Enhance financial tools error handling: added fallback for stock price retrieval, improved exception handling for individual stock errors, and updated README formatting for clarity.

README.md

@@ -215,10 +215,10 @@ The system automatically detects your preference:
 
 ## Issues to Resolve
 The problem is in the current flow:
-    1. Agent executor runs all tools
-    2. We collect ALL results
-    3. Then display everything at once
-    4. Then stream the final response
+1. Agent executor runs all tools
+2. We collect ALL results
+3. Then display everything at once
+4. Then stream the final response
 
 ## 🤝 Contributing
 

agents/tools.py

@@ -475,20 +475,41 @@ class FinancialTools:
                     symbol = holding["symbol"]
                     shares = holding["shares"]
 
-                    stock = yf.Ticker(symbol)
-                    current_price = stock.info.get("currentPrice", 0)
-                    value = current_price * shares
-                    total_value += value
-
-                    portfolio_data.append(
-                        {
-                            "symbol": symbol,
-                            "shares": shares,
-                            "current_price": current_price,
-                            "value": value,
-                            "sector": stock.info.get("sector", "Unknown"),
-                        }
-                    )
+                    try:
+                        stock = yf.Ticker(symbol)
+                        info = stock.info
+                        current_price = info.get("currentPrice", 0)
+                        
+                        # Fallback for current price if not available
+                        if current_price == 0:
+                            hist = stock.history(period="1d")
+                            if not hist.empty:
+                                current_price = hist["Close"].iloc[-1]
+                        
+                        value = current_price * shares
+                        total_value += value
+
+                        portfolio_data.append(
+                            {
+                                "symbol": symbol,
+                                "shares": shares,
+                                "current_price": current_price,
+                                "value": value,
+                                "sector": info.get("sector", "Unknown"),
+                            }
+                        )
+                    except Exception as e:
+                        # Handle individual stock errors gracefully
+                        portfolio_data.append(
+                            {
+                                "symbol": symbol,
+                                "shares": shares,
+                                "current_price": 0,
+                                "value": 0,
+                                "sector": "Unknown",
+                                "error": f"Failed to fetch data: {str(e)}"
+                            }
+                        )
 
                 # Calculate allocations
                 for item in portfolio_data:
@@ -498,7 +519,11 @@ class FinancialTools:
 
                 # Sector diversification
                 df = pd.DataFrame(portfolio_data)
-                sector_allocation = df.groupby("sector")["allocation"].sum().to_dict()
+                # Handle case where portfolio_data is empty or sector column has issues
+                if not df.empty and "sector" in df.columns:
+                    sector_allocation = df.groupby("sector")["allocation"].sum().to_dict()
+                else:
+                    sector_allocation = {}
 
                 analysis = {
                     "total_portfolio_value": f"${total_value:.2f}",
@@ -516,11 +541,12 @@ class FinancialTools:
                         "Consider diversifying with more holdings"
                     )
 
-                max_allocation = max(item["allocation"] for item in portfolio_data)
-                if max_allocation > 30:
-                    analysis["recommendations"].append(
-                        f"High concentration risk: largest holding is {max_allocation:.1f}%"
-                    )
+                if portfolio_data:
+                    max_allocation = max(item["allocation"] for item in portfolio_data)
+                    if max_allocation > 30:
+                        analysis["recommendations"].append(
+                            f"High concentration risk: largest holding is {max_allocation:.1f}%"
+                        )
 
                 return json.dumps(analysis, indent=2)