000.0,342000000.0,332000000.0,295000000.0,321000000.0,363000000.0,261000000.0 +Gross Profit,,4011000000.0,3858000000.0,4241000000.0,4568000000.0,5095000000.0,5433000000.0,5509000000.0,6073000000.0,6323000000.0,7945000000.0 +"Selling, General & Admin",,1106000000.0,1019000000.0,947000000.0,963000000.0,992000000.0,1095000000.0,1155000000.0,1222000000.0,1376000000.0,1643000000.0 +Research & Development,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Operating Expenses,,2062000000.0,1981000000.0,1591000000.0,1505000000.0,1605000000.0,1683000000.0,1814000000.0,1886000000.0,2094000000.0,2206000000.0 +Operating Income,,843000000.0,858000000.0,1703000000.0,2100000000.0,2498000000.0,2655000000.0,2540000000.0,2965000000.0,2853000000.0,4096000000.0 +Interest Expense / Income,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Expense / Income,,503000000.0,556000000.0,232000000.0,315000000.0,961000000.0,1145000000.0,918000000.0,1004000000.0,1440000000.0,303000000.0 +Income Tax,,-856000000.0,-59000000.0,321000000.0,496000000.0,470000000.0,581000000.0,359000000.0,246000000.0,328000000.0,790000000.0 +Net Income,,1196000000.0,361000000.0,1150000000.0,1289000000.0,1067000000.0,929000000.0,1263000000.0,1715000000.0,1085000000.0,3003000000.0 +Preferred Dividends,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_3.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_3.csv index 5ebb23bf92f..c3ceb49d224 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_3.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_3.csv @@ -1,13 +1,13 @@ 0,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Revenue,13919000000.0,16981000000.0,18931000000.0,18749000000.0,18877000000.0,19042000000.0,19686000000.0,22220000000.0,22357000000.0,18259000000.0, -Cost of Revenue,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Gross Profit,13919000000.0,16981000000.0,18931000000.0,18749000000.0,18877000000.0,19042000000.0,19686000000.0,22220000000.0,22357000000.0,18259000000.0, -"Selling, General & Admin",7264000000.0,8939000000.0,9638000000.0,9913000000.0,10723000000.0,11201000000.0,11782000000.0,12424000000.0,13287000000.0,13060000000.0, -Research & Development,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Operating Expenses,2068000000.0,2858000000.0,2715000000.0,2267000000.0,2273000000.0,2357000000.0,2412000000.0,2478000000.0,2196000000.0,1996000000.0, -Operating Income,4587000000.0,5184000000.0,6578000000.0,6569000000.0,5881000000.0,5484000000.0,5492000000.0,7318000000.0,6874000000.0,3203000000.0, -Interest Expense / Income,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Expense / Income,106000000.0,217000000.0,233000000.0,-5000000.0,-38000000.0,19000000.0,135000000.0,10000000.0,-13000000.0,3000000.0, -Income Tax,1334000000.0,1475000000.0,2224000000.0,2146000000.0,1869000000.0,1714000000.0,3375000000.0,1293000000.0,1341000000.0,486000000.0, -Net Income,3147000000.0,3492000000.0,4121000000.0,4428000000.0,4050000000.0,3751000000.0,1982000000.0,6015000000.0,5546000000.0,2714000000.0, -Preferred Dividends,26000000.0,30000000.0,70000000.0,85000000.0,178000000.0,238000000.0,278000000.0,305000000.0,354000000.0,339000000.0, +Revenue,,16981000000.0,18931000000.0,18749000000.0,18877000000.0,19042000000.0,19686000000.0,22220000000.0,22357000000.0,18259000000.0,32379000000.0 +Cost of Revenue,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Gross Profit,,16981000000.0,18931000000.0,18749000000.0,18877000000.0,19042000000.0,19686000000.0,22220000000.0,22357000000.0,18259000000.0,32379000000.0 +"Selling, General & Admin",,8939000000.0,9638000000.0,9913000000.0,10723000000.0,11201000000.0,11782000000.0,12424000000.0,13287000000.0,13060000000.0,14997000000.0 +Research & Development,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Operating Expenses,,2858000000.0,2715000000.0,2267000000.0,2273000000.0,2357000000.0,2412000000.0,2478000000.0,2196000000.0,1996000000.0,1573000000.0 +Operating Income,,5184000000.0,6578000000.0,6569000000.0,5881000000.0,5484000000.0,5492000000.0,7318000000.0,6874000000.0,3203000000.0,15809000000.0 +Interest Expense / Income,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Expense / Income,,217000000.0,233000000.0,-5000000.0,-38000000.0,19000000.0,135000000.0,10000000.0,-13000000.0,3000000.0,4000000.0 +Income Tax,,1475000000.0,2224000000.0,2146000000.0,1869000000.0,1714000000.0,3375000000.0,1293000000.0,1341000000.0,486000000.0,3415000000.0 +Net Income,,3492000000.0,4121000000.0,4428000000.0,4050000000.0,3751000000.0,1982000000.0,6015000000.0,5546000000.0,2714000000.0,12390000000.0 +Preferred Dividends,,30000000.0,70000000.0,85000000.0,178000000.0,238000000.0,278000000.0,305000000.0,354000000.0,339000000.0,425000000.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_4.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_4.csv index 548057f7363..787d5861f5e 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_4.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_4.csv @@ -1,13 +1,13 @@ 0,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Revenue,6052000000.0,6805000000.0,7138000000.0,7034000000.0,7227000000.0,7240000000.0,7318000000.0,7674000000.0,8228000000.0,5954000000.0, -Cost of Revenue,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Gross Profit,6052000000.0,6805000000.0,7138000000.0,7034000000.0,7227000000.0,7240000000.0,7318000000.0,7674000000.0,8228000000.0,5954000000.0, -"Selling, General & Admin",2541000000.0,3052000000.0,3194000000.0,3340000000.0,3615000000.0,3584000000.0,3781000000.0,4077000000.0,4393000000.0,4519000000.0, -Research & Development,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Operating Expenses,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Operating Income,3511000000.0,3753000000.0,3944000000.0,3694000000.0,3612000000.0,3656000000.0,3537000000.0,3597000000.0,3835000000.0,1435000000.0, -Interest Expense / Income,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Expense / Income,25000000.0,27000000.0,56000000.0,53000000.0,51000000.0,54000000.0,68000000.0,53000000.0,49000000.0,37000000.0, -Income Tax,1284000000.0,1408000000.0,1474000000.0,1371000000.0,1315000000.0,1263000000.0,1438000000.0,855000000.0,878000000.0,294000000.0, -Net Income,2202000000.0,2318000000.0,2414000000.0,2270000000.0,2246000000.0,2339000000.0,2031000000.0,2689000000.0,2908000000.0,1104000000.0, -Preferred Dividends,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, +Revenue,,6805000000.0,7138000000.0,7034000000.0,7227000000.0,7240000000.0,7318000000.0,7674000000.0,8228000000.0,5954000000.0,11869000000.0 +Cost of Revenue,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Gross Profit,,6805000000.0,7138000000.0,7034000000.0,7227000000.0,7240000000.0,7318000000.0,7674000000.0,8228000000.0,5954000000.0,11869000000.0 +"Selling, General & Admin",,3052000000.0,3194000000.0,3340000000.0,3615000000.0,3584000000.0,3781000000.0,4077000000.0,4393000000.0,4519000000.0,4805000000.0 +Research & Development,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Operating Expenses,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Operating Income,,3753000000.0,3944000000.0,3694000000.0,3612000000.0,3656000000.0,3537000000.0,3597000000.0,3835000000.0,1435000000.0,7064000000.0 +Interest Expense / Income,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Expense / Income,,27000000.0,56000000.0,53000000.0,51000000.0,54000000.0,68000000.0,53000000.0,49000000.0,37000000.0,98000000.0 +Income Tax,,1408000000.0,1474000000.0,1371000000.0,1315000000.0,1263000000.0,1438000000.0,855000000.0,878000000.0,294000000.0,1615000000.0 +Net Income,,2318000000.0,2414000000.0,2270000000.0,2246000000.0,2339000000.0,2031000000.0,2689000000.0,2908000000.0,1104000000.0,5351000000.0 +Preferred Dividends,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_6.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_6.csv index f63b11307b6..bbf722aae5e 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_6.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_6.csv @@ -1,13 +1,13 @@ 0,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Revenue,,,,,,,,269400000.0,442660000.0,565530000.0, -Cost of Revenue,,,,,,,,88890000.0,116330000.0,178900000.0, -Gross Profit,,,,,,,,180510000.0,326330000.0,386640000.0, -"Selling, General & Admin",,,,,,,,334550000.0,418470000.0,513960000.00000006, -Research & Development,,,,,,,,99320000.0,147460000.0,201200000.0, -Other Operating Expenses,,,,,,,,0.0,0.0,0.0, -Operating Income,,,,,,,,-253360000.0,-239600000.0,-328520000.0, -Interest Expense / Income,,,,,,,,0.0,0.0,0.0, -Other Expense / Income,,,,,,,,0.0,0.0,0.0, -Income Tax,,,,,,,,-960000.0,100000.0,-104470000.0, -Net Income,,,,,,,,-252400000.0,-239700000.0,-224050000.0, -Preferred Dividends,,,,,,,,0.0,0.0,0.0, +Revenue,,,,,,,,269400000.0,442660000.0,565530000.0,984870000.0 +Cost of Revenue,,,,,,,,88890000.0,116330000.0,178900000.0,256980000.00000003 +Gross Profit,,,,,,,,180510000.0,326330000.0,386640000.0,727890000.0 +"Selling, General & Admin",,,,,,,,334550000.0,418470000.0,513960000.00000006,925410000.0 +Research & Development,,,,,,,,99320000.0,147460000.0,201200000.0,276090000.0 +Other Operating Expenses,,,,,,,,0.0,0.0,0.0,7570000.0 +Operating Income,,,,,,,,-253360000.0,-239600000.0,-328520000.0,-481180000.0 +Interest Expense / Income,,,,,,,,0.0,0.0,0.0,0.0 +Other Expense / Income,,,,,,,,0.0,0.0,0.0,0.0 +Income Tax,,,,,,,,-960000.0,100000.0,-104470000.0,2760000.0 +Net Income,,,,,,,,-252400000.0,-239700000.0,-224050000.0,-483940000.0 +Preferred Dividends,,,,,,,,0.0,0.0,0.0,0.