Update scoring system

src/Managing.Application/GeneticService.cs

@@ -813,7 +813,7 @@ public class TradingBotFitness : IFitness
             ).Result;
 
             // Calculate multi-objective fitness based on backtest results
-            var fitness = CalculateMultiObjectiveFitness(backtest, config);
+                            var fitness = CalculateFitness(backtest, config);
 
             return fitness;
         }
@@ -824,38 +824,13 @@ public class TradingBotFitness : IFitness
         }
     }
 
-    private double CalculateMultiObjectiveFitness(Backtest backtest, TradingBotConfig config)
+    private double CalculateFitness(Backtest backtest, TradingBotConfig config)
     {
         if (backtest == null || backtest.Statistics == null)
             return 0.1;
 
-        var stats = backtest.Statistics;
-
-        // Multi-objective fitness function (matching frontend)
-        var pnlScore = Math.Max(0, (double)stats.TotalPnL / 1000); // Normalize PnL
-        var winRateScore = backtest.WinRate / 100.0; // Normalize win rate
-        var riskRewardScore =
-            Math.Min(2, (double)stats.WinningTrades / Math.Max(1, Math.Abs((double)stats.LoosingTrades)));
-        var consistencyScore = 1 - Math.Abs((double)stats.TotalPnL - (double)backtest.FinalPnl) /
-            Math.Max(1, Math.Abs((double)stats.TotalPnL));
-
-        // Risk-reward ratio bonus
-        var riskRewardRatio = (double)(config.MoneyManagement.TakeProfit / config.MoneyManagement.StopLoss);
-        var riskRewardBonus = Math.Min(0.2, (riskRewardRatio - 1.1) * 0.1);
-
-        // Drawdown score (normalized to 0-1, where lower drawdown is better)
-        var maxDrawdownPc = Math.Abs((double)stats.MaxDrawdownPc);
-        var drawdownScore = Math.Max(0, 1 - (maxDrawdownPc / 50));
-
-        // Weighted combination
-        var fitness =
-            pnlScore * 0.3 +
-            winRateScore * 0.2 +
-            riskRewardScore * 0.2 +
-            consistencyScore * 0.1 +
-            riskRewardBonus * 0.1 +
-            drawdownScore * 0.1;
-
-        return Math.Max(0, fitness);
+        // Use the comprehensive backtest score directly as fitness
