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https://github.com/OpenBB-finance/OpenBB.git
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1ca7fd9beb
* Began the fax * Bumped ruff * Fixed a BUNCH of ruff errors * Fixed a lot of ruff errors * Simplify if statements * Fixed black * Updated some stuff * Updated some stuff * A lot more ruff fixes * Added a fix * Added a fix * Fixed mypy * Fixed some stuff * Reverted one thing * Small fix * Small fix * Small fix * Added fix * Added fix * Update requirements
411 lines
11 KiB
Python
411 lines
11 KiB
Python
"""Yahoo Finance Model"""
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__docformat__ = "numpy"
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import logging
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import ssl
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from datetime import datetime
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from typing import Optional, Tuple
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from urllib.request import Request, urlopen
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import numpy as np
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import pandas as pd
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import yfinance as yf
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from bs4 import BeautifulSoup
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import lambda_long_number_format
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from openbb_terminal.rich_config import console
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from openbb_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index
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logger = logging.getLogger(__name__)
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# pylint: disable=W0212
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ssl._create_default_https_context = ssl._create_unverified_context
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@log_start_end(log=logger)
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def get_info(symbol: str) -> pd.DataFrame:
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"""Gets ticker symbol info
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Parameters
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----------
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symbol: str
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Stock ticker symbol
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Returns
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-------
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pd.DataFrame
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DataFrame of yfinance information
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"""
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stock = yf.Ticker(symbol)
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df_info = pd.DataFrame(stock.fast_info.items(), columns=["Metric", "Value"])
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df_info = df_info.set_index("Metric")
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clean_df_index(df_info)
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if "Last split date" in df_info.index and df_info.loc["Last split date"].values[0]:
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df_info.loc["Last split date"].values[0] = datetime.fromtimestamp(
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df_info.loc["Last split date"].values[0]
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).strftime("%Y-%m-%d")
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df_info = df_info.mask(df_info["Value"].astype(str).eq("[]")).dropna()
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df_info[df_info.index != "Zip"] = df_info[df_info.index != "Zip"].applymap(
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lambda x: lambda_long_number_format(x)
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)
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df_info = df_info.rename(
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index={
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"Address1": "Address",
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"Average daily volume10 day": "Average daily volume 10 day",
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"Average volume10days": "Average volume 10 days",
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"Price to sales trailing12 months": "Price to sales trailing 12 months",
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}
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)
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df_info.index = df_info.index.str.replace("eps", "EPS")
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df_info.index = df_info.index.str.replace("p e", "PE")
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df_info.index = df_info.index.str.replace("Peg", "PEG")
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return df_info
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@log_start_end(log=logger)
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def get_shareholders(symbol: str, holder: str = "institutional") -> pd.DataFrame:
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"""Get shareholders from yahoo
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Parameters
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----------
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symbol : str
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Stock ticker symbol
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holder : str
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Which holder to get table for
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Returns
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-------
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pd.DataFrame
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Major holders
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"""
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stock = yf.Ticker(symbol)
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# Major holders
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df_major_holders = stock.major_holders
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df_major_holders[1] = df_major_holders[1].apply(
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lambda x: x.replace("%", "Percentage")
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)
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# Institutional holders
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df_institutional_shareholders = stock.institutional_holders
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df_institutional_shareholders.columns = (
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df_institutional_shareholders.columns.str.replace("% Out", "Stake")
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)
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df_institutional_shareholders["Shares"] = df_institutional_shareholders[
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"Shares"
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].apply(lambda x: lambda_long_number_format(x))
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df_institutional_shareholders["Value"] = df_institutional_shareholders[
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"Value"
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].apply(lambda x: lambda_long_number_format(x))
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df_institutional_shareholders["Stake"] = df_institutional_shareholders[
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"Stake"
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].apply(lambda x: str(f"{100 * x:.2f}") + " %")
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# Mutualfunds holders
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df_mutualfund_shareholders = stock.mutualfund_holders
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df_mutualfund_shareholders.columns = df_mutualfund_shareholders.columns.str.replace(
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"% Out", "Stake"
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)
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df_mutualfund_shareholders["Shares"] = df_mutualfund_shareholders["Shares"].apply(
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lambda x: lambda_long_number_format(x)
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)
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df_mutualfund_shareholders["Value"] = df_mutualfund_shareholders["Value"].apply(
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lambda x: lambda_long_number_format(x)
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)
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df_mutualfund_shareholders["Stake"] = df_mutualfund_shareholders["Stake"].apply(
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lambda x: str(f"{100 * x:.2f}") + " %"
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)
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if holder == "major":
