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Code Issues Packages Projects Releases Wiki Activity

Fred menu (#1038)

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This commit is contained in:
jmaslek
2021-12-09 08:08:24 -05:00
committed by GitHub
parent 6c51a584e0
commit d21b070b88
14 changed files with 586 additions and 376 deletions
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120
gamestonk_terminal/economy/economy_controller.py
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@@ -16,7 +16,6 @@ from gamestonk_terminal.economy import (
cnn_view,
finnhub_view,
finviz_view,
fred_view,
nasdaq_model,
wsj_view,
nasdaq_view,
@@ -35,7 +34,7 @@ from gamestonk_terminal.helper_funcs import (
)
from gamestonk_terminal.menu import session
# pylint: disable=R1710,R0904
# pylint: disable=R1710,R0904,C0415
class EconomyController:
@@ -67,6 +66,7 @@ class EconomyController:
"quit",
"reset",
]
CHOICES_MENUS = ["fred"]
CHOICES_COMMANDS = [
"feargreed",
@@ -82,8 +82,6 @@ class EconomyController:
"meats",
"grains",
"softs",
"search",
"series",
"valuation",
"performance",
"spectrum",
@@ -99,7 +97,7 @@ class EconomyController:
"bigmac",
]
CHOICES += CHOICES_COMMANDS
CHOICES += CHOICES_COMMANDS + CHOICES_MENUS
def __init__(self):
"""Constructor"""
@@ -147,11 +145,10 @@ Alpha Vantage:
cpi consumer price index for United States
tyld treasury yields for United States
unemp United States unemployment rates
FRED:
search search FRED series notes
series plot series from https://fred.stlouisfed.org
NASDAQ DataLink (formerly Quandl):
bigmac the economists Big Mac index
> fred Federal Reserve Economic Data submenu
"""
print(help_text)
@@ -1236,102 +1233,6 @@ NASDAQ DataLink (formerly Quandl):
export=ns_parser.export,
)
@try_except
def call_series(self, other_args: List[str]):
"""Process series command"""
parser = argparse.ArgumentParser(
add_help=False,
prog="series",
description="""
Display (multiple) series from https://fred.stlouisfed.org. [Source: FRED]
""",
)
parser.add_argument(
"-i",
"--id",
dest="series_id",
required="-h" not in other_args,
type=str,
help="FRED Series from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3",
)
parser.add_argument(
"-s",
dest="start_date",
type=valid_date,
default="2019-01-01",
help="Starting date (YYYY-MM-DD) of data",
)
parser.add_argument(
"--raw",
action="store_true",
dest="raw",
help="Only output raw data",
)
parser.add_argument(
"--export",
choices=["csv", "json", "xlsx"]
if "--raw" in other_args
else ["png", "jpg", "pdf", "svg"],
default="",
type=str,
dest="export",
help="Export data to csv,json,xlsx or png,jpg,pdf,svg file",
)
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-i")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
fred_view.display_series(
series=ns_parser.series_id,
start_date=ns_parser.start_date,
raw=ns_parser.raw,
export=ns_parser.export,
)
@try_except
def call_search(self, other_args: List[str]):
"""Process search command"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="search",
description="Print series notes when searching for series. [Source: FRED]",
)
parser.add_argument(
"-s",
"--series",
action="store",
dest="series_term",
type=str,
required="-h" not in other_args,
help="Search for this series term.",
)
parser.add_argument(
"-n",
"--num",
action="store",
dest="num",
type=check_positive,
default=5,
help="Maximum number of series notes to display.",
)
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-s")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
fred_view.notes(
series_term=ns_parser.series_term,
num=ns_parser.num,
)
@try_except
def call_bigmac(self, other_args: List[str]):
"""Process bigmac command"""
@@ -1382,6 +1283,17 @@ NASDAQ DataLink (formerly Quandl):
export=ns_parser.export,
)
def call_fred(self, _):
"""Process fred command"""
from gamestonk_terminal.economy.fred import fred_controller
ret = fred_controller.menu()
if ret is False:
self.print_help()
else:
return True
def menu():
"""Econ Menu"""