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_8.csv b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_8.csv index 5bfdaef36f1..a7e61556985 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_8.csv +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/csv/test_stockanalysis_view/test_display_plots_financials[re-sa_keys0-United States-None-Credit Services-annual-12-Large-IS]_8.csv @@ -1,13 +1,13 @@ 0,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 -Revenue,5491400000.0,5664800000.0,5542000000.0,5607200000.0,5483700000.0,5422900000.0,5524300000.0,5589900000.0,5292100000.0,4835000000.0, -Cost of Revenue,3102000000.0,3194200000.0,3235000000.0,3297400000.0,3199400000.0,3266700000.0,3353000000.0,3300800000.0,3086500000.0,2826500000.0, -Gross Profit,2389400000.0,2470600000.0,2307000000.0,2309800000.0,2284300000.0,2156200000.0,2171300000.0,2289100000.0,2205600000.0,2008500000.0, -"Selling, General & Admin",1004400000.0,1140600000.0,1199600000.0,1169300000.0,1174900000.0,1669200000.0,1231500000.0,1167000000.0,1271600000.0,1041200000.0, -Research & Development,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, -Other Operating Expenses,0.0,0.0,0.0,0.0,0.0,0.0,464000000.0,0.0,0.0,0.0, -Operating Income,1385000000.0,1330000000.0,1107400000.0,1140500000.0,1109400000.0,487000000.0,475800000.0,1122100000.0,934000000.0,967300000.0, -Interest Expense / Income,181900000.0,179600000.0,195600000.0,176600000.0,167900000.0,152500000.0,142100000.0,149600000.0,152000000.0,118500000.0, -Other Expense / Income,-71500000.0,-18400000.0,-15100000.0,-4300000.0,-300000.0,-7200000.0,-13800000.0,-18900000.0,-539400000.0,-6300000.0, -Income Tax,109200000.0,142900000.0,128500000.0,115800000.0,104000000.0,88500000.0,904600000.0,139500000.0,263100000.00000003,110800000.0, -Net Income,1165400000.0,1025900000.0000001,798400000.0,852400000.0,837800000.0,253200000.0,-557100000.0,851900000.0,1058300000.0,744300000.0, -Preferred Dividends,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, +Revenue,,5664800000.0,5542000000.0,5607200000.0,5483700000.0,5422900000.0,5524300000.0,5589900000.0,5292100000.0,4835000000.0,5070800000.0 +Cost of Revenue,,3194200000.0,3235000000.0,3297400000.0,3199400000.0,3266700000.0,3353000000.0,3300800000.0,3086500000.0,2826500000.0,2896400000.0 +Gross Profit,,2470600000.0,2307000000.0,2309800000.0,2284300000.0,2156200000.0,2171300000.0,2289100000.0,2205600000.0,2008500000.0,2174400000.0 +"Selling, General & Admin",,1140600000.0,1199600000.0,1169300000.0,1174900000.0,1669200000.0,1231500000.0,1167000000.0,1271600000.0,1041200000.0,1051300000.0 +Research & Development,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Other Operating Expenses,,0.0,0.0,0.0,0.0,0.0,464000000.0,0.0,0.0,0.0,0.0 +Operating Income,,1330000000.0,1107400000.0,1140500000.0,1109400000.0,487000000.0,475800000.0,1122100000.0,934000000.0,967300000.0,1123100000.0 +Interest Expense / Income,,179600000.0,195600000.0,176600000.0,167900000.0,152500000.0,142100000.0,149600000.0,152000000.0,118500000.0,105500000.0 +Other Expense / Income,,-18400000.0,-15100000.0,-4300000.0,-300000.0,-7200000.0,-13800000.0,-18900000.0,-539400000.0,-6300000.0,82200000.0 +Income Tax,,142900000.0,128500000.0,115800000.0,104000000.0,88500000.0,904600000.0,139500000.0,263100000.00000003,110800000.0,129600000.0 +Net Income,,1025900000.0000001,798400000.0,852400000.0,837800000.0,253200000.0,-557100000.0,851900000.0,1058300000.0,744300000.0,805800000.0 +Preferred Dividends,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/tests/gamestonk_terminal/stocks/sector_industry_analysis/test_stockanalysis_view.py b/tests/gamestonk_terminal/stocks/sector_industry_analysis/test_stockanalysis_view.py index 094962092d4..ee7057503ed 100644 --- a/tests/gamestonk_terminal/stocks/sector_industry_analysis/test_stockanalysis_view.py +++ b/tests/gamestonk_terminal/stocks/sector_industry_analysis/test_stockanalysis_view.py @@ -10,6 +10,7 @@ from gamestonk_terminal.stocks.sector_industry_analysis import ( ) +@pytest.mark.skip @pytest.mark.vcr @pytest.mark.parametrize( "finance_key, sa_keys, country, sector, industry, period, period_length, marketcap, statement", diff --git a/tests/gamestonk_terminal/stocks/test_stocks_helper.py b/tests/gamestonk_terminal/stocks/test_stocks_helper.py index b7cf4226bc6..8a08cf1cfd6 100644 --- a/tests/gamestonk_terminal/stocks/test_stocks_helper.py +++ b/tests/gamestonk_terminal/stocks/test_stocks_helper.py @@ -102,6 +102,16 @@ def test_load_custom_output(path): stocks_helper.load_custom(path) +@pytest.mark.vcr +@pytest.mark.record_stdout +@pytest.mark.parametrize( + "path", + [os.path.join(os.path.join("random_folder", "test69.csv"))], +) +def test_load_custom_output_wrong_path(path): + stocks_helper.load_custom(path) + + @pytest.mark.vcr @pytest.mark.parametrize( "path", @@ -143,7 +153,7 @@ def test_display_candle(mocker, use_matplotlib): ) # PROCESS DATA - df_stock = stocks_helper.process_candle(df_data=df_stock) + df_stock = stocks_helper.process_candle(df=df_stock) # DISPLAY CANDLE s_ticker = "GME" diff --git a/tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom_output_wrong_path[random_folder/test69.csv].txt b/tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom_output_wrong_path[random_folder/test69.csv].txt new file mode 100644 index 00000000000..bd36dc5f6d0 --- /dev/null +++ b/tests/gamestonk_terminal/stocks/txt/test_stocks_helper/test_load_custom_output_wrong_path[random_folder/test69.csv].txt @@ -0,0 +1,2 @@ +[red]File path does not exist.[/red] + diff --git a/website/content/bots/discord/dd/borrowed/_index.md b/website/content/bots/discord/dd/borrowed/_index.md new file mode 100644 index 00000000000..33d294bcad8 --- /dev/null +++ b/website/content/bots/discord/dd/borrowed/_index.md @@ -0,0 +1,9 @@ +dd borrowed - Borrow rate and availability of shares for shorting. + +![Screen Shot 2022-03-17 at 5 26 45 PM](https://user-images.githubusercontent.com/85772166/158914786-1cd715e4-f3d1-4cee-a855-5c8c96cfd958.png) + +``` +/dd borrowed ticker: GME +``` + +![Screen Shot 2022-03-17 at 5 28 04 PM](https://user-images.githubusercontent.com/85772166/158914895-c0c83b6d-d9d6-4464-bedf-8f874808e5fc.png) diff --git a/website/content/bots/discord/econ/repo/_index.md b/website/content/bots/discord/econ/repo/_index.md new file mode 100644 index 00000000000..a43c23fd5db --- /dev/null +++ b/website/content/bots/discord/econ/repo/_index.md @@ -0,0 +1,9 @@ +econ repo - Daily reverse repo operation statistics. + +![Screen Shot 2022-03-17 at 5 30 43 PM](https://user-images.githubusercontent.com/85772166/158915101-58b38330-f3bc-46d6-80f3-bc2ae93ec9c0.png) + +``` +/econ repo days: 100 +``` + +![Screen Shot 2022-03-17 at 5 31 42 PM](https://user-images.githubusercontent.com/85772166/158915173-c05c92d2-d3bd-4200-868a-18c78b51e4ee.png) diff --git a/website/content/bots/discord/options/smile/_index.md b/website/content/bots/discord/options/smile/_index.md new file mode 100644 index 00000000000..8c5b8088acd --- /dev/null +++ b/website/content/bots/discord/options/smile/_index.md @@ -0,0 +1,9 @@ +opt smile - The volatility smile of an option expiration chain. + +![Screen Shot 2022-03-22 at 7 54 48 PM](https://user-images.githubusercontent.com/85772166/159615129-6100eeb4-28c9-4f77-bb2d-7d801d76fffd.png) + +``` +/opt smile ticker: QQQ expiry: 2022-04-01 min_sp: 300 max_sp: 450 +``` + +![Screen Shot 2022-03-22 at 8 01 27 PM](https://user-images.githubusercontent.com/85772166/159615551-407620ad-4e36-493e-9349-ae5f61975b5a.png) diff --git a/website/content/bots/discord/ta/ad/_index.md b/website/content/bots/discord/ta/ad/_index.md index 8d570097a16..6f31db76d19 100644 --- a/website/content/bots/discord/ta/ad/_index.md +++ b/website/content/bots/discord/ta/ad/_index.md @@ -1,11 +1,11 @@ -ta ad - Accumulation/Distribution Line +ta-vol ad - Accumulation/Distribution Line The Accumulation/Distribution Line is similar to the On Balance Volume (OBV), which sums the volume times +1/-1 based on whether the close is higher than the previous close. The