Update app.py

app.py

@@ -6,43 +6,58 @@ import plotly.express as px
 import scipy.optimize as sco
 from datetime import datetime, timedelta
 import random
-import yfinance as yf
+import requests
 import time
 
 def fetch_stock_data(tickers):
-    """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'])
+    """Fetch real stock data using Alpha Vantage API"""
+    API_KEY = "Y86RZ52NQ8YVX7F6"
+    BASE_URL = "https://www.alphavantage.co/query"
+    all_data = {}
+    
+    for ticker in tickers:
+        try:
+            # Use TIME_SERIES_DAILY for daily data
+            params = {
+                "function": "TIME_SERIES_DAILY",
+                "symbol": ticker,
+                "apikey": API_KEY,
+                "outputsize": "full"
+            }
             
-        # 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)
+            print(f"Fetching data for {ticker}...")
+            response = requests.get(BASE_URL, params=params)
+            response.raise_for_status()
+            data = response.json()
             
-        return close_prices
-        
-    except Exception as e:
-        print(f"Error fetching data: {str(e)}")
+            if "Time Series (Daily)" in data:
+                daily_data = data["Time Series (Daily)"]
+                # Convert to DataFrame
+                df = pd.DataFrame.from_dict(daily_data, orient='index')
+                df = df.astype(float)
+                # Use adjusted close price
+                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}")
+                if "Note" in data:
+                    print("API Message:", data["Note"])
+            
+            # Add delay between requests (Alpha Vantage has a rate limit)
+            time.sleep(12)  # 12 second delay between requests
+                
+        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)
+    
+    # Combine all data and align dates
+    df = pd.DataFrame(all_data)
+    df = df.sort_index(ascending=True)
+    return df
 
 def generate_sample_data(tickers):
     """Generate sample data as backup"""
@@ -58,18 +73,13 @@ def generate_sample_data(tickers):
         
     return pd.DataFrame(data, index=dates)
 
-# Predefined S&P 500 Stock List (Sample tickers)
+# Updated S&P 500 Stock List (reduced number due to API rate limits)
 SP500_TICKERS = [
     'AAPL',  # Apple
     'MSFT',  # Microsoft
-    'GOOG',  # Google
+    'GOOGL', # Google
     'AMZN',  # Amazon
-    'TSLA',  # Tesla
-    'NVDA',  # NVIDIA
-    'META',  # Meta
-    'BRK-B', # Berkshire Hathaway
-    'JPM',   # JPMorgan Chase
-    'V'      # Visa
+    'TSLA'   # Tesla
 ]
 
 def calculate_portfolio_metrics(weights, returns):
@@ -103,8 +113,8 @@ def simulate_investment(weights, mu, years, initial_investment=10000):
 
 def output_results(risk_level):
     try:
-        # Select random tickers
-        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 5))
+        # Select random tickers (reduced number due to API rate limits)
+        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 3))
         
         # Fetch real stock data
         stocks_df = fetch_stock_data(selected_tickers)
@@ -223,3 +233,4 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
     )
 
 if __name__ == "__main__":
+    app.launch()