0
gamestonk_terminal/economy/fred/__init__.py Normal file
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301
gamestonk_terminal/economy/fred/fred_controller.py Normal file
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@@ -0,0 +1,301 @@
"""FRED Controller"""
__docformat__ = "numpy"
import argparse
import difflib
from typing import List
from colorama import Style
from prompt_toolkit.completion import NestedCompleter
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.economy.fred import fred_view, fred_model
from gamestonk_terminal.helper_funcs import (
EXPORT_BOTH_RAW_DATA_AND_FIGURES,
check_positive,
get_flair,
parse_known_args_and_warn,
valid_date,
try_except,
system_clear,
)
from gamestonk_terminal.menu import session
class FredController:
CHOICES = ["cls", "?", "help", "q", "quit"]
CHOICES_COMMANDS = ["search", "add", "rmv", "plot"]
CHOICES += CHOICES_COMMANDS
def __init__(self):
"""Constructor"""
self.fred_parser = argparse.ArgumentParser(add_help=False, prog="fred")
self.fred_parser.add_argument(
"cmd",
choices=self.CHOICES,
)
self.current_series = dict()
self.current_long_id = 0
def print_help(self):
"""Print help"""
id_string = ""
for s_id, sub_dict in self.current_series.items():
id_string += f" {s_id.upper()}{(self.current_long_id-len(s_id)) * ' '} : {sub_dict['title']}\n"
help_text = f"""
What do you want to do?
cls clear screen
?/help show this menu again
q quit this menu, and shows back to main menu
quit quit to abandon program
search search FRED series notes
add add series ID to list
rmv remove series ID from list
Current Series IDs:
{id_string}{Style.DIM if not self.current_series else ""}
plot plot selected series {Style.RESET_ALL}
"""
print(help_text)
def switch(self, an_input: str):
"""Process and dispatch input
Returns
-------
MENU_GO_BACK, MENU_QUIT, MENU_RESET
MENU_GO_BACK - Show main context menu again
MENU_QUIT - Quit terminal
MENU_RESET - Reset terminal and go back to same previous menu
"""
# Empty command
if not an_input:
print("")
return None
(known_args, other_args) = self.fred_parser.parse_known_args(an_input.split())
# Help menu again
if known_args.cmd == "?":
self.print_help()
return None
# Clear screen
if known_args.cmd == "cls":
system_clear()
return None
return getattr(
self, "call_" + known_args.cmd, lambda: "Command not recognized!"
)(other_args)
def call_help(self, _):
"""Process Help command"""
self.print_help()
def call_q(self, _):
"""Process Q command - quit the menu"""
return False
def call_quit(self, _):
"""Process Quit command - exit the program"""
return True
@try_except
def call_search(self, other_args: List[str]):
"""Process search command"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="search",
description="Print series notes when searching for series. [Source: FRED]",
)
parser.add_argument(
"-s",
"--series",
action="store",
dest="series_term",
type=str,
required="-h" not in other_args,
help="Search for this series term.",
)
parser.add_argument(
"-n",
"--num",
action="store",
dest="num",
type=check_positive,
default=5,
help="Maximum number of series notes to display.",
)
if other_args and "-" not in other_args[0]:
other_args.insert(0, "-s")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
fred_view.notes(
series_term=ns_parser.series_term,
num=ns_parser.num,
)
@try_except
def call_add(self, other_args: List[str]):
"""Process add command"""
parser = argparse.ArgumentParser(
add_help=False,
prog="add",
description="Add a FRED series to current selection",
)
parser.add_argument(
"-i",
"--id",
dest="series_id",
required="-h" not in other_args,
type=str,
help="FRED Series from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3",
)
if other_args and "-" not in other_args[0]:
other_args.insert(0, "-i")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
# Loop through entries. If it exists, save title in dictionary
for s_id in ns_parser.series_id.split(","):
exists, information = fred_model.check_series_id(s_id)
if exists:
self.current_series[s_id] = {
"title": information["seriess"][0]["title"],
"units": information["seriess"][0]["units_short"],
}
self.current_long_id = max(self.current_long_id, len(s_id))
print(
f"Current Series: {', '.join(self.current_series.keys()) .upper() or None}\n"
)
@try_except
def call_rmv(self, other_args: List[str]):
"""Process rmv command"""
parser = argparse.ArgumentParser(
add_help=False,
prog="rmv",
description="Remove a FRED series from current selection",
)
parser.add_argument(
"-a",
"--all",
help="Flag to remove all selections",
dest="all",
action="store_true",
default=False,
)
parser.add_argument(
"-i",
"--id",
type=str,
choices=self.current_series.keys(),
required="-h" not in other_args
and "-a" not in other_args
and "--all" not in other_args,
dest="series_id",
help="Specific id's to remove",
)
if other_args:
if (
"-h" not in other_args
and "-i" not in other_args
and "--id" not in other_args
and "-a" not in other_args
and "--all" not in other_args
):
other_args.insert(0, "-i")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
if ns_parser.all:
self.current_series = {}
self.current_long_id = 0
print("")
return
self.current_series.pop(ns_parser.series_id)
print(f"Current Series Ids: {', '.join(self.current_series.keys()) or None}\n")
@try_except
def call_plot(self, other_args):
"""Process plot command"""
parser = argparse.ArgumentParser(
add_help=False,
prog="plot",
description="Plot selected FRED Series",
)
parser.add_argument(
"-s",
dest="start_date",
type=valid_date,
default="2020-01-01",
help="Starting date (YYYY-MM-DD) of data",
)
parser.add_argument(
"--raw",
help="Flag to show raw data",
dest="raw",
action="store_true",
default=False,
)
ns_parser = parse_known_args_and_warn(
parser, other_args, export_allowed=EXPORT_BOTH_RAW_DATA_AND_FIGURES
)
if not ns_parser:
return
fred_view.display_fred_series(
self.current_series, ns_parser.start_date, ns_parser.raw, ns_parser.export
)
def menu():
"""Fred Menu"""
fred_controller = FredController()
fred_controller.print_help()
# Loop forever and ever
while True:
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
completer = NestedCompleter.from_nested_dict(
{c: None for c in fred_controller.CHOICES}
)
an_input = session.prompt(
f"{get_flair()} (economy)(fred)> ",
completer=completer,
)
else:
an_input = input(f"{get_flair()} (economy)(fred)> ")
try:
process_input = fred_controller.switch(an_input)
if process_input is not None:
return process_input
except SystemExit:
print("The command selected doesn't exist\n")
similar_cmd = difflib.get_close_matches(
an_input, fred_controller.CHOICES, n=1, cutoff=0.7
)
if similar_cmd:
print(f"Did you mean '{similar_cmd[0]}'?\n")
continue