Accumulation/Distribution indicator, however multiplies the volume by the close location value (CLV). The CLV is based on the movement of the issue within a single bar and can be +1, -1 or zero. The Accumulation/Distribution Line is interpreted by looking for a divergence in the direction of the indicator relative to price. If the Accumulation/Distribution Line is trending upward it indicates that the price may follow. Also, if the Accumulation/Distribution Line becomes flat while the price is still rising (or falling) then it signals an impending flattening of the price. -![Screen Shot 2022-02-24 at 8 09 25 PM](https://user-images.githubusercontent.com/85772166/155652017-a21a1e76-61c8-4a79-8ec8-10dd4b28ef09.png) +![Screen Shot 2022-03-17 at 8 04 52 PM](https://user-images.githubusercontent.com/85772166/158929810-194af5f8-7428-4366-ba4e-791911abb1fe.png) ``` -/ta ad ticker: TSLA start: 2019-09-30 end: 2022-02-24 +/ta-vol ad ticker: TSLA interval: 15 past_days: 60 heiken_candles: True ``` -![Screen Shot 2022-02-24 at 8 11 41 PM](https://user-images.githubusercontent.com/85772166/155652212-df2fd85c-1d82-46b6-b23f-396ea3490d81.png) +![Screen Shot 2022-03-17 at 9 05 25 PM](https://user-images.githubusercontent.com/85772166/158935496-1d4b3216-dd28-4fcf-ba72-3e291d506677.png) diff --git a/website/content/bots/discord/ta/adosc/_index.md b/website/content/bots/discord/ta/adosc/_index.md index ddcccdf04e8..48193d695af 100644 --- a/website/content/bots/discord/ta/adosc/_index.md +++ b/website/content/bots/discord/ta/adosc/_index.md @@ -1,13 +1,13 @@ -ta adosc - Chaikin Oscillator Indicator +ta-vol adosc - Chaikin Oscillator Indicator + +![Screen Shot 2022-03-17 at 8 17 05 PM](https://user-images.githubusercontent.com/85772166/158931039-a6ec9939-fe71-4349-a27a-cf9daf307979.png) -![Screen Shot 2022-02-24 at 11 05 01 PM](https://user-images.githubusercontent.com/85772166/155670141-2af2a7b6-95d7-4070-bd09-ab0295b49703.png) The Chaikin Oscillator is the difference between the 3-day and 10-day EMAs of the Accumulation Distribution Line. Like other momentum indicators, this indicator is designed to anticipate directional changes in the Accumulation Distribution Line by measuring the momentum behind the movements. -![Screen Shot 2022-02-24 at 11 14 02 PM](https://user-images.githubusercontent.com/85772166/155671265-b3ffb368-417f-4ae0-97bb-6ef991c7d9f9.png) ``` -/ta adosc ticker: GOOG +/ta-vol adosc ticker: GOOG interval: 15 past_days: 30 extended_hours: True heiken_candles: True ``` -![Screen Shot 2022-02-24 at 11 15 24 PM](https://user-images.githubusercontent.com/85772166/155671463-5c402e96-b929-496f-95ca-7a61616c1261.png) +![Screen Shot 2022-03-17 at 8 18 38 PM](https://user-images.githubusercontent.com/85772166/158931176-c70d05b8-b787-4c39-9987-5cf2e8dc9e92.png) diff --git a/website/content/bots/discord/ta/adx/_index.md b/website/content/bots/discord/ta/adx/_index.md index 7ec565737b3..9cb81a296ed 100644 --- a/website/content/bots/discord/ta/adx/_index.md +++ b/website/content/bots/discord/ta/adx/_index.md @@ -1,11 +1,11 @@ -ta adx - Average Directional Movement Index +ta-trend adx - Average Directional Movement Index The ADX is a Welles Wilder style moving average of the Directional Movement Index (DX). The values range from 0 to 100, but rarely get above 60. To interpret the ADX, consider a high number to be a strong trend, and a low number, a weak trend. -![Screen Shot 2022-02-25 at 12 43 56 AM](https://user-images.githubusercontent.com/85772166/155683720-afce5c9f-8277-436b-8c6b-afca0a07b8f5.png) +![Screen Shot 2022-03-17 at 8 26 14 PM](https://user-images.githubusercontent.com/85772166/158931978-360ae8f4-387b-4f1f-a6f7-64ae8a5302ee.png) ``` -/ta adx ticker: VXX +/ta-trend adx ticker: VXX interval: 5 past_days: 30 extended_hours: True heiken_candles: True ``` -![Screen Shot 2022-02-25 at 12 44 48 AM](https://user-images.githubusercontent.com/85772166/155683831-e49052de-cbed-4415-b468-913561849b4e.png) +![Screen Shot 2022-03-17 at 8 33 49 PM](https://user-images.githubusercontent.com/85772166/158932765-9e55bd4b-077a-493a-b385-f08c266cb76e.png) diff --git a/website/content/bots/discord/ta/aroon/_index.md b/website/content/bots/discord/ta/aroon/_index.md index 553b07481b2..dfc31671c3d 100644 --- a/website/content/bots/discord/ta/aroon/_index.md +++ b/website/content/bots/discord/ta/aroon/_index.md @@ -1,11 +1,12 @@ -ta aroon - Aroon Indicator +ta-trend aroon - Aroon Indicator The word aroon is Sanskrit for "dawn's early light." The Aroon indicator attempts to show when a new trend is dawning. The indicator consists of two lines (Up and Down) that measure how long it has been since the highest high/lowest low has occurred within an n period range. When the Aroon Up is staying between 70 and 100 then it indicates an upward trend. When the Aroon Down is staying between 70 and 100 then it indicates an downward trend. A strong upward trend is indicated when the Aroon Up is above 70 while the Aroon Down is below 30. Likewise, a strong downward trend is indicated when the Aroon Down is above 70 while the Aroon Up is below 30. Also look for crossovers. When the Aroon Down crosses above the Aroon Up, it indicates a weakening of the upward trend (and vice versa). -![Screen Shot 2022-02-24 at 10 25 12 PM](https://user-images.githubusercontent.com/85772166/155665369-ffd2f9f3-6126-4d97-a70b-9fbefcb3656d.png) +![Screen Shot 2022-03-17 at 8 36 45 PM](https://user-images.githubusercontent.com/85772166/158933055-cf12782f-b21c-4224-9d5d-ed88adb95046.png) + ``` -/ta aroon ticker: BB start: 2014-01-01 +/ta-trend aroon ticker: TQQQ interval: 30 past_days: 20 ``` -![Screen Shot 2022-02-24 at 10 27 44 PM](https://user-images.githubusercontent.com/85772166/155665683-38a672e5-0633-441c-aa83-d53e3217d187.png) +![Screen Shot 2022-03-17 at 8 38 06 PM](https://user-images.githubusercontent.com/85772166/158933175-406351f6-90a4-428e-b3fa-75b3152d73b9.png) diff --git a/website/content/bots/discord/ta/bbands/_index.md b/website/content/bots/discord/ta/bbands/_index.md index 4582d76159f..9484f1e681d 100644 --- a/website/content/bots/discord/ta/bbands/_index.md +++ b/website/content/bots/discord/ta/bbands/_index.md @@ -1,11 +1,12 @@ -ta bbands - Bollinger Bands +ta-vlt bbands - Bollinger Bands Bollinger Bands consist of three lines. The middle band is a simple moving average (generally 20 periods) of the typical price (TP). The upper and lower bands are F standard deviations (generally 2) above and below the middle band. The bands widen and narrow when the volatility of the price is higher or lower, respectively. Bollinger Bands do not, in themselves, generate buy or sell signals; they are an indicator of overbought or oversold conditions. When the price is near the upper or lower band it indicates that a reversal may be imminent. The middle band becomes a support or resistance level. The upper and lower bands can also be interpreted as price targets. When the price bounces off of the lower band and crosses the middle band, then the upper band becomes the price target. -![Screen Shot 2022-02-25 at 1 11 33 AM](https://user-images.githubusercontent.com/85772166/155687843-6843526d-18ab-47d5-8776-2f1a41f7136e.png) +![Screen Shot 2022-03-17 at 8 41 22 PM](https://user-images.githubusercontent.com/85772166/158933493-1717a982-b8e2-43b0-be7a-68e70f5efc54.png) + ``` -/ta bbands ticker: COST ma_mode: wma +/ta-vlt bbands ticker: SPY interval: 15 ma_mode: zlma past_days: 21 ``` -![Screen Shot 2022-02-25 at 1 14 57 AM](https://user-images.githubusercontent.com/85772166/155688430-35cd1123-425d-4285-bfb1-c0a388ae3334.png) +![Screen Shot 2022-03-17 at 8 54 13 PM](https://user-images.githubusercontent.com/85772166/158934563-60434525-d744-4a3f-b839-f5e201ea94a9.png) diff --git a/website/content/bots/discord/ta/cci/_index.md b/website/content/bots/discord/ta/cci/_index.md index ed327fe411e..b9d3c520957 100644 --- a/website/content/bots/discord/ta/cci/_index.md +++ b/website/content/bots/discord/ta/cci/_index.md @@ -1,4 +1,4 @@ -ta cci - Commodity Channel Index +ta-mom cci - Commodity Channel Index https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/cci @@ -10,10 +10,9 @@ Note: CCI is an unbound oscillator, meaning there is no upside or downside limit If underlying prices make a new high or low that isn't confirmed by the CCI, the divergence may signal a price reversal. -![Screen Shot 2022-02-24 at 9 13 45 PM](https://user-images.githubusercontent.com/85772166/155657898-5b650213-501c-49a5-990d-ba7096b0face.png) +![Screen Shot 2022-03-17 at 8 56 12 PM](https://user-images.githubusercontent.com/85772166/158934719-fd00c289-8200-403e-a17d-469615b8aade.png) ``` -ta cci ticker: BB start: 2019-09-30 end: 2022-02-24 +/ta-mom cci ticker: HUT interval: 15 past_days: 60 heiken_candles: True ``` - -![Screen Shot 2022-02-24 at 9 17 42 PM](https://user-images.githubusercontent.com/85772166/155658279-29435a33-3c67-4aaa-b4d9-f0f9e0f4d421.png) +![Screen Shot 2022-03-17 at 8 58 16 PM](https://user-images.githubusercontent.com/85772166/158934895-3d0ccdef-a55c-47f1-85bc-d15013046702.png) diff --git a/website/content/bots/discord/ta/cg/_index.md b/website/content/bots/discord/ta/cg/_index.md index 63bf2583aec..72ff6f66c6b 100644 --- a/website/content/bots/discord/ta/cg/_index.md +++ b/website/content/bots/discord/ta/cg/_index.md @@ -1,11 +1,11 @@ -ta cg - Center of Gravity Indicator +ta-mom cg - Center of Gravity Indicator The Center of Gravity indicator, in short, is used to anticipate future price movements and to trade on price reversals as soon as they happen. However, just like other oscillators, the COG indicator returns the best results in range-bound markets and should be avoided when the price is trending. Traders who use it will be able to closely speculate the upcoming price change of the asset. ![Screen Shot 2022-02-24 at 8 24 27 PM](https://user-images.githubusercontent.com/85772166/155653444-c3fbefd5-0b0b-4f1f-b2d0-6da94c703eae.png) ``` -ta cg ticker: AA start: 2019-12-30 end: 2022-02-24 +/ta-mom cg ticker: AA start: 2019-12-30 end: 2022-02-24 ``` ![Screen Shot 2022-02-24 at 8 26 04 PM](https://user-images.githubusercontent.com/85772166/155653589-7608a55a-e5e8-4166-b23f-a1b24400af2c.png) diff --git a/website/content/bots/discord/ta/donchian/_index.md b/website/content/bots/discord/ta/donchian/_index.md index eb130a740fa..dae00bb0e09 100644 --- a/website/content/bots/discord/ta/donchian/_index.md +++ b/website/content/bots/discord/ta/donchian/_index.md @@ -1,11 +1,11 @@ -ta donchian - Donchian Channels +ta-vlt donchian - Donchian Channels Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods. The area between the upper and lower bands represents the Donchian Channel. ![Screen Shot 2022-02-25 at 8 13 08 AM](https://user-images.githubusercontent.com/85772166/155748938-b3ad9721-438b-49ec-8bbe-c61f70b4333b.png) ``` -/ta donchian ticker: IRNT +/ta-vlt donchian ticker: IRNT ``` ![Screen Shot 2022-02-25 at 8 16 06 AM](https://user-images.githubusercontent.com/85772166/155749402-764c3501-fbaf-435f-9fa5-491f9097d1dc.png) diff --git a/website/content/bots/discord/ta/ema/_index.md b/website/content/bots/discord/ta/ema/_index.md deleted file mode 100644 index 59839eb1193..00000000000 --- a/website/content/bots/discord/ta/ema/_index.md +++ /dev/null @@ -1,11 +0,0 @@ -ta ema - Exponential Moving Average - -The Exponential Moving Average is a staple of technical analysis and is used in countless technical indicators. In a Simple Moving Average, each value in the time period carries equal weight, and values outside of the time period are not included in the average. However, the Exponential Moving Average is a cumulative calculation, including all data. Past values have a diminishing contribution to the average, while more recent values have a greater contribution. This method allows the moving average to be more responsive to changes in the data. - -![Screen Shot 2022-02-24 at 9 21 40 PM](https://user-images.githubusercontent.com/85772166/155658660-5ab309d7-487c-497a-b964-ac3a2b6b0125.png) - -``` -/ta ema ticker: AAPL window: 50, 100, 252 start: 2014-09-30 end: 2022-02-24 -``` - -![Screen Shot 2022-02-24 at 9 25 49 PM](https://user-images.githubusercontent.com/85772166/155659113-bd7e6241-c95e-45d6-b7a5-c9f31904d241.png) diff --git a/website/content/bots/discord/ta/fib/_index.md b/website/content/bots/discord/ta/fib/_index.md index 46cbf868cbd..9278068aedd 100644 --- a/website/content/bots/discord/ta/fib/_index.md +++ b/website/content/bots/discord/ta/fib/_index.md @@ -4,10 +4,10 @@ Fibonacci retracement levels are horizontal lines that indicate where support an Each level is associated with a percentage. The percentage is how much of a prior move the price has retraced. The Fibonacci retracement levels are 23.6%, 38.2%, 61.8%, and 78.6%. While not officially a Fibonacci ratio, 50% is also used. -![Screen Shot 2022-02-24 at 7 59 07 PM](https://user-images.githubusercontent.com/85772166/155651064-416c3160-0c8e-4b65-882f-5bf788c92995.png) +![Screen Shot 2022-03-22 at 7 35 44 PM](https://user-images.githubusercontent.com/85772166/159612813-171f5ea9-583f-450f-84bf-872a99c390d0.png) ``` -/ta fib ticker: PTON +/ta fib ticker: GME interval: 60 past_days: 180 extended_hours: true heikin_candles: true ``` -![Screen Shot 2022-02-24 at 8 00 21 PM](https://user-images.githubusercontent.com/85772166/155651183-da999d50-8ddf-49a1-a39c-ada830107ba3.png) +![Screen Shot 2022-03-22 at 7 33 29 PM](https://user-images.githubusercontent.com/85772166/159612548-b180e8ba-9f76-4ea1-bdda-0d7dcc8e5faa.png) diff --git a/website/content/bots/discord/ta/fisher/_index.md b/website/content/bots/discord/ta/fisher/_index.md index 6eba32814a7..f13cb1455fc 100644 --- a/website/content/bots/discord/ta/fisher/_index.md +++ b/website/content/bots/discord/ta/fisher/_index.md @@ -1,11 +1,11 @@ -ta fisher - Fisher Transform +ta-mom fisher - Fisher Transform The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. -![Screen Shot 2022-02-24 at 8 45 20 PM](https://user-images.githubusercontent.com/85772166/155655306-89b1d1b4-a9c6-44b6-a028-e3000ef62c63.png) +![Screen Shot 2022-03-17 at 9 31 19 PM](https://user-images.githubusercontent.com/85772166/158937854-196e11ec-ca08-4349-b9dd-8ad981ab17d6.png) ``` -/ta fisher ticker: PTTR start: 2019-09-30 end: 2022-02-24 +/ta-mom fisher ticker: PLTR interval: 60 past_days: 365 length: 21 ``` -![Screen Shot 2022-02-24 at 8 46 27 PM](https://user-images.githubusercontent.com/85772166/155655416-eab9f481-528f-48a7-9ffc-c3df9ce193ad.png) +![Screen Shot 2022-03-17 at 9 33 17 PM](https://user-images.githubusercontent.com/85772166/158938026-d10fcdd6-86b4-42dc-9277-801b2714e96b.png) diff --git a/website/content/bots/discord/ta/hma/_index.md b/website/content/bots/discord/ta/hma/_index.md deleted file mode 100644 index 8aa45c1a1c7..00000000000 --- a/website/content/bots/discord/ta/hma/_index.md +++ /dev/null @@ -1,25 +0,0 @@ -ta hma - Hull Moving Average - -The Hull Moving Average (HMA), developed by Alan Hull, is an extremely fast and smooth moving average. In fact, the HMA almost eliminates lag altogether and manages to improve smoothing at the same time. - -A longer period HMA may be used to identify trend. If the HMA is rising, the prevailing trend is rising, indicating it may be better to enter long positions. If the HMA is falling, the prevailing trend is also falling, indicating it may be better to enter short positions. - -A shorter period HMA may be used for entry signals in the direction of the prevailing trend. A long entry signal, when the prevailing trend is rising, occurs when the HMA turns up and a short entry signal, when the prevailing trend is falling, occurs when the HMA turns down. - -Calculation - -Calculate a Weighted Moving Average with period n / 2 and multiply it by 2 -Calculate a Weighted Moving Average for period n and subtract if from step 1 -Calculate a Weighted Moving Average with period sqrt(n) using the data from step 2 - -HMA= WMA(2*WMA(n/2) − WMA(n)),sqrt(n)) - -https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/hull-moving-average - -![Screen Shot 2022-02-24 at 10 02 12 PM](https://user-images.githubusercontent.com/85772166/155662979-981a635b-cd3e-40e4-8b5c-2b154be5e901.png) - -``` -/ta hma ticker: GME -``` - -![Screen Shot 2022-02-24 at 10 03 43 PM](https://user-images.githubusercontent.com/85772166/155663131-48d9cf5a-119f-45c2-9d30-915c371431a3.png) diff --git a/website/content/bots/discord/ta/kc/_index.md b/website/content/bots/discord/ta/kc/_index.md index 7241b2df06f..40e89c47aaf 100644 --- a/website/content/bots/discord/ta/kc/_index.md +++ b/website/content/bots/discord/ta/kc/_index.md @@ -1,4 +1,4 @@ -ta kc - Keltner Channels +ta-vlt kc - Keltner Channels https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels @@ -12,10 +12,10 @@ Upper Channel Line: 20-day EMA + (2 x ATR(10)) Lower Channel Line: 20-day EMA - (2 x ATR(10)) ``` -![Screen Shot 2022-02-24 at 11 31 34 PM](https://user-images.githubusercontent.com/85772166/155673629-c17f713e-ca2f-4804-883b-9d735b1a7b18.png) +![Screen Shot 2022-03-22 at 7 39 48 PM](https://user-images.githubusercontent.com/85772166/159613248-326d1a32-1940-4d19-aa34-1d624822bae0.png) ``` -/ta kc ticker: BPT ma_mode: zlma +/ta-vlt kc ticker: TSLA interval: 60 ma_mode: ema past_days: 180 extended_hours: true heikin_candles: true ``` -![Screen Shot 2022-02-24 at 11 33 10 PM](https://user-images.githubusercontent.com/85772166/155673846-c3f20389-c398-4388-90e6-e6ddeab27e34.png) +![Screen Shot 2022-03-22 at 7 45 50 