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return df_major_holders
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if holder == "institutional":
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return df_institutional_shareholders
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if holder == "mutualfund":
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return df_mutualfund_shareholders
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return pd.DataFrame()
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@log_start_end(log=logger)
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def get_calendar_earnings(symbol: str) -> pd.DataFrame:
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"""Get calendar earnings for ticker symbol
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Parameters
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----------
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symbol: str
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Stock ticker symbol
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Returns
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-------
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pd.DataFrame
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Dataframe of calendar earnings
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"""
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stock = yf.Ticker(symbol)
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df_calendar = stock.calendar
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if df_calendar.empty:
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return pd.DataFrame()
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df_calendar.iloc[0, :] = df_calendar.iloc[0, :].apply(
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lambda x: x.date().strftime("%m/%d/%Y")
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)
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df_calendar.iloc[1:, :] = df_calendar.iloc[1:, :].applymap(
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lambda x: lambda_long_number_format(x)
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)
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return df_calendar.T
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@log_start_end(log=logger)
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def get_dividends(symbol: str) -> pd.DataFrame:
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"""Get historical dividend for ticker
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Parameters
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----------
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symbol: str
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Ticker symbol to get dividend for
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Returns
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-------
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pd.DataFrame
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Dataframe of dividends and dates
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Examples
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--------
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>>> from openbb_terminal.sdk import openbb
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>>> openbb.fa.divs("AAPL")
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"""
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df = pd.DataFrame(yf.Ticker(symbol).dividends)
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if df.empty:
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console.print("No dividends found.\n")
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return pd.DataFrame()
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df["Change"] = df.diff()
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df = df[::-1]
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return df
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@log_start_end(log=logger)
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def get_mktcap(
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symbol: str,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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) -> Tuple[pd.DataFrame, str]:
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"""Get market cap over time for ticker. [Source: Yahoo Finance]
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Parameters
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----------
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symbol: str
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Ticker to get market cap over time
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start_date: Optional[str]
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Initial date (e.g., 2021-10-01). Defaults to 3 years back
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Returns
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-------
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pd.DataFrame
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Dataframe of estimated market cap over time
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str:
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Currency of ticker
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"""
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if start_date is None:
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# Set data far in the past to ensure all data is returned
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start_date = "1900-01-01"
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if end_date is None:
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end_date = datetime.now() # type: ignore
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currency = ""
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df_data = yf.download(
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symbol,
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start=start_date,
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end=end_date,
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progress=False,
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threads=False,
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ignore_tz=True,
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)
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if not df_data.empty:
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data = yf.Ticker(symbol).fast_info
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if data:
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df_data["Adj Close"] = df_data["Adj Close"] * data["shares"]
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df_data = df_data["Adj Close"]
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currency = data["currency"] if data["currency"] else ""
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return df_data, currency
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@log_start_end(log=logger)
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def get_splits(symbol: str) -> pd.DataFrame:
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"""Get splits and reverse splits events. [Source: Yahoo Finance]
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Parameters
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----------
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symbol: str
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Ticker to get forward and reverse splits
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Returns
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-------
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pd.DataFrame
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Dataframe of forward and reverse splits
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"""
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data = yf.Ticker(symbol).splits
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if not data.empty:
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return data.to_frame()
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return pd.DataFrame()
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@log_start_end(log=logger)
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def get_financials(symbol: str, statement: str, ratios: bool = False) -> pd.DataFrame:
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"""Get cashflow statement for company
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Parameters
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----------
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symbol : str
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Stock ticker symbol
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statement: str
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can be:
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- cash-flow
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- financials for Income
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- balance-sheet
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ratios: bool
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Shows percentage change