37
gamestonk_terminal/economy/fred_model.py → gamestonk_terminal/economy/fred/fred_model.py
View File

@@ -1,23 +1,46 @@
""" Fred Model """
__docformat__ = "numpy"
from typing import List, Tuple
from typing import List, Tuple, Dict
import requests
import fred
import pandas as pd
from fredapi import Fred
from gamestonk_terminal import config_terminal as cfg
from gamestonk_terminal.helper_funcs import get_user_agent
def check_series_id(series_id: str) -> Tuple[bool, Dict]:
"""Checks if series ID exists in fred
Parameters
----------
series_id: str
Series ID to check
Returns
-------
bool:
Boolean if series ID exists
dict:
Dictionary of series information
"""
url = f"https://api.stlouisfed.org/fred/series?series_id={series_id}&api_key={cfg.API_FRED_KEY}&file_type=json"
r = requests.get(url, headers={"User-Agent": get_user_agent()})
# The above returns 200 if series is found
# There seems to be an occasional bug giving a 503 response where the json decoding fails
if r.status_code >= 500:
return False, {}
return r.status_code == 200, r.json()
def get_series_notes(series_term: str) -> pd.DataFrame:
"""Get Series notes. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
Returns
----------
pd.DataFrame
@@ -38,14 +61,12 @@ def get_series_notes(series_term: str) -> pd.DataFrame:
def get_series_ids(series_term: str, num: int) -> Tuple[List[str], List[str]]:
"""Get Series IDs. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
num : int
Maximum number of series IDs to output
Returns
----------
List[str]
@@ -67,16 +88,14 @@ def get_series_ids(series_term: str, num: int) -> Tuple[List[str], List[str]]:
return df_series["id"].values, df_series["title"].values
def get_series_data(series_id: str, start: str):
def get_series_data(series_id: str, start: str) -> pd.DataFrame:
"""Get Series data. [Source: FRED]
Parameters
----------
series_id : str
Series ID to get data from
start : str
Start date to get data from, format yyyy-mm-dd
Returns
----------
pd.DataFrame