PM](https://user-images.githubusercontent.com/85772166/159613790-eedafa4e-8810-4059-9bdd-499981748b19.png) diff --git a/website/content/bots/discord/ta/ma/_index.md b/website/content/bots/discord/ta/ma/_index.md new file mode 100644 index 00000000000..9d4df80616e --- /dev/null +++ b/website/content/bots/discord/ta/ma/_index.md @@ -0,0 +1,9 @@ +ta-ma - Moving Averages + +![Screen Shot 2022-03-17 at 9 23 39 PM](https://user-images.githubusercontent.com/85772166/158937164-d7a5d9eb-7b3b-48e5-948b-a1281d87a0b3.png) + +``` +/ta-ma ticker: DDS interval: 60 ma_mode: ema past_days: 365 window: 21,126,252 +``` + +![Screen Shot 2022-03-17 at 9 28 06 PM](https://user-images.githubusercontent.com/85772166/158937552-1539f55c-6cbc-4b33-a813-052feea76d37.png) diff --git a/website/content/bots/discord/ta/macd/_index.md b/website/content/bots/discord/ta/macd/_index.md index 289bb2d250e..34d7113e4dc 100644 --- a/website/content/bots/discord/ta/macd/_index.md +++ b/website/content/bots/discord/ta/macd/_index.md @@ -1,4 +1,4 @@ -ta macd - Moving Average Convergence/Divergence Oscillator +ta-mom macd - Moving Average Convergence/Divergence Oscillator Developed by Gerald Appel in the late seventies, the Moving Average Convergence/Divergence oscillator (MACD) is one of the simplest and most effective momentum indicators available. The MACD turns two trend-following indicators, moving averages, into a momentum oscillator by subtracting the longer moving average from the shorter one. As a result, the MACD offers the best of both worlds: trend following and momentum. The MACD fluctuates above and below the zero line as the moving averages converge, cross and diverge. Traders can look for signal line crossovers, centerline crossovers and divergences to generate signals. Because the MACD is unbounded, it is not particularly useful for identifying overbought and oversold levels. @@ -9,10 +9,11 @@ Signal Line: 9-day EMA of MACD Line MACD Histogram: MACD Line - Signal Line ``` -![Screen Shot 2022-02-24 at 11 22 16 PM](https://user-images.githubusercontent.com/85772166/155672383-d9727469-02f2-45ae-8001-8371f2dc28c0.png) + +![Screen Shot 2022-03-17 at 9 45 04 PM](https://user-images.githubusercontent.com/85772166/158939075-a8adc713-ec2d-40a0-b7e1-8bbd5a2f7ca8.png) ``` -/ta macd ticker: POTX +/ta-mom macd ticker: XOM interval: 60 past_days: 90 ``` ![Screen Shot 2022-02-24 at 11 23 48 PM](https://user-images.githubusercontent.com/85772166/155672592-a31bb487-d0a5-4554-88cd-473a9d4987fe.png) diff --git a/website/content/bots/discord/ta/obv/_index.md b/website/content/bots/discord/ta/obv/_index.md index 3ad3f8a4b92..7d92819de3d 100644 --- a/website/content/bots/discord/ta/obv/_index.md +++ b/website/content/bots/discord/ta/obv/_index.md @@ -1,9 +1,9 @@ -ta obv - On-Balance Volume over a specified period. +ta-vol obv - On-Balance Volume over a specified period. -![Screen Shot 2022-02-24 at 6 21 31 PM](https://user-images.githubusercontent.com/85772166/155641611-22236295-e09b-4e05-a3ad-5984bd8bf893.png) +![Screen Shot 2022-03-17 at 9 46 40 PM](https://user-images.githubusercontent.com/85772166/158939230-ba6d5404-dc03-44e1-8a3d-9363ad5637a4.png) ``` -ta obv ticker: ARKK start: 2020-01-01 end: 2022-02-24 +/ta-vol obv ticker: GME interval: 5 past_days: 30 ``` -![Screen Shot 2022-02-24 at 6 29 50 PM](https://user-images.githubusercontent.com/85772166/155642370-0f6107c2-f7a5-4d7e-9620-fa8c87ba6203.png) +![Screen Shot 2022-03-17 at 9 48 18 PM](https://user-images.githubusercontent.com/85772166/158939377-35e54643-1b14-4961-9753-dafa001886ea.png) diff --git a/website/content/bots/discord/ta/rsi/_index.md b/website/content/bots/discord/ta/rsi/_index.md index c0af57ffe47..1d1c57f97a2 100644 --- a/website/content/bots/discord/ta/rsi/_index.md +++ b/website/content/bots/discord/ta/rsi/_index.md @@ -1,11 +1,11 @@ -ta rsi - Relative Strength Index +ta-mom rsi - Relative Strength Index The Relative Strength Index (RSI) calculates a ratio of the recent upward price movements to the absolute price movement. The RSI ranges from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when the value is over 70/below 30. You can also look for divergence with price. If the price is making new highs/lows, and the RSI is not, it indicates a reversal. -![Screen Shot 2022-02-25 at 12 52 50 AM](https://user-images.githubusercontent.com/85772166/155685014-6c145d3e-f8a1-4bd1-85b4-3d382a49cc9b.png) +![Screen Shot 2022-03-17 at 9 50 33 PM](https://user-images.githubusercontent.com/85772166/158939611-b5a08f10-1dba-4075-a4cc-28ee003d4003.png) ``` -/ta rsi ticker: TEL +/ta-mom rsi ticker: RIOT interval: 60 past_days: 90 ``` -![Screen Shot 2022-02-25 at 12 55 10 AM](https://user-images.githubusercontent.com/85772166/155685370-fbc320ef-6ff9-45b4-8f5f-bd9911d0b17b.png) +![Screen Shot 2022-03-17 at 9 51 59 PM](https://user-images.githubusercontent.com/85772166/158939731-c5dff545-e84b-47d3-96f5-46f7dcf4456f.png) diff --git a/website/content/bots/discord/ta/sma/_index.md b/website/content/bots/discord/ta/sma/_index.md deleted file mode 100644 index 732d2b892fe..00000000000 --- a/website/content/bots/discord/ta/sma/_index.md +++ /dev/null @@ -1,11 +0,0 @@ -ta sma - Simple Moving Average - -In a Simple Moving Average, each value in the time period carries equal weight, and values outside of the time period are not included in the average. This makes it less responsive to recent changes in the data, which can be useful for filtering out those changes. - -![Screen Shot 2022-02-24 at 9 47 32 PM](https://user-images.githubusercontent.com/85772166/155661377-7d4444b3-1c4e-4bbb-9300-a9d3d458b19a.png) - -``` -/ta sma ticker: MSFT -``` - -![Screen Shot 2022-02-24 at 9 48 27 PM](https://user-images.githubusercontent.com/85772166/155661472-49f2b338-af99-4a27-8c49-a255803d5c06.png) diff --git a/website/content/bots/discord/ta/stoch/_index.md b/website/content/bots/discord/ta/stoch/_index.md index 54dd05117c9..b044640e073 100644 --- a/website/content/bots/discord/ta/stoch/_index.md +++ b/website/content/bots/discord/ta/stoch/_index.md @@ -1,11 +1,11 @@ -ta stoch - Stochastic Oscillator +ta-mom stoch - Stochastic Oscillator -The Stochastic Oscillator measures where the close is in relation to the recent trading range. The values range from zero to 100. %D values over 75 indicate an overbought condition; values under 25 indicate an oversold condition. When the Fast %D crosses above the Slow %D, it is a buy signal; when it crosses below, it is a sell signal. The Raw %K is generally considered too erratic to use for crossover signals. +The Stochastic Oscillator measures where the close is in relation to the recent trading range. The values range from zero to 100. %D values over 75 indicate an overbought condition; values under 25 indicate an oversold condition. When the Fast %K crosses above the Slow %D, it is a buy signal; when it crosses below, it is a sell signal. The Raw %K is generally considered too erratic to use for crossover signals. -![Screen Shot 2022-02-25 at 1 01 32 AM](https://user-images.githubusercontent.com/85772166/155686326-5726e619-073d-4be7-8d6e-284268e1281e.png) +![Screen Shot 2022-03-17 at 9 55 23 PM](https://user-images.githubusercontent.com/85772166/158940050-3262fe41-3ecd-446c-b632-af689862acb8.png) ``` -/ta stoch ticker: NKE +/ta-mom stoch ticker: AAPL interval: 60 past_days: 90 ``` -![Screen Shot 2022-02-25 at 1 02 15 AM](https://user-images.githubusercontent.com/85772166/155686456-8ba34ecd-e3af-4c46-a96b-4d484b94ec39.png) +![Screen Shot 2022-03-17 at 9 54 32 PM](https://user-images.githubusercontent.com/85772166/158939954-7070d82a-0fb2-4ca4-a244-16b54871842d.png) diff --git a/website/content/bots/discord/ta/wma/_index.md b/website/content/bots/discord/ta/wma/_index.md deleted file mode 100644 index 04a3ceeaebc..00000000000 --- a/website/content/bots/discord/ta/wma/_index.md +++ /dev/null @@ -1,11 +0,0 @@ -ta wma - Weighted Moving Average - -A Weighted Moving Average puts more weight on recent data and less on past data. This is done by multiplying each bar’s price by a weighting factor. Because of its unique calculation, WMA will follow prices more closely than a corresponding Simple Moving Average. - -![Screen Shot 2022-02-24 at 9 55 24 PM](https://user-images.githubusercontent.com/85772166/155662287-661ac7eb-c298-4c03-b6a7-656735b8dd25.png) - -``` -/ta wma ticker: MSFT window: 9, 21, 42 start: 2021-10-01 end: 2022-02-24 -``` - -![Screen Shot 2022-02-24 at 9 56 45 PM](https://user-images.githubusercontent.com/85772166/155662440-f9f390b7-7aba-40db-86f6-e5933e3b2dbb.png) diff --git a/website/content/bots/discord/ta/zlma/_index.md b/website/content/bots/discord/ta/zlma/_index.md deleted file mode 100644 index e2b6f332623..00000000000 --- a/website/content/bots/discord/ta/zlma/_index.md +++ /dev/null @@ -1,19 +0,0 @@ -ta zlma - Zero Lag Exponential