Kr08/Stock_Forecasting

1import yfinance as yf2import pandas as pd3from pytickersymbols import PyTickerSymbols4from config import ticker_dict5 6def get_stocks_from_index(idx):7    stock_data = PyTickerSymbols()8    index = ticker_dict[idx]9    stocks = list(stock_data.get_stocks_by_index(index))10    stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks]11    return stock_names12 13def get_stock_data(ticker_name, interval, start_date, end_date):14    series = yf.download(tickers=ticker_name, start=start_date, end=end_date, interval=interval)15    return series.reset_index()16 17def get_company_info(ticker):18    stock = yf.Ticker(ticker)19    info = stock.info20    21    fundamentals = {22        "Company Name": info.get("longName", "N/A"),23        "Sector": info.get("sector", "N/A"),24        "Industry": info.get("industry", "N/A"),25        "Market Cap": f"${info.get('marketCap', 'N/A'):,}",26        "P/E Ratio": round(info.get("trailingPE", 0), 2),27        "EPS": round(info.get("trailingEps", 0), 2),28        "52 Week High": f"${info.get('fiftyTwoWeekHigh', 'N/A'):,}",29        "52 Week Low": f"${info.get('fiftyTwoWeekLow', 'N/A'):,}",30        "Dividend Yield": f"{info.get('dividendYield', 0) * 100:.2f}%",31        "Beta": round(info.get("beta", 0), 2),32    }33    34    return pd.DataFrame(list(fundamentals.items()), columns=['Metric', 'Value'])