Refactor performance calculation logic in PerformanceCalculator class. Updated profit factor and expectancy calculations to use sums of winning and losing trades for improved accuracy. This change enhances the reliability of performance metrics in trading analysis.

src/stats/performance_calculator.py

@@ -121,12 +121,16 @@ class PerformanceCalculator:
             
             # Calculate win rate and profit factor
             win_rate = (total_wins / total_trades * 100) if total_trades > 0 else 0
-            profit_factor = (total_pnl / abs(total_losses)) if total_losses < 0 else float('inf') if total_pnl > 0 else 0
+            
+            # Calculate sum of winning and losing trades
+            sum_winning = sum(token.get('sum_of_winning_pnl', 0) for token in token_stats)
+            sum_losing = abs(sum(token.get('sum_of_losing_pnl', 0) for token in token_stats))
+            profit_factor = (sum_winning / sum_losing) if sum_losing > 0 else float('inf') if sum_winning > 0 else 0
             
             # Calculate expectancy
-            avg_win = total_pnl / total_wins if total_wins > 0 else 0
-            avg_loss = total_losses / total_losses if total_losses > 0 else 0
-            expectancy = (avg_win * (win_rate/100)) - (abs(avg_loss) * (1 - win_rate/100))
+            avg_win = sum_winning / total_wins if total_wins > 0 else 0
+            avg_loss = sum_losing / total_losses if total_losses > 0 else 0
+            expectancy = (avg_win * (win_rate/100)) - (avg_loss * (1 - win_rate/100))
             
             # Get max drawdown
             max_drawdown, max_drawdown_pct = self.get_live_max_drawdown()

trading_bot.py

@@ -14,7 +14,7 @@ from datetime import datetime
 from pathlib import Path
 
 # Bot version
-BOT_VERSION = "2.4.212"
+BOT_VERSION = "2.4.213"
 
 # Add src directory to Python path
 sys.path.insert(0, str(Path(__file__).parent / "src"))