145
gamestonk_terminal/economy/fred/fred_view.py Normal file
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@@ -0,0 +1,145 @@
""" Fred View """
__docformat__ = "numpy"
import os
import textwrap
from typing import Dict
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pandas.plotting import register_matplotlib_converters
from tabulate import tabulate
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.economy.fred import fred_model
from gamestonk_terminal.helper_funcs import export_data, plot_autoscale
register_matplotlib_converters()
def format_units(num: int) -> str:
"""Helper to format number into string with K,M,B,T. Number will be in form of 10^n"""
number_zeros = int(np.log10(num))
if number_zeros < 3:
return str(num)
if number_zeros < 6:
return f"{int(num/1000)}K"
if number_zeros < 9:
return f"{int(num/1_000_000)}M"
if number_zeros < 12:
return f"{int(num/1_000_000_000)}B"
if number_zeros < 15:
return f"{int(num/1_000_000_000_000)}T"
return f"10^{number_zeros}"
def notes(series_term: str, num: int):
"""Print Series notes. [Source: FRED]
Parameters
----------
series_term : str
Search for these series_term
num : int
Maximum number of series notes to display
"""
df_search = fred_model.get_series_notes(series_term)
if df_search.empty:
print("No matches found. \n")
return
df_search["notes"] = df_search["notes"].apply(
lambda x: "\n".join(textwrap.wrap(x, width=100)) if isinstance(x, str) else x
)
df_search["title"] = df_search["title"].apply(
lambda x: "\n".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x
)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_search[["id", "title", "notes"]].head(num),
tablefmt="fancy_grid",
headers=["Series ID", "Title", "Description"],
showindex=False,
)
)
else:
print(df_search[["id", "title", "notes"]].head(num).to_string(index=False))
print("")
def display_fred_series(
d_series: Dict[str, Dict[str, str]],
start_date: str,
raw: bool = False,
export: str = "",
):
"""Display (multiple) series from https://fred.stlouisfed.org. [Source: FRED]
Parameters
----------
series : str
FRED Series ID from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3
start_date : str
Starting date (YYYY-MM-DD) of data
raw : bool
Output only raw data
export : str
Export data to csv,json,xlsx or png,jpg,pdf,svg file
"""
series_ids = list(d_series.keys())
data = pd.DataFrame()
for s_id in series_ids:
data = pd.concat(
[
data,
pd.DataFrame(
fred_model.get_series_data(s_id, start_date), columns=[s_id]
),
],
axis=1,
)
# Try to get everything onto the same 0-10 scale.
# To do so, think in scientific notation. Divide the data by whatever the E would be
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
if len(series_ids) == 1:
s_id = series_ids[0]
sub_dict: Dict = d_series[s_id]
title = f"{sub_dict['title']} ({sub_dict['units']})"
ax.plot(data.index, data, label="\n".join(textwrap.wrap(title, 80)))
else:
for s_id, sub_dict in d_series.items():
data_to_plot = data[s_id].dropna()
exponent = int(np.log10(data_to_plot.max()))
data_to_plot /= 10 ** exponent
multiplier = f"x {format_units(10**exponent)}" if exponent > 0 else ""
title = f"{sub_dict['title']} ({sub_dict['units']}) {'['+multiplier+']' if multiplier else ''}"
ax.plot(
data_to_plot.index,
data_to_plot,
label="\n".join(textwrap.wrap(title, 80)),
)
ax.legend(prop={"size": 10}, bbox_to_anchor=(0, 1), loc="lower left")
ax.grid()
ax.set_xlim(data.index[0], data.index[-1])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
if gtff.USE_ION:
plt.ion()
plt.gcf().autofmt_xdate()
fig.tight_layout()
plt.show()
if raw:
if gtff.USE_TABULATE_DF:
print(tabulate(data.tail(20), headers=data.columns, tablefmt="fancy_grid"))
else:
print(data.tail(20).to_string())
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"plot",
data,
)
print("")

