mirror of
https://github.com/OpenBB-finance/OpenBB.git
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* Add lookup commands * main ETF commands * Add technical analysis to ETF * add prediction techniques to ETF * add comparison analysis to etf * allow to select nr of holdings on load * add shold and summary commands to ETF * minor improvs * screener on ETF menu * add etf/scr wsj commands * readd compare command * add sbc to etf/screener * rename chold and shold * fix rogue etf and reset * minor corrections * minor menu print improv * legend of weights and title of plot * refactor screener to screener and disc on ETF * add ETF scripts
147 lines
4.3 KiB
Python
147 lines
4.3 KiB
Python
"""Finance Database view"""
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__docformat__ = "numpy"
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import os
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import pandas as pd
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from tabulate import tabulate
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.etf import financedatabase_model
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from gamestonk_terminal.helper_funcs import export_data
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def display_etf_by_name(
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name: str,
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limit: int,
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export: str = "",
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):
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"""Display a selection of ETFs based on name filtered by total assets. [Source: Finance Database]
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Parameters
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----------
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name: str
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Search by name to find ETFs matching the criteria.
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limit: int
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Limit of ETFs to display
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export: str
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Type of format to export data
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"""
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data = financedatabase_model.get_etfs_by_name(name)
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if not data:
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print("No data was found with that name\n")
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return
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tabulate_data = pd.DataFrame(data).T[
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["long_name", "family", "category", "total_assets"]
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]
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tabulate_data_sorted = tabulate_data.sort_values(by="total_assets", ascending=False)
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tabulate_data_sorted["total_assets"] = tabulate_data_sorted["total_assets"] / 1e6
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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tabulate_data_sorted.iloc[:limit],
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showindex=True,
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headers=["Name", "Family", "Category", "Total Assets [M]"],
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floatfmt=".2f",
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(tabulate_data_sorted.iloc[:limit].to_string(), "\n")
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export_data(export, os.path.dirname(os.path.abspath(__file__)), "ln_fd", data)
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def display_etf_by_description(
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description: str,
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limit: int,
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export: str = "",
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):
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"""Display a selection of ETFs based on description filtered by total assets. [Source: Finance Database]
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Parameters
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----------
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description: str
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Search by description to find ETFs matching the criteria.
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limit: int
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Limit of ETFs to display
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export: str
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Type of format to export data
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"""
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data = financedatabase_model.get_etfs_by_description(description)
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if not data:
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print("No data was found with that description\n")
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return
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tabulate_data = pd.DataFrame(data).T[
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["long_name", "family", "category", "total_assets"]
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]
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tabulate_data_sorted = tabulate_data.sort_values(by="total_assets", ascending=False)
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tabulate_data_sorted["total_assets"] = tabulate_data_sorted["total_assets"] / 1e6
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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tabulate_data_sorted.iloc[:limit],
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showindex=True,
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headers=["Name", "Family", "Category", "Total Assets [M]"],
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floatfmt=".2f",
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(tabulate_data_sorted.iloc[:limit].to_string(), "\n")
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export_data(export, os.path.dirname(os.path.abspath(__file__)), "ld", data)
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def display_etf_by_category(
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category: str,
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limit: int,
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export: str = "",
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):
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"""Display a selection of ETFs based on a category filtered by total assets. [Source: Finance Database]
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Parameters
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----------
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description: str
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Search by description to find ETFs matching the criteria.
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limit: int
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Limit of ETFs to display
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export: str
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Type of format to export data
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"""
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data = financedatabase_model.get_etfs_by_category(category)
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if not data:
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print("No data was found on that category\n")
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return
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tabulate_data = pd.DataFrame(data).T[
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["long_name", "family", "category", "total_assets"]
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]
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tabulate_data_sorted = tabulate_data.sort_values(by="total_assets", ascending=False)
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tabulate_data_sorted["total_assets"] = tabulate_data_sorted["total_assets"] / 1e6
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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tabulate_data_sorted.iloc[:limit],
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showindex=True,
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headers=["Name", "Family", "Category", "Total Assets [M]"],
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floatfmt=".2f",
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tablefmt="fancy_grid",
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),
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"\n",
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)
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else:
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print(tabulate_data_sorted.iloc[:limit].to_string(), "\n")
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export_data(
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export,
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os.path.join(os.path.dirname(os.path.abspath(__file__)), "screener"),
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"sbc",
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data,
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)
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