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Returns
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-------
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pd.DataFrame
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Dataframe of Financial statement
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"""
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url = (
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"https://uk.finance.yahoo.com/quote/"
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+ symbol
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+ "/"
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+ statement
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+ "?p="
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+ symbol
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)
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# Making the website believe that you are accessing it using a Mozilla browser
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req = Request(url, headers={"User-Agent": "Mozilla/5.0"})
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webpage = urlopen(req).read() # pylint: disable= R1732 # noqa: S310
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soup = BeautifulSoup(webpage, "html.parser")
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features = soup.find_all("div", class_="D(tbr)")
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headers = []
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temp_list = []
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final = []
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if len(features) == 0:
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return console.print("No data found in Yahoo Finance\n")
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index = 0 # create headers
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for item in features[0].find_all("div", class_="D(ib)"):
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headers.append(item.text) # statement contents
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while index <= len(features) - 1:
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# filter for each line of the statement
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temp = features[index].find_all("div", class_="D(tbc)")
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for line in temp:
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# each item adding to a temporary list
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temp_list.append(line.text)
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# temp_list added to final list
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final.append(temp_list)
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# clear temp_list
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temp_list = []
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index += 1
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df = pd.DataFrame(final[1:])
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if df.empty:
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return pd.DataFrame()
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new_headers = []
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if statement == "balance-sheet":
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for dates in headers[1:]:
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read = datetime.strptime(dates, "%d/%m/%Y")
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write = read.strftime("%Y-%m-%d")
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new_headers.append(write)
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new_headers[:0] = ["Breakdown"]
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df.columns = new_headers
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df.set_index("Breakdown", inplace=True)
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elif statement in ("financials", "cash-flow"):
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for dates in headers[2:]:
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read = datetime.strptime(dates, "%d/%m/%Y")
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write = read.strftime("%Y-%m-%d")
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new_headers.append(write)
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new_headers[:0] = ["Breakdown", "ttm"]
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df.columns = new_headers
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df.set_index("Breakdown", inplace=True)
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df.replace("", np.nan, inplace=True)
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df.replace("-", np.nan, inplace=True)
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df = df.dropna(how="all")
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df = df.replace(",", "", regex=True)
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df = df.replace("k", "", regex=True)
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df = df.astype("float")
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df.index = df.index.str.replace(",", "")
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not_skipped = ~df.reset_index()["Breakdown"].str.contains("EPS", case=False)
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skipped = df.reset_index()["Breakdown"].str.contains("EPS", case=False)
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skipped_df = df.reset_index()[skipped].set_index("Breakdown")
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transformed = df.reset_index()[not_skipped].set_index("Breakdown") * 1000
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df = pd.concat([transformed, skipped_df])
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if ratios:
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types = df.copy().applymap(lambda x: isinstance(x, (float, int)))
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types = types.all(axis=1)
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# For rows with complete data
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valid = []
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i = 0
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for row in types:
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if row:
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valid.append(i)
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i += 1
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df_fa_pc = df.iloc[valid].pct_change(axis="columns", periods=-1).fillna(0)
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j = 0
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for i in valid:
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df.iloc[i] = df_fa_pc.iloc[j]
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j += 1
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return df
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@log_start_end(log=logger)
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def get_earnings_history(symbol: str) -> pd.DataFrame:
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"""Get earning reports
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Parameters
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----------
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symbol: str
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Symbol to get earnings for
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Returns
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-------
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pd.DataFrame
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Dataframe of historical earnings if present
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"""
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df = yf.Ticker(symbol).earnings_dates
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df.reset_index(inplace=True)
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df["Earnings Date"] = df["Earnings Date"].dt.strftime("%Y-%m-%d")
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df.drop_duplicates(inplace=True)
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df = df.fillna("-")
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return df
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@log_start_end(log=logger)
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def get_currency(symbol) -> str:
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"""Quick helper to get currency for financial statements"""
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try:
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ticker_info = yf.Ticker(symbol).info
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except Exception:
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return "Not Specified"
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if "financialCurrency" in ticker_info:
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return ticker_info["financialCurrency"]
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return "Not Specified"
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