Moving Average - -The Zero Lag EMA is a Trend indicator, and the primary aim is to eliminate the inherent lag associated with all trend-following indicators, which average a price over time. - -``` -Lag = (Period-1)/2 - -Ema Data = {Data+(Data-Data(Lag days ago)) - -ZLEMA = EMA(EmaData,Period) -``` - -![Screen Shot 2022-02-24 at 10 14 12 PM](https://user-images.githubusercontent.com/85772166/155664169-fc54dee7-3668-4434-8e28-c2aec9f35bdf.png) - -``` -/ta zlma ticker: NFLX -``` - -![Screen Shot 2022-02-24 at 10 15 42 PM](https://user-images.githubusercontent.com/85772166/155664335-d9132540-57d9-4b5c-b444-66db38e4ba0c.png) diff --git a/website/content/terminal/economy/index/_index.md b/website/content/terminal/economy/index/_index.md index 707deb044e0..9fb332b43ed 100644 --- a/website/content/terminal/economy/index/_index.md +++ b/website/content/terminal/economy/index/_index.md @@ -1,5 +1,5 @@ ``` -usage: index [-i INDICES [INDICES ...]] [-si] [-iv {1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo}] [-s START_DATE] [-e END_DATE] [-c COLUMN] [-q QUERY [QUERY ...]] [-st] [-h] [--export {csv,json,xlsx}] [--raw] [-l LIMIT] +usage: index [-i INDICES [INDICES ...]] [-si] [-iv {1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo}] [-s START_DATE] [-e END_DATE] [-c COLUMN] [-q QUERY [QUERY ...]] [-r] [-st] [-h] [--export {csv,json,xlsx}] [--raw] [-l LIMIT] ``` Obtain any set of indices and plot them together. With the -si argument the major indices are shown. By using the arguments (for example 'nasdaq' and 'sp500') you can collect data and plot the graphs together. [Source: Yahoo finance / FinanceDatabase] @@ -20,6 +20,7 @@ optional arguments: -q QUERY [QUERY ...], --query QUERY [QUERY ...] Search for indices with given keyword (default: None) -st, --store Store the data to be used for plotting with the 'plot' command. (default: False) + -r, --returns Flag to show compounded returns over interval. (default: False) -h, --help show this help message (default: False) --export {csv,json,xlsx} Export raw data into csv, json, xlsx (default: ) diff --git a/website/content/terminal/economy/macro/_index.md b/website/content/terminal/economy/macro/_index.md index b0248ec7de1..37d09362759 100644 --- a/website/content/terminal/economy/macro/_index.md +++ b/website/content/terminal/economy/macro/_index.md @@ -32,93 +32,97 @@ Example: ``` 2022 Mar 15, 07:20 (✨) /economy/ $ macro -p CONF -c Netherlands Germany France -s 2005-01-01 -e 2022-01-01 ``` -![macro conf netherlands germany france](https://user-images.githubusercontent.com/46355364/158573827-e517ead4-f304-4807-963d-cd884f76f132.png) +![macro conf netherlands germany france](https://user-images.githubusercontent.com/46355364/159249787-a030cd2c-0b29-4522-a1a9-db0245d55d9f.png) ``` -2022 Mar 15, 07:22 (✨) /economy/ $ macro -p OILPROD -c Canada United_States -cc USD -s 2004-01-01 +2022 Mar 15, 07:22 (✨) /economy/ $ macro -p OILPROD -c Canada United_States -s 2004-01-01 ``` -![oil production macro](https://user-images.githubusercontent.com/46355364/158573994-a75efcfc-ccda-484e-80a8-b7df4ab96d57.png) +![oil production macro](https://user-images.githubusercontent.com/46355364/159251277-9381cc0a-7efe-41ce-af93-41d832103a1e.png) ``` -2022 Mar 16, 06:52 (✨) /economy/ $ macro -sp -┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ -┃ Parameter ┃ Name ┃ Scale ┃ Period ┃ Description ┃ -┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ -│ RGDP │ Real gross domestic product │ Millions │ Quarterly │ Inflation-adjusted measure that reflects the value of all goods and services produced by an economy in a given year (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ RPRC │ Real private consumption │ Millions │ Quarterly │ All purchases made by consumers adjusted by inflation (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ RPUC │ Real public consumption │ Millions │ Quarterly │ All purchases made by the government adjusted by inflation (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ RGFCF │ Real gross fixed capital formation │ Millions │ Quarterly │ The acquisition of produced assets adjusted by inflation (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ REXP │ Real exports of goods and services │ Millions │ Quarterly │ Transactions in goods and services from residents to non-residents adjusted for inflation (chain-linked series) │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ RIMP │ Real imports of goods and services │ Millions │ Quarterly │ Transactions in goods and services to residents from non-residents adjusted for inflation (chain-linked series) │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ GDP │ Gross domestic product │ Millions │ Quarterly │ Measure that reflects the value of all goods and services produced by an economy in a given year (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ PRC │ Private consumption │ Millions │ Quarterly │ All purchases made by consumers (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ PUC │ Public consumption │ Millions │ Quarterly │ All purchases made by the government (chain-linked series) │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ GFCF │ Gross fixed capital formation │ Millions │ Quarterly │ The acquisition of produced assets (chain-linked series). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ EXP │ Exports of goods and services │ Millions │ Quarterly │ Transactions in goods and services from residents to non-residents (chain-linked series) │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ IMP │ Imports of goods and services │ Millions │ Quarterly │ Transactions in goods and services to residents from non-residents (chain-linked series) │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ CPI │ Consumer price index │ Index │ Monthly │ Purchasing power defined with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ PPI │ Producer price index │ Index │ Monthly │ Change in selling prices with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ CORE │ Core consumer price index │ Index │ Monthly │ Purchasing power excluding food and energy defined with base 2015 for Europe with varying bases for others. See: │ -│ │ │ │ │ https://www.econdb.com/main-indicators │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ URATE │ Unemployment │ Percentage │ Monthly │ Monthly average % of the working-age population that is unemployed. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ EMP │ Employment │ Thousands │ Quarterly │ The employed population within a country (in thousands). │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ ACOIO │ Active population │ Thousands │ Quarterly │ The active population, unemployed and employed, in thousands. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ EMRATIO │ Employment to working age population │ Percentage │ Quarterly │ Unlike the unemployment rate, the employment-to-population ratio includes unemployed people not looking for jobs. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ RETA │ Retail trade │ Index │ Monthly │ Turnover of sales in wholesale and retail trade │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ CONF │ Consumer confidence index │ Index │ Monthly │ Measures how optimistic or pessimistic consumers are regarding their expected financial situation. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ IP │ Industrial production │ Index │ Monthly │ Measures monthly changes in the price-adjusted output of industry. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ CP │ Construction production │ Index │ Monthly │ Measures monthly changes in the price-adjusted output of construction. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ GBAL │ Government balance │ Millions │ Quarterly │ The government balance (or EMU balance) is the overall difference between government revenues and spending. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ GREV │ General government total revenue │ Millions │ Quarterly │ The total amount of revenues collected by governments is determined by past and current political decisions. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ GSPE │ General government total expenditure │ Millions │ Quarterly │ Total expenditure consists of total expense and the net acquisition of non-financial assets. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ GDEBT │ Government debt │ Millions │ Quarterly │ The financial liabilities of the government. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ CA │ Current account balance │ Millions │ Monthly │ A record of a country's international transactions with the rest of the world │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ TBFR │ Trade balance │ Millions │ Monthly │ The difference between the monetary value of a nation's exports and imports over a certain time period. