Add Date column and sort Investment Performance table by date

- Added Date column on the right showing first buy date (YYYY-MM-DD format)
- Sort table by date with most recent trades first
- Captures first buy date for each symbol from order history
- Maintains all existing styling and functionality

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

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

app.py

@@ -351,7 +351,7 @@ def refresh_investment_performance_table():
     positions = get_current_positions()
     
     # Create proper empty DataFrame with correct column names
-    columns = ['Symbol', 'Status', 'IPO Price', 'Buy Price', 'Sell Price', 'Investment', 'P&L ($)', 'P&L (%)']
+    columns = ['Symbol', 'Status', 'IPO Price', 'Buy Price', 'Sell Price', 'Investment', 'P&L ($)', 'P&L (%)', 'Date']
     
     logger.info(f"Found {len(orders)} total orders for performance analysis")
     
@@ -401,6 +401,9 @@ def refresh_investment_performance_table():
                 # Get IPO price if available
                 ipo_price = ipo_price_lookup.get(symbol, 0)
                 
+                # Get first buy date
+                first_buy_date = min(buy_orders, key=lambda x: x.filled_at).filled_at.strftime('%Y-%m-%d')
+                
                 # Calculate sell price (average of all sells)
                 if sell_orders:
                     avg_sell_price = sum(float(o.filled_qty or 0) * float(o.filled_avg_price or 0) for o in sell_orders) / sum(float(o.filled_qty or 0) for o in sell_orders)
@@ -459,9 +462,14 @@ def refresh_investment_performance_table():
                     'Investment': f"${investment:.2f}",
                     'P&L ($)': pl_dollar_str,
                     'P&L (%)': pl_percent_str,
-                    '_row_bg': row_bg  # Store background color for styling
+                    'Date': first_buy_date,
+                    '_row_bg': row_bg,  # Store background color for styling
+                    '_sort_date': min(buy_orders, key=lambda x: x.filled_at).filled_at  # Store datetime for sorting
                 })
     
+    # Sort by date (most recent first)
+    invested_data.sort(key=lambda x: x['_sort_date'], reverse=True)
+    
     df = pd.DataFrame(invested_data)
     return df