Update app.py

app.py

@@ -6,51 +6,43 @@ import plotly.express as px
 import scipy.optimize as sco
 from datetime import datetime, timedelta
 import random
-import requests
+import yfinance as yf
 import time
 
 def fetch_stock_data(tickers):
-    """Fetch real stock data using Alpha Vantage API"""
-    API_KEY = "Y86RZ52NQ8YVX7F6"  # Should be in environment variable in production
-    BASE_URL = "https://www.alphavantage.co/query"
-    all_data = {}
-    
-    for ticker in tickers:
-        try:
-            # Add delay to respect rate limits (5 API calls per minute for free tier)
-            time.sleep(12)  # 12 second delay between calls
-            
-            params = {
-                "function": "TIME_SERIES_DAILY_ADJUSTED",
-                "symbol": ticker,
-                "outputsize": "full",
-                "apikey": API_KEY
-            }
+    """Fetch real stock data using yfinance with better error handling"""
+    try:
+        # Download data for all tickers at once
+        data = yf.download(
+            tickers,
+            start=(datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d'),
+            end=datetime.now().strftime('%Y-%m-%d'),
+            group_by='ticker',
+            auto_adjust=True
+        )
+        
+        # If only one ticker is passed, the data format is different
+        if len(tickers) == 1:
+            if data.empty:
+                print(f"No data received for {tickers[0]}")
+                return generate_sample_data(tickers)
+            return pd.DataFrame(data['Close'])
             
-            response = requests.get(BASE_URL, params=params)
-            data = response.json()
+        # For multiple tickers, extract just the Close prices
+        close_prices = pd.DataFrame()
+        for ticker in tickers:
+            if (ticker, 'Close') in data.columns:
+                close_prices[ticker] = data[ticker]['Close']
+        
+        if close_prices.empty:
+            print("No data received for any ticker")
+            return generate_sample_data(tickers)
             
-            if "Time Series (Daily)" in data:
-                # Convert the time series data to DataFrame
-                df = pd.DataFrame.from_dict(data["Time Series (Daily)"], orient="index")
-                df = df.astype(float)
-                all_data[ticker] = df["4. close"].iloc[:252]  # Get last year of data
-                print(f"Successfully fetched data for {ticker}")
-            else:
-                print(f"No data found for {ticker}")
-                
-        except Exception as e:
-            print(f"Error fetching {ticker}: {str(e)}")
-            continue
-    
-    if not all_data:
-        print("No data received, using backup data")
-        return generate_sample_data(tickers)
+        return close_prices
         
-    # Combine all data and align dates
-    df = pd.DataFrame(all_data)
-    df = df.sort_index(ascending=True)
-    return df
+    except Exception as e:
+        print(f"Error fetching data: {str(e)}")
+        return generate_sample_data(tickers)
 
 def generate_sample_data(tickers):
     """Generate sample data as backup"""
@@ -66,13 +58,18 @@ def generate_sample_data(tickers):
         
     return pd.DataFrame(data, index=dates)
 
-# Updated S&P 500 Stock List (reduced number due to API rate limits)
+# Predefined S&P 500 Stock List (Sample tickers)
 SP500_TICKERS = [
     'AAPL',  # Apple
     'MSFT',  # Microsoft
-    'GOOGL', # Google
+    'GOOG',  # Google
     'AMZN',  # Amazon
-    'TSLA'   # Tesla
+    'TSLA',  # Tesla
+    'NVDA',  # NVIDIA
+    'META',  # Meta
+    'BRK-B', # Berkshire Hathaway
+    'JPM',   # JPMorgan Chase
+    'V'      # Visa
 ]
 
 def calculate_portfolio_metrics(weights, returns):
@@ -106,8 +103,8 @@ def simulate_investment(weights, mu, years, initial_investment=10000):
 
 def output_results(risk_level):
     try:
-        # Select random tickers (reduced number due to API rate limits)
-        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 3))
+        # Select random tickers
+        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 5))
         
         # Fetch real stock data
         stocks_df = fetch_stock_data(selected_tickers)
@@ -226,4 +223,3 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
     )
 
 if __name__ == "__main__":
-    app.launch()