mirror of
https://github.com/OpenBB-finance/OpenBB.git
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141dd68e68
* Removed prints * FIxed typing * FIxed typing * Fixed typing * Fixed typing * Fixed typing * Fixed typing * Fixed typing * Fix * Reverted stuff
250 lines
7.8 KiB
Python
250 lines
7.8 KiB
Python
""" Finviz Model """
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__docformat__ = "numpy"
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import logging
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import webbrowser
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from ast import literal_eval
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from typing import List
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import pandas as pd
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from finvizfinance.group import performance, spectrum, valuation
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from openbb_terminal.core.session.current_user import get_current_user
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import get_user_agent, request
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from openbb_terminal.rich_config import console
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# pylint: disable=unsupported-assignment-operation
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logger = logging.getLogger(__name__)
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# pylint: disable=unsupported-assignment-operation
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GROUPS = {
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"sector": "Sector",
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"industry": "Industry",
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"basic_materials": "Industry (Basic Materials)",
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"communication_services": "Industry (Communication Services)",
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"consumer_cyclical": "Industry (Consumer Cyclical)",
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"consumer_defensive": "Industry (Consumer Defensive)",
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"energy": "Industry (Energy)",
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"financial": "Industry (Financial)",
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"healthcare": "Industry (Healthcare)",
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"industrials": "Industry (Industrials)",
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"real_Estate": "Industry (Real Estate)",
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"technology": "Industry (Technology)",
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"utilities": "Industry (Utilities)",
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"country": "Country (U.S. listed stocks only)",
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"capitalization": "Capitalization",
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}
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@log_start_end(log=logger)
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def get_performance_map(period: str = "1d", map_filter: str = "sp500"):
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"""Opens Finviz map website in a browser. [Source: Finviz]
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Parameters
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----------
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period : str
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Performance period. Available periods are 1d, 1w, 1m, 3m, 6m, 1y.
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scope : str
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Map filter. Available map filters are sp500, world, full, etf.
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"""
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# Conversion from period and type, to fit url requirements
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d_period = {"1d": "", "1w": "w1", "1m": "w4", "3m": "w13", "6m": "w26", "1y": "w52"}
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d_type = {"sp500": "sec", "world": "geo", "full": "sec_all", "etf": "etf"}
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url = f"https://finviz.com/map.ashx?t={d_type[map_filter]}&st={d_period[period]}"
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webbrowser.open(url)
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@log_start_end(log=logger)
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def get_groups() -> List[str]:
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"""Get group available"""
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return list(GROUPS.keys())
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@log_start_end(log=logger)
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def get_valuation_data(
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group: str = "sector", sortby: str = "Name", ascend: bool = True
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) -> pd.DataFrame:
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"""Get group (sectors, industry or country) valuation data. [Source: Finviz]
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Parameters
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----------
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group : str
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Group by category. Available groups can be accessed through get_groups().
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sortby : str
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Column to sort by
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ascend : bool
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Flag to sort in ascending order
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Returns
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-------
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pd.DataFrame
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dataframe with valuation/performance data
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"""
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if group not in GROUPS:
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console.print(
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f"[red]Group {group} not found. Check available groups through get_groups().[/red]\n"
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)
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return pd.DataFrame()
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try:
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group = GROUPS[group]
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df_group = valuation.Valuation().screener_view(group=group)
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df_group.columns = [col.strip() for col in df_group.columns]
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df_group = df_group.sort_values(by=sortby, ascending=ascend)
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df_group.fillna("", inplace=True)
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# Passing Raw data to Pandas DataFrame if using interactive mode
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if get_current_user().preferences.USE_INTERACTIVE_DF:
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return df_group
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df_group["Market Cap"] = df_group["Market Cap"].apply(
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lambda x: float(x.strip("B"))
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if x.endswith("B")
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else float(x.strip("M")) / 1000
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)
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df_group["Volume"] = df_group["Volume"] / 1_000_000
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df_group = df_group.rename(columns={"Volume": "Volume [1M]"})
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return df_group
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except IndexError:
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console.print("Data not found.\n")
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return pd.DataFrame()
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@log_start_end(log=logger)
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def get_performance_data(
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group: str = "sector", sortby: str = "Name", ascend: bool = True
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) -> pd.DataFrame:
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"""Get group (sectors, industry or country) performance data. [Source: Finviz]
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Parameters
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----------
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group : str
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Group by category. Available groups can be accessed through get_groups().
