Fix Investment Performance table to show all trading symbols

- Modified refresh_investment_performance_table() to process ALL symbols from order history
- No longer limited to just IPOs marked as "INVESTED" in VM data
- Now shows comprehensive trading performance for EGG, SLDE, MENS, and all other traded symbols
- Maintains IPO price lookup when available for reference
- Sorts symbols alphabetically for better organization

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

app.py

@@ -344,7 +344,7 @@ def get_order_history():
         return []
 
 def refresh_investment_performance_table():
-    """Refresh investment performance table with P&L for invested IPOs"""
+    """Refresh investment performance table with P&L for all trading symbols"""
     # Get IPO data and orders
     ipos = fetch_from_vm('ipos?limit=100', [])
     orders = get_order_history()
@@ -353,39 +353,36 @@ def refresh_investment_performance_table():
     # Create proper empty DataFrame with correct column names
     columns = ['Symbol', 'Status', 'IPO Price', 'Buy Price', 'Current Price', 'Quantity', 'Investment', 'Current Value', 'P&L ($)', 'P&L (%)']
     
-    if not ipos:
+    logger.info(f"Found {len(orders)} total orders for performance analysis")
+    
+    if not orders:
         return pd.DataFrame(columns=columns)
     
-    # Debug: Let's see what we're getting
-    logger.info(f"Found {len(ipos)} total IPOs")
-    invested_symbols = [ipo for ipo in ipos if ipo.get('investment_status') == 'INVESTED']
-    logger.info(f"Found {len(invested_symbols)} invested IPOs")
-    logger.info(f"Found {len(orders)} total orders")
+    # Get all unique symbols from order history
+    symbols_traded = set()
+    for order in orders:
+        if hasattr(order, 'symbol') and order.symbol:
+            symbols_traded.add(order.symbol)
     
-    invested_data = []
+    logger.info(f"Found {len(symbols_traded)} unique symbols traded: {list(symbols_traded)}")
     
-    if not invested_symbols:
-        # No invested IPOs found, add a debug row to see what's happening
-        invested_data.append({
-            'Symbol': "🔍 DEBUG",
-            'Status': f"Found {len(ipos)} IPOs total",
-            'IPO Price': "Check logs",
-            'Buy Price': "N/A",
-            'Current Price': "N/A", 
-            'Quantity': "0",
-            'Investment': "$0.00",
-            'Current Value': "N/A",
-            'P&L ($)': "$0.00",
-            'P&L (%)': "0.00%"
-        })
-        return pd.DataFrame(invested_data)
+    # Create IPO price lookup from VM data
+    ipo_price_lookup = {}
+    for ipo in ipos:
+        symbol = ipo.get('symbol', '')
+        if symbol:
+            try:
+                price = float(ipo.get('trading_price', 0))
+                if price > 0:
+                    ipo_price_lookup[symbol] = price
+            except (ValueError, TypeError):
+                pass
     
-    # Simple version first - just show the IPOs that were invested in
-    for ipo in invested_symbols:
-        symbol = ipo.get('symbol', 'N/A')
-        ipo_price = ipo.get('trading_price', 0)
-        
-        # Check if we have order data for this symbol
+    invested_data = []
+    
+    # Process each symbol that was traded
+    for symbol in sorted(symbols_traded):
+        # Get all orders for this symbol
         symbol_orders = [o for o in orders if o.symbol == symbol]
         
         if symbol_orders:
@@ -401,6 +398,9 @@ def refresh_investment_performance_table():
                 total_sold = sum(float(o.filled_qty or 0) for o in sell_orders)
                 current_qty = total_bought - total_sold
                 
+                # Get IPO price if available
+                ipo_price = ipo_price_lookup.get(symbol, 0)
+                
                 if current_qty > 0:
                     # Still holding
                     status = "🟦 HOLDING"
@@ -433,7 +433,7 @@ def refresh_investment_performance_table():
                 invested_data.append({
                     'Symbol': f"{pl_indicator} {symbol}",
                     'Status': status,
-                    'IPO Price': f"${ipo_price}" if ipo_price != 'N/A' else 'N/A',
+                    'IPO Price': f"${ipo_price:.2f}" if ipo_price > 0 else 'N/A',
                     'Buy Price': f"${avg_buy_price:.2f}",
                     'Current Price': f"${current_price:.2f}" if current_price > 0 else "N/A",
                     'Quantity': f"{current_qty:.0f}",
@@ -442,20 +442,6 @@ def refresh_investment_performance_table():
                     'P&L ($)': f"${pl_dollars:+.2f}",
                     'P&L (%)': f"{pl_percent:+.2f}%"
                 })
-        else:
-            # No order data found, show placeholder
-            invested_data.append({
-                'Symbol': f"❓ {symbol}",
-                'Status': "🔍 NO ORDERS",
-                'IPO Price': f"${ipo_price}" if ipo_price != 'N/A' else 'N/A',
-                'Buy Price': "N/A",
-                'Current Price': "N/A", 
-                'Quantity': "0",
-                'Investment': "$0.00",
-                'Current Value': "N/A",
-                'P&L ($)': "$0.00",
-                'P&L (%)': "0.00%"
-            })
     
     return pd.DataFrame(invested_data)