Create data_fetcher.py

data_fetcher.py

@@ -0,0 +1,34 @@
+import yfinance as yf
+import pandas as pd
+from pytickersymbols import PyTickerSymbols
+from config import ticker_dict, START_DATE, END_DATE
+
+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):
+    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'])