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sortby : str
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Column to sort by
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ascend : bool
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Flag to sort in ascending order
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Returns
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-------
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pd.DataFrame
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dataframe with performance data
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"""
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if group not in GROUPS:
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console.print(
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f"[red]Group {group} not found. Check available groups through get_groups().[/red]\n"
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)
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return pd.DataFrame()
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try:
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group = GROUPS[group]
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df_group = performance.Performance().screener_view(group=group)
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df_group = df_group.rename(
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columns={
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"Perf Week": "1W",
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"Perf Month": "1M",
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"Perf Quart": "3M",
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"Perf Half": "6M",
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"Perf Year": "1Y",
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"Perf YTD": "YTD",
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"Avg Volume": "Avg Volume",
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}
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)
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df_group.columns = [col.strip() for col in df_group.columns]
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df_group["1W"] = df_group["1W"].apply(lambda x: float(x.strip("%")) / 100)
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df_group = df_group.sort_values(by=sortby, ascending=ascend)
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df_group.fillna("", inplace=True)
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# Passing Raw data to Pandas DataFrame if using interactive mode
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if get_current_user().preferences.USE_INTERACTIVE_DF:
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return df_group
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df_group["Volume"] = df_group["Volume"] / 1_000_000
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df_group["Avg Volume"] = df_group["Avg Volume"] / 1_000_000
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df_group = df_group.rename(
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columns={"Volume": "Volume [1M]", "Avg Volume": "Avg Volume [1M]"}
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)
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return df_group
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except IndexError:
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console.print("Data not found.\n")
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return pd.DataFrame()
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@log_start_end(log=logger)
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def get_spectrum_data(group: str = "sector"):
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"""Get group (sectors, industry or country) valuation/performance data. [Source: Finviz]
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Parameters
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----------
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group : str
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Group by category. Available groups can be accessed through get_groups().
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"""
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if group not in GROUPS:
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console.print(
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f"[red]Group {group} not found. Check available groups through get_groups().[/red]\n"
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)
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return
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group = GROUPS[group]
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spectrum.Spectrum().screener_view(group=group)
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@log_start_end(log=logger)
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def get_futures(
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future_type: str = "Indices", sortby: str = "ticker", ascend: bool = False
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) -> pd.DataFrame:
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"""Get futures data. [Source: Finviz]
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Parameters
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----------
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future_type : str
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From the following: Indices, Energy, Metals, Meats, Grains, Softs, Bonds, Currencies
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sortby : str
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Column to sort by
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ascend : bool
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Flag to sort in ascending order
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Returns
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-------
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pd.Dataframe
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Indices, Energy, Metals, Meats, Grains, Softs, Bonds, Currencies
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"""
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source = request(
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"https://finviz.com/futures.ashx", headers={"User-Agent": get_user_agent()}
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).text
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slice_source = source[
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source.find("var groups = ") : source.find( # noqa: E203
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"\r\n\r\n groups.forEach(function(group) "
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)
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]
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groups = literal_eval(
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slice_source[
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: slice_source.find("\r\n var tiles = ") - 1
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].strip("var groups = ")
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)
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titles = literal_eval(
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slice_source[
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slice_source.find("\r\n var tiles = ") : -1 # noqa: E203
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].strip("\r\n var tiles = ")
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)
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d_futures: dict = {}
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for future in groups:
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d_futures[future["label"]] = []
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for ticker in future["contracts"]:
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d_futures[future["label"]].append(titles[ticker["ticker"]])
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df = pd.DataFrame(d_futures[future_type])
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df = df.set_index("label")
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df = df.sort_values(by=sortby, ascending=ascend)
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df = df[["prevClose", "last", "change"]].fillna("")
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return df
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