177
gamestonk_terminal/economy/fred_view.py
View File

@@ -1,177 +0,0 @@
""" Fred View """
__docformat__ = "numpy"
import os
import textwrap
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from tabulate import tabulate
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.economy import fred_model
from gamestonk_terminal.helper_funcs import export_data, plot_autoscale
register_matplotlib_converters()
def notes(series_term: str, num: int):
"""Print Series notes. [Source: FRED]
Parameters
----------
series_term : str
Search for these series_term
num : int
Maximum number of series notes to display
"""
df_search = fred_model.get_series_notes(series_term)
if df_search.empty:
print("No matches found. \n")
return
df_search["notes"] = df_search["notes"].apply(
lambda x: "\n".join(textwrap.wrap(x, width=100)) if isinstance(x, str) else x
)
df_search["title"] = df_search["title"].apply(
lambda x: "\n".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x
)
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_search[["id", "title", "notes"]].head(num),
tablefmt="fancy_grid",
headers=["Series ID", "Title", "Description"],
showindex=False,
)
)
else:
print(df_search[["id", "title", "notes"]].head(num).to_string(index=False))
print("")
def display_series(series: str, start_date: str, raw: bool, export: str):
"""Display (multiple) series from https://fred.stlouisfed.org. [Source: FRED]
Parameters
----------
series : str
FRED Series ID from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3
start_date : str
Starting date (YYYY-MM-DD) of data
raw : bool
Output only raw data
export : str
Export data to csv,json,xlsx or png,jpg,pdf,svg file
"""
if export:
l_series_fred = []
if not raw:
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
ax.axes.get_yaxis().set_visible(False)
plt.subplots_adjust(right=0.9 - series.count(",") * 0.1)
l_colors = [
"tab:blue",
"tab:orange",
"tab:green",
"tab:red",
"tab:purple",
"tab:brown",
"tab:pink",
"tab:gray",
"tab:olive",
"tab:cyan",
]
l_ts_start = []
l_ts_end = []
p = {}
success = -1
success_series = []
success_titles = []
for series_term in series.split(","):
if series_term:
l_series, l_title = fred_model.get_series_ids(series_term, 5)
if len(l_series) == 0:
print(f"No series found for term '{series_term}'\n")
continue
print(f"For '{series_term}', series IDs found: {', '.join(l_series)}.\n")
ser = l_series[0]
ser_title = l_title[0]
df_fred = fred_model.get_series_data(ser, start_date)
if export:
l_series_fred.append(df_fred)
df_fred.index.name = "Date"
if raw:
df_fred.index = df_fred.index.strftime("%d/%m/%Y")
if gtff.USE_TABULATE_DF:
print(
tabulate(
df_fred.dropna().to_frame(),
showindex=True,
headers=[f"{ser}: {ser_title}"],
tablefmt="fancy_grid",
floatfmt=".2f",
),
"\n",
)
else:
print(df_fred.dropna().to_frame().to_string(), "\n")
else:
success += 1
success_series.append(ser.upper())
success_titles.append(ser_title)
axes = ax.twinx()
axes.spines["right"].set_position(("axes", 1 + success * 0.15))
axes.spines["right"].set_color(l_colors[success])
(p[success],) = axes.plot(
df_fred.index,
df_fred.values,
c=l_colors[success],
label=ser.upper(),
)
axes.yaxis.label.set_color(l_colors[success])
l_ts_start.append(df_fred.index[0])
l_ts_end.append(df_fred.index[-1])
if not raw and success > -1:
plt.title("FRED: " + ", ".join(success_series))
plt.xlim(min(l_ts_start), max(l_ts_end))
plt.gcf().autofmt_xdate()
plt.xlabel("Time")
plt.legend(
[val for _, val in p.items()], success_titles, loc="best", prop={"size": 6}
)
plt.gca().spines["left"].set_visible(False)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"series",
)
if gtff.USE_ION:
plt.ion()
plt.show()
if export and raw:
df_data = pd.concat(l_series_fred, axis=1)
df_data.columns = success_series
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"series",
df_data,
)

