Update app.py

app.py

@@ -1,81 +1,34 @@
+# app.py
 import gradio as gr
 import pandas as pd
 import numpy as np
 import plotly.express as px
 import scipy.optimize as sco
 from datetime import datetime, timedelta
-import requests
 import random
-import time
+import yfinance as yf
 
 def fetch_stock_data(tickers):
-    """Fetch real stock data from Yahoo Finance API with rate limiting"""
-    all_data = {}
-    
-    url = "https://yahoo-finance166.p.rapidapi.com/stock/v2/get-summary"
-    
-    headers = {
-        "X-RapidAPI-Key": "e4d2d5bccdmsh3ad7175fdbb435bp13c65cjsn33c57",
-        "X-RapidAPI-Host": "yahoo-finance166.p.rapidapi.com"
-    }
-    
-    for ticker in tickers:
-        try:
-            # Add delay between requests to avoid rate limiting
-            time.sleep(1)
-            
-            # Clean up ticker symbol
-            clean_ticker = ticker.replace('-', '').strip()
-            
-            querystring = {"symbol": clean_ticker}
-            
-            response = requests.get(url, headers=headers, params=querystring)
-            response.raise_for_status()
-            data = response.json()
-            
-            # Extract price data
-            if "price" in data and "regularMarketPrice" in data["price"]:
-                price = data["price"]["regularMarketPrice"]["raw"]
-                all_data[ticker] = [price]
-                print(f"Successfully fetched data for {ticker}")
-            else:
-                print(f"No price data found for {ticker}")
-                
-        except requests.exceptions.RequestException as e:
-            print(f"Error fetching data for {ticker}: {str(e)}")
-            continue
-        except Exception as e:
-            print(f"Unexpected error for {ticker}: {str(e)}")
-            continue
-    
-    if not all_data:
-        raise ValueError("No data could be fetched for any ticker")
+    """Fetch real stock data using yfinance"""
+    try:
+        # Download 1 year of 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'),
+            progress=False
+        )
         
-    # Create DataFrame with single row of current prices
-    df = pd.DataFrame(all_data)
-    
-    # Generate synthetic historical data based on current prices
-    periods = 252  # One year of trading days
-    historical_data = {}
-    
-    for ticker in df.columns:
-        current_price = df[ticker].iloc[0]
-        # Generate random walk backwards from current price
-        np.random.seed(42)  # For reproducibility
-        daily_returns = np.random.normal(loc=0.0001, scale=0.02, size=periods)
-        prices = np.zeros(periods)
-        prices[-1] = current_price
-        
-        for i in range(periods-2, -1, -1):
-            prices[i] = prices[i+1] / (1 + daily_returns[i])
-            
-        historical_data[ticker] = prices
-    
-    # Create DataFrame with dates
-    dates = pd.date_range(end=datetime.now(), periods=periods)
-    historical_df = pd.DataFrame(historical_data, index=dates)
-    
-    return historical_df
+        # If only one ticker, the format is different
+        if len(tickers) == 1:
+            return pd.DataFrame(data['Adj Close'])
+        
+        # Get just the adjusted close prices
+        return data['Adj Close']
+        
+    except Exception as e:
+        print(f"Error fetching data: {str(e)}")
+        raise ValueError(f"Failed to fetch stock data: {str(e)}")
 
 # Predefined S&P 500 Stock List (Sample tickers)
 SP500_TICKERS = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 'BRK-B', 'NVDA', 'JPM', 'JNJ', 'V']
@@ -111,12 +64,14 @@ def simulate_investment(weights, mu, years, initial_investment=10000):
 
 def output_results(risk_level):
     try:
-        # Use real data instead of sample data
-        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 5))  # Reduced to 5 tickers to avoid API limits
+        # 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)
         
         if stocks_df.empty:
-            raise ValueError("Failed to fetch stock data")
+            raise ValueError("No stock data received")
         
         returns = stocks_df.pct_change().dropna()
         
@@ -229,4 +184,4 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
     )
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=True)  # Added share=True to create a public link