120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
# gets data/stats
|
|
|
|
from collections.abc import Callable
|
|
from functools import wraps
|
|
from typing import Annotated, Any
|
|
|
|
import pandas as pd
|
|
import yfinance as yf
|
|
from pandas import DataFrame
|
|
|
|
from .utils import SavePathType, decorate_all_methods
|
|
|
|
|
|
def init_ticker(func: Callable) -> Callable:
|
|
"""Decorator to initialize yf.Ticker and pass it to the function."""
|
|
|
|
@wraps(func)
|
|
def wrapper(symbol: Annotated[str, "ticker symbol"], *args, **kwargs) -> Any:
|
|
ticker = yf.Ticker(symbol)
|
|
return func(ticker, *args, **kwargs)
|
|
|
|
return wrapper
|
|
|
|
|
|
@decorate_all_methods(init_ticker)
|
|
class YFinanceUtils:
|
|
|
|
def get_stock_data(
|
|
symbol: Annotated[str, "ticker symbol"],
|
|
start_date: Annotated[
|
|
str, "start date for retrieving stock price data, YYYY-mm-dd"
|
|
],
|
|
end_date: Annotated[
|
|
str, "end date for retrieving stock price data, YYYY-mm-dd"
|
|
],
|
|
save_path: SavePathType = None,
|
|
) -> DataFrame:
|
|
"""retrieve stock price data for designated ticker symbol"""
|
|
ticker = symbol
|
|
# add one day to the end_date so that the data range is inclusive
|
|
end_date = pd.to_datetime(end_date) + pd.DateOffset(days=1)
|
|
end_date = end_date.strftime("%Y-%m-%d")
|
|
stock_data = ticker.history(start=start_date, end=end_date)
|
|
# save_output(stock_data, f"Stock data for {ticker.ticker}", save_path)
|
|
return stock_data
|
|
|
|
def get_stock_info(
|
|
symbol: Annotated[str, "ticker symbol"],
|
|
) -> dict:
|
|
"""Fetches and returns latest stock information."""
|
|
ticker = symbol
|
|
stock_info = ticker.info
|
|
return stock_info
|
|
|
|
def get_company_info(
|
|
symbol: Annotated[str, "ticker symbol"],
|
|
save_path: str | None = None,
|
|
) -> DataFrame:
|
|
"""Fetches and returns company information as a DataFrame."""
|
|
ticker = symbol
|
|
info = ticker.info
|
|
company_info = {
|
|
"Company Name": info.get("shortName", "N/A"),
|
|
"Industry": info.get("industry", "N/A"),
|
|
"Sector": info.get("sector", "N/A"),
|
|
"Country": info.get("country", "N/A"),
|
|
"Website": info.get("website", "N/A"),
|
|
}
|
|
company_info_df = DataFrame([company_info])
|
|
if save_path:
|
|
company_info_df.to_csv(save_path)
|
|
print(f"Company info for {ticker.ticker} saved to {save_path}")
|
|
return company_info_df
|
|
|
|
def get_stock_dividends(
|
|
symbol: Annotated[str, "ticker symbol"],
|
|
save_path: str | None = None,
|
|
) -> DataFrame:
|
|
"""Fetches and returns the latest dividends data as a DataFrame."""
|
|
ticker = symbol
|
|
dividends = ticker.dividends
|
|
if save_path:
|
|
dividends.to_csv(save_path)
|
|
print(f"Dividends for {ticker.ticker} saved to {save_path}")
|
|
return dividends
|
|
|
|
def get_income_stmt(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
|
|
"""Fetches and returns the latest income statement of the company as a DataFrame."""
|
|
ticker = symbol
|
|
income_stmt = ticker.financials
|
|
return income_stmt
|
|
|
|
def get_balance_sheet(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
|
|
"""Fetches and returns the latest balance sheet of the company as a DataFrame."""
|
|
ticker = symbol
|
|
balance_sheet = ticker.balance_sheet
|
|
return balance_sheet
|
|
|
|
def get_cash_flow(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
|
|
"""Fetches and returns the latest cash flow statement of the company as a DataFrame."""
|
|
ticker = symbol
|
|
cash_flow = ticker.cashflow
|
|
return cash_flow
|
|
|
|
def get_analyst_recommendations(symbol: Annotated[str, "ticker symbol"]) -> tuple:
|
|
"""Fetches the latest analyst recommendations and returns the most common recommendation and its count."""
|
|
ticker = symbol
|
|
recommendations = ticker.recommendations
|
|
if recommendations.empty:
|
|
return None, 0 # No recommendations available
|
|
|
|
# Assuming 'period' column exists and needs to be excluded
|
|
row_0 = recommendations.iloc[0, 1:] # Exclude 'period' column if necessary
|
|
|
|
# Find the maximum voting result
|
|
max_votes = row_0.max()
|
|
majority_voting_result = row_0[row_0 == max_votes].index.tolist()
|
|
|
|
return majority_voting_result[0], max_votes
|