13
tests/gamestonk_terminal/economy/test_fred_view.py
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@@ -2,10 +2,10 @@
import unittest
from unittest import mock
import io
import pytest
import pandas as pd
from gamestonk_terminal.economy.fred_view import display_series
from gamestonk_terminal.economy.fred.fred_view import display_fred_series
from tests.helpers import check_print
fred_data_mock = """
@@ -27,15 +27,16 @@ Date
"""
@pytest.mark.skip
class TestFredFredView(unittest.TestCase):
@check_print(assert_in="No series found for term")
@mock.patch("gamestonk_terminal.economy.fred_model.get_series_data")
@check_print(assert_in="gdp")
@mock.patch("gamestonk_terminal.economy.fred.fred_model.get_series_data")
def test_display_fred(self, mock_get_series):
fred_data = pd.read_csv(io.StringIO(fred_data_mock), header=0, index_col=0)
mock_get_series.return_value = fred_data
display_series(
series="gdp",
display_fred_series(
d_series={"gdp": {"title": "GDP", "units": "Bil Dollars"}},
start_date="2019-01-01",
raw=True,
export="",

11
website/content/economy/fred/add/_index.md vendored Normal file
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@@ -0,0 +1,11 @@
```
usage: add [-i SERIES_ID] [-h]
```
Add a FRED series to current selection
```
optional arguments:
-i SERIES_ID, --id SERIES_ID
FRED Series from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3
-h, --help show this help message
```

13
website/content/economy/fred/plot/_index.md vendored Normal file
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@@ -0,0 +1,13 @@
```
usage: plot [-s START_DATE] [--raw] [-h] [--export {csv,json,xlsx,png,jpg,pdf,svg}]
```
Plot selected FRED Series
``````
optional arguments:
-s START_DATE Starting date (YYYY-MM-DD) of data
--raw Flag to show raw data
-h, --help show this help message
--export {csv,json,xlsx,png,jpg,pdf,svg}
Export raw data into csv, json, xlsx and figure into png, jpg, pdf, svg
```

12
website/content/economy/fred/rmv/_index.md vendored Normal file
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@@ -0,0 +1,12 @@
```
usage: rmv [-a] [-i {}] [-h]
```
Remove a FRED series from current selection
```
optional arguments:
-a, --all Flag to remove all selections
-i {}, --id {} Specific id's to remove
-h, --help show this help message
```

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website/content/economy/fred/search/_index.md vendored Normal file
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@@ -0,0 +1,42 @@
```
usage: search [-s SERIES_TERM] [-n NUM] [-h]
```
Print series notes when searching for series. [Source: FRED]
```
optional arguments:
-s SERIES_TERM, --series SERIES_TERM
Search for this series term. (default: None)
-n NUM, --num NUM Maximum number of series notes to display. (default: 5)
-h, --help show this help message (default: False)
```
Sample usage:
```python
(✨) (economy)(fred)> search gdp -n 3
╒═════════════════╤═════════════════════════════╤══════════════════════════════════════════════════════════════════════════════════════════════════════╕
│ Series ID │ Title │ Description │
╞═════════════════╪═════════════════════════════╪══════════════════════════════════════════════════════════════════════════════════════════════════════╡
│ GDPC1 │ Real Gross Domestic Product │ BEA Account Code: A191RX Real gross domestic product is the inflation adjusted value of the goods │
│ │ │ and services produced by labor and property located in the United States.For more information see │
│ │ │ the Guide to the National Income and Product Accounts of the United States (NIPA). For more │
│ │ │ information, please visit the Bureau of Economic Analysis │
│ │ │ (http://www.bea.gov/national/pdf/nipaguid.pdf). │
├─────────────────┼─────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ A191RL1Q225SBEA │ Real Gross Domestic Product │ BEA Account Code: A191RL Gross domestic product (GDP) is the value of the goods and services │
│ │ │ produced by the nation's economy less the value of the goods and services used up in production. GDP │
│ │ │ is also equal to the sum of personal consumption expenditures, gross private domestic investment, │
│ │ │ net exports of goods and services, and government consumption expenditures and gross investment. │
│ │ │ Real values are inflation-adjusted estimates—that is, estimates that exclude the effects of price │
│ │ │ changes. For more information about this series, please visit the Bureau of Economic Analysis │
│ │ │ (http://www.bea.gov/national/). │
├─────────────────┼─────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ GDP │ Gross Domestic Product │ BEA Account Code: A191RC Gross domestic product (GDP), the featured measure of U.S. output, is the │
│ │ │ market value of the goods and services produced by labor and property located in the United │
│ │ │ States.For more information, see the Guide to the National Income and Product Accounts of the United │
│ │ │ States (NIPA) and the Bureau of Economic Analysis (http://www.bea.gov/national/pdf/nipaguid.pdf). │
╘═════════════════╧═════════════════════════════╧══════════════════════════════════════════════════════════════════════════════════════════════════════╛
```