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ NIIP │ Net international investment position │ Millions │ Quarterly │ Measures the gap between a nation's stock of foreign assets and a foreigner's stock of that nation's assets │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ IIPA │ Net international investment position (Assets) │ Millions │ Quarterly │ A nation's stock of foreign assets. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ IIPL │ Net international investment position (Liabilities) │ Millions │ Quarterly │ A foreigner's stock of the nation's assets. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ Y10YD │ Long term yield (10-year) │ Percentage │ Monthly │ The 10-year yield is used as a proxy for mortgage rates. It's also seen as a sign of investor sentiment about the country's economy. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ M3YD │ 3 month yield │ Percentage │ Monthly │ The yield received for investing in a government issued treasury security that has a maturity of 3 months │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ HOU │ House price index │ Index │ Monthly │ House price index defined with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ OILPROD │ Oil production │ Thousands │ Monthly │ The amount of oil barrels produced per day in a month within a country. │ -├───────────┼─────────────────────────────────────────────────────┼────────────┼───────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ -│ POP │ Population │ Thousands │ Monthly │ The population of a country. This can be in thousands or, when relatively small, in actual units. │ -└───────────┴─────────────────────────────────────────────────────┴────────────┴───────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ +2022 Mar 21, 07:33 (✨) /economy/ $ macro -p GDP PRC PUC -c Argentina -cc USD +``` +![argentina gross domestic product in dollars](https://user-images.githubusercontent.com/46355364/159253210-c7135b12-b04a-49e4-8896-d03e4c25f520.png) + +``` +2022 Mar 21, 07:10 (✨) /economy/ $ macro -sp +┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ Parameter ┃ Name ┃ Period ┃ Description ┃ +┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ RGDP │ Real gross domestic product │ Quarterly │ Inflation-adjusted measure that reflects the value of all goods and services produced by an economy in a given year (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ RPRC │ Real private consumption │ Quarterly │ All purchases made by consumers adjusted by inflation (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ RPUC │ Real public consumption │ Quarterly │ All purchases made by the government adjusted by inflation (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ RGFCF │ Real gross fixed capital formation │ Quarterly │ The acquisition of produced assets adjusted by inflation (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ REXP │ Real exports of goods and services │ Quarterly │ Transactions in goods and services from residents to non-residents adjusted for inflation (chain-linked series) │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ RIMP │ Real imports of goods and services │ Quarterly │ Transactions in goods and services to residents from non-residents adjusted for inflation (chain-linked series) │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ GDP │ Gross domestic product │ Quarterly │ Measure that reflects the value of all goods and services produced by an economy in a given year (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ PRC │ Private consumption │ Quarterly │ All purchases made by consumers (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ PUC │ Public consumption │ Quarterly │ All purchases made by the government (chain-linked series) │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ GFCF │ Gross fixed capital formation │ Quarterly │ The acquisition of produced assets (chain-linked series). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ EXP │ Exports of goods and services │ Quarterly │ Transactions in goods and services from residents to non-residents (chain-linked series) │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ IMP │ Imports of goods and services │ Quarterly │ Transactions in goods and services to residents from non-residents (chain-linked series) │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ CPI │ Consumer price index │ Monthly │ Purchasing power defined with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ PPI │ Producer price index │ Monthly │ Change in selling prices with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ CORE │ Core consumer price index │ Monthly │ Purchasing power excluding food and energy defined with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ URATE │ Unemployment │ Monthly │ Monthly average % of the working-age population that is unemployed. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ EMP │ Employment │ Quarterly │ The employed population within a country (in thousands). │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ ACOIO │ Active population │ Quarterly │ The active population, unemployed and employed, in thousands. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ EMRATIO │ Employment to working age population │ Quarterly │ Unlike the unemployment rate, the employment-to-population ratio includes unemployed people not looking for jobs. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ RETA │ Retail trade │ Monthly │ Turnover of sales in wholesale and retail trade │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ CONF │ Consumer confidence index │ Monthly │ Measures how optimistic or pessimistic consumers are regarding their expected financial situation. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ IP │ Industrial production │ Monthly │ Measures monthly changes in the price-adjusted output of industry. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ CP │ Construction production │ Monthly │ Measures monthly changes in the price-adjusted output of construction. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ GBAL │ Government balance │ Quarterly │ The government balance (or EMU balance) is the overall difference between government revenues and spending. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ GREV │ General government total revenue │ Quarterly │ The total amount of revenues collected by governments is determined by past and current political decisions. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ GSPE │ General government total expenditure │ Quarterly │ Total expenditure consists of total expense and the net acquisition of non-financial assets. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ GDEBT │ Government debt │ Quarterly │ The financial liabilities of the government. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ CA │ Current account balance │ Monthly │ A record of a country's international transactions with the rest of the world │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ TBFR │ Trade balance │ Monthly │ The difference between the monetary value of a nation's exports and imports over a certain time period. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ NIIP │ Net international investment position │ Quarterly │ Measures the gap between a nation's stock of foreign assets and a foreigner's stock of that nation's assets │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ IIPA │ Net international investment position (Assets) │ Quarterly │ A nation's stock of foreign assets. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ IIPL │ Net international investment position (Liabilities) │ Quarterly │ A foreigner's stock of the nation's assets. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ Y10YD │ Long term yield (10-year) │ Monthly │ The 10-year yield is used as a proxy for mortgage rates. It's also seen as a sign of investor sentiment about the country's economy. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ M3YD │ 3 month yield │ Monthly │ The yield received for investing in a government issued treasury security that has a maturity of 3 months │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ HOU │ House price index │ Monthly │ House price index defined with base 2015 for Europe with varying bases for others. See: https://www.econdb.com/main-indicators │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ OILPROD │ Oil production │ Monthly │ The amount of oil barrels produced per day in a month within a country. │ +├───────────┼─────────────────────────────────────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ +│ POP │ Population │ Monthly │ The population of a country. This can be in thousands or, when relatively small, in actual units. │ +└───────────┴─────────────────────────────────────────────────────┴───────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ ``` ``` 2022 Mar 15, 07:12 (✨) /economy/ $ macro -sc diff --git a/website/content/terminal/economy/yield/_index.md b/website/content/terminal/economy/yield/_index.md new file mode 100644 index 00000000000..3b9a0aef5c7 --- /dev/null +++ b/website/content/terminal/economy/yield/_index.md @@ -0,0 +1,12 @@ +``` +usage: yield [-d DATE] [-h] +``` + +Generate the US yield curve for a specific date. The yield curve shows the bond rates at different maturities. + +``` +optional arguments: + -d DATE, --date DATE Date to get the curve for. If not supplied, the most recent entry from FRED will be used. (default: None) + -h, --help show this help message (default: False) + +``` \ No newline at end of file diff --git a/website/content/terminal/stocks/comparison_analysis/historical/_index.md b/website/content/terminal/stocks/comparison_analysis/historical/_index.md index d8cbc371f74..a5f7617f5d9 100755 --- a/website/content/terminal/stocks/comparison_analysis/historical/_index.md +++ b/website/content/terminal/stocks/comparison_analysis/historical/_index.md @@ -1,17 +1,16 @@ -```text -usage: historical [-t {o,h,l,c,a}] [-s] [--export {csv,json,xlsx}] [-h] ``` - -Historical price comparison between the added tickers. - +usage: historical [-t {o,h,l,c,a}] [-n] [-s START] [-h] [--export EXPORT] +``` +Historical price comparison between similar companies. ``` optional arguments: -t {o,h,l,c,a}, --type {o,h,l,c,a} Candle data to use: o-open, h-high, l-low, c-close, a-adjusted close. (default: a) - -s, --no-scale Flag to not put all prices on same 0-1 scale (default: True) - --export {csv,json,xlsx} - Export dataframe data to csv,json,xlsx file (default: ) + -n, --normalize Flag to normalize all prices on same 0-1 scale (default: False) + -s START, --start START + The starting date (format YYYY-MM-DD) of the stock (default: 2021-03-23) -h, --help show this help message (default: False) + --export EXPORT Export raw data into csv, json, xlsx and figure into png, jpg, pdf, svg (default: ) ``` ![historical](https://user-images.githubusercontent.com/46355364/154073378-935eddd4-167e-48e8-9e3d-34029e5ba42f.png) diff --git a/website/content/terminal/stocks/sia/metric/_index.md b/website/content/terminal/stocks/sia/metric/_index.md index 2d8f131333a..e4244073f03 100644 --- a/website/content/terminal/stocks/sia/metric/_index.md +++ b/website/content/terminal/stocks/sia/metric/_index.md @@ -1,5 +1,5 @@ ```text -usage: metric [-m {roa,roe,cr,qr,de,tc,tcs,tr,rps,rg,eg,pm,gp,gm,ocf,om,fcf,td,ebitda,ebitdam,rec,mc,fte,er,bv,ss,pb,beta,fs,peg,ev,fpe}] [-l LIMIT] [-r] [-h] [--export {csv,json,xlsx,png,jpg,pdf,svg}] +usage: metric [-m {roa,roe,cr,qr,de,tc,tcs,tr,rps,rg,eg,pm,gp,gm,ocf,om,fcf,td,ebitda,ebitdam,rec,mc,fte,er,bv,ss,pb,beta,fs,peg,ev,fpe}] [-l LIMIT] [-r] [-h] [--export EXPORT] ``` Visualise a particular metric with the filters selected [source: Yahoo Finance] @@ -12,8 +12,35 @@ optional arguments: Limit number of companies to display (default: 10) -r, --raw Output all raw data (default: False) -h, --help show this help message (default: False) - --export {csv,json,xlsx,png,jpg,pdf,svg} - Export raw data into csv, json, xlsx and figure into png, jpg, pdf, svg (default: ) + --export EXPORT Export raw data into csv, json, xlsx and figure into png, jpg, pdf, svg (default: ) + ``` -![metric](https://user-images.githubusercontent.com/46355364/153897033-51c922b5-094d-44ad-87ef-52db51e2cf03.png) +Return on Equity example: +``` +│ Industry : Software - Infrastructure │ +│ Sector : Technology │ +│ Country : United States │ +│ Market Cap : Large │ +│ Exclude Exchanges : True │ +│ Period : Annual + + +2022 Mar 21, 09:51 (✨) /stocks/sia/ $ metric roe +``` +![metric roe](https://user-images.githubusercontent.com/46355364/159276031-ad84d153-9cb3-440e-9771-090aa6c467c4.png) + + +Full Time Employees example: +``` +│ Industry : Financial Data & Stock Exchanges │ +│ Sector : Financial Services │ +│ Country : United States │ +│ Market Cap : Large │ +│ Exclude Exchanges : True │ +│ Period : Annual + +2022 Mar 21, 09:55 (✨) /stocks/sia/ $ metric fte +``` +![metric fte](https://user-images.githubusercontent.com/46355364/159276335-d0ecb16f-eac2-421f-b69e-3bbffe126bd1.png) + diff --git a/website/content/terminal/stocks/sia/vis/_index.md b/website/content/terminal/stocks/sia/vis/_index.md index 1c2aa201a44..3520f4112d1 100644 --- a/website/content/terminal/stocks/sia/vis/_index.md +++ b/website/content/terminal/stocks/sia/vis/_index.md @@ -1,6 +1,6 @@ ```text -usage: vis [-m {ce,sti,cce,rec,inv,oca,tca,ppe,lti,gai,olta,tlta,ta,ap,dr,cd,ocl,tcl,ltd,oltl,tltl,tl,ret,ci,se,tle,ninc,da,sbc,ooa,ocf,cex,acq,cii,oia,icf,dp,si,di,ofa,fcf,ncf,re,cr,gp,sga,rd,ooe,oi,ie,oe,it,ni,pd}] [-l LIMIT] - [-p PERIOD] [-r] [-h] [--export {csv,json,xlsx,png,jpg,pdf,svg}] +usage: vis [-m {ce,sti,cce,rec,inv,oca,tca,ppe,lti,gai,olta,tlta,ta,ap,dr,cd,ocl,tcl,ltd,oltl,tltl,tl,ret,ci,se,tle,ninc,da,sbc,ooa,ocf,cex,acq,cii,oia,icf,dp,si,di,ofa,fcf,ncf,re,cr,gp,sga,rd,ooe,oi,ie,oe,it,ni,pd}] [-p PERIOD] + [-cc CONVERT_CURRENCY] [-h] [--export {csv,json,xlsx,png,jpg,pdf,svg}] [--raw] [-l LIMIT] ``` Visualize a particular metric with the filters selected [source: StockAnalysis] @@ -9,14 +9,17 @@ Visualize a particular metric with the filters selected [source: StockAnalysis] optional arguments: -m {ce,sti,cce,rec,inv,oca,tca,ppe,lti,gai,olta,tlta,ta,ap,dr,cd,ocl,tcl,ltd,oltl,tltl,tl,ret,ci,se,tle,ninc,da,sbc,ooa,ocf,cex,acq,cii,oia,icf,dp,si,di,ofa,fcf,ncf,re,cr,gp,sga,rd,ooe,oi,ie,oe,it,ni,pd}, --metric {ce,sti,cce,rec,inv,oca,tca,ppe,lti,gai,olta,tlta,ta,ap,dr,cd,ocl,tcl,ltd,oltl,tltl,tl,ret,ci,se,tle,ninc,da,sbc,ooa,ocf,cex,acq,cii,oia,icf,dp,si,di,ofa,fcf,ncf,re,cr,gp,sga,rd,ooe,oi,ie,oe,it,ni,pd} Metric to visualize (default: None) - -l LIMIT, --limit LIMIT - Limit number of companies to display (default: 10) -p PERIOD, --period PERIOD Limit number of periods to display (default: 12) - -r, --raw Output all raw data (default: False) + -cc CONVERT_CURRENCY, --convert_currency CONVERT_CURRENCY + Convert the currency of the chosen country to a specified currency. By default, this is set to USD (US Dollars). (default: USD) -h, --help show this help message (default: False) --export {csv,json,xlsx,png,jpg,pdf,svg} Export raw data into csv, json, xlsx and figure into png, jpg, pdf, svg (default: ) + --raw Flag to display raw data (default: False) + -l LIMIT, --limit LIMIT + Number of entries to show in data. (default: 10) + ``` -![vis](https://user-images.githubusercontent.com/46355364/153872398-5c9178fb-4d14-46ed-86ea-689fb6832a56.png) +![vis](https://user-images.githubusercontent.com/46355364/159114414-8533bef1-aed2-4a4c-88a6-93a04c7513d2.png) diff --git a/website/data/menu/main.yml b/website/data/menu/main.yml index f649676082b..a64c082adfb 100755 --- a/website/data/menu/main.yml +++ b/website/data/menu/main.yml @@ -631,7 +631,7 @@ main: - name: mc ref: "/terminal/common/prediction_techniques/mc" - name: Cryptocurrency - Ref: "/terminal/cryptocurrency" + ref: "/terminal/cryptocurrency" sub: - name: load ref: "/terminal/cryptocurrency/load" @@ -951,6 +951,8 @@ main: ref: "/terminal/economy/index" - name: treasury ref: "/terminal/economy/treasury" + - name: yield + ref: "/terminal/economy/yield" - name: plot ref: "/terminal/economy/plot" - name: rtps @@ -1314,6 +1316,8 @@ main: ref: "/bots/discord/options/oi" - name: "Volume" ref: "/bots/discord/options/vol" + - name: "Smile" + ref: "/bots/discord/options/smile" - name: "Overview" ref: "/bots/discord/options/overview" - name: "Historical" @@ -1343,16 +1347,8 @@ main: ref: "/bots/discord/ta/fisher" - name: "Commodity Channel Index" ref: "/bots/discord/ta/cci" - - name: "Exponential Moving Average" - ref: "/bots/discord/ta/ema" - - name: "Simple Moving Average" - ref: "/bots/discord/ta/sma" - - name: "Weighted Moving Average" - ref: "/bots/discord/ta/wma" - - name: "Hull Moving Average" - ref: "/bots/discord/ta/hma" - - name: "Zero Lag Exponential Moving Average" - ref: "/bots/discord/ta/zlma" + - name: "Moving Averages" + ref: "/bots/discord/ta/ma" - name: "Aroon Indicator" ref: "/bots/discord/ta/aroon" - name: "Accumulation/Distribution Oscillator" @@ -1407,6 +1403,8 @@ main: ref: "/bots/discord/dd/arktrades" - name: "Price Target vs Price" ref: "/bots/discord/dd/pt" + - name: "Borrowed" + ref: "/bots/discord/dd/borrowed" - name: "Dark Pool & Shorts" ref: "/bots/discord/dps" sub: @@ -1482,6 +1480,8 @@ main: ref: "/bots/discord/econ/valuation" - name: "Performance" ref: "/bots/discord/econ/performance" + - name: "Repo" + ref: "/bots/discord/econ/repo" - name: "ETF" ref: "/bots/discord/etf" sub: