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import yfinance as yf | |
import pandas as pd | |
from pytickersymbols import PyTickerSymbols | |
from config import ticker_dict | |
def get_stocks_from_index(idx): | |
stock_data = PyTickerSymbols() | |
index = ticker_dict[idx] | |
stocks = list(stock_data.get_stocks_by_index(index)) | |
stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks] | |
return stock_names | |
def get_stock_data(ticker_name, interval, start_date, end_date): | |
series = yf.download(tickers=ticker_name, start=start_date, end=end_date, interval=interval) | |
return series.reset_index() | |
def get_company_info(ticker): | |
stock = yf.Ticker(ticker) | |
info = stock.info | |
fundamentals = { | |
"Company Name": info.get("longName", "N/A"), | |
"Sector": info.get("sector", "N/A"), | |
"Industry": info.get("industry", "N/A"), | |
"Market Cap": f"${info.get('marketCap', 'N/A'):,}", | |
"P/E Ratio": round(info.get("trailingPE", 0), 2), | |
"EPS": round(info.get("trailingEps", 0), 2), | |
"52 Week High": f"${info.get('fiftyTwoWeekHigh', 'N/A'):,}", | |
"52 Week Low": f"${info.get('fiftyTwoWeekLow', 'N/A'):,}", | |
"Dividend Yield": f"{info.get('dividendYield', 0) * 100:.2f}%", | |
"Beta": round(info.get("beta", 0), 2), | |
} | |
return pd.DataFrame(list(fundamentals.items()), columns=['Metric', 'Value']) |