58
website/content/economy/search/_index.md vendored
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@@ -1,58 +0,0 @@
```
usage: search [-s SERIES_TERM] [-n NUM] [-h]
```
Print series notes when searching for series. https://fred.stlouisfed.org
```
optional arguments:
-s SERIES_TERM, --series SERIES_TERM
Search for this series term. (default: None)
-n NUM, --num NUM Maximum number of series notes to display. (default: 5)
-h, --help show this help message (default: False)
```
search household
```
(✨) (economy)> search household
UNRATE
------
The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.
This rate is also defined as the U-3 measure of labor underutilization.
The series comes from the 'Current Population Survey (Household Survey)'
The source code is: LNS14000000
CIVPART
-------
The series comes from the 'Current Population Survey (Household Survey)'
The source code is: LNS11300000
MEHOINUSA672N
-------------
Household data are collected as of March.
As stated in the Census's Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011 (http://www.census.gov/hhes/www/p60_243sa.pdf).
Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.
We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.
Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, (https://www2.census.gov/prod2/popscan/p60-114.pdf) Money Income in 1976 of Families and Persons in the United States.
TDSP
----
The Household Debt Service Ratio (DSR) (https://fred.stlouisfed.org/series/TDSP) is the ratio of total required household debt payments to total disposable income.
The DSR is divided into two parts. The Mortgage DSR (MDSP) (https://fred.stlouisfed.org/series/MDSP) is total quarterly required mortgage payments divided by total quarterly disposable personal income. The Consumer DSR (CDSP) (https://fred.stlouisfed.org/series/CDSP) is total quarterly scheduled consumer debt payments divided by total quarterly disposable personal income. The Mortgage DSR and the Consumer DSR sum to the DSR.
For more information, please visit the Board of Governors (https://www.federalreserve.gov/releases/housedebt/about.htm).
LNS14000006
-----------
The series comes from the 'Current Population Survey (Household Survey)'
The source code is: LNS14000006
```

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website/content/economy/series/_index.md vendored
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@@ -1,18 +0,0 @@
```
usage: series [-i SERIES_ID] [-s START_DATE] [--raw] [--export {png,jpg,pdf,svg}] [-h]
```
Search for series numbers by term and display one or more series in a chart. https://fred.stlouisfed.org
```
optional arguments:
-i SERIES_ID, --id SERIES_ID
FRED Series from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3
-s START_DATE Starting date (YYYY-MM-DD) of data
--raw Only output raw data
--export {png,jpg,pdf,svg}
Export data to csv,json,xlsx or png,jpg,pdf,svg file
-h, --help show this help message
```
<img size="1400" alt="Feature Screenshot - series" src="https://user-images.githubusercontent.com/85772166/141927396-50828ef4-fb19-443c-93a1-ba7276979ade.png">
<img size="1400" alt="Feature Screenshot - series ID" src="https://user-images.githubusercontent.com/85772166/141927959-7199673f-d88a-48e2-9fea-f43733a8ac04.png">

15
website/data/menu/main.yml vendored
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@@ -873,12 +873,19 @@ main:
ref: "/economy/tyld"
- name: unemp
ref: "/economy/unemp"
- name: search
ref: "/economy/search"
- name: series
ref: "/economy/series"
- name: bigmac
ref: "/economy/bigmac"
- name: fred
ref: "/economy/fred"
sub:
- name: search
ref : "/economy/fred/search"
- name: add
ref: "/economy/fred/add"
- name: rmv
ref: "/economy/fred/rmv"
- name: plot
ref: "/economy/fred/plot"
- name: etf
ref: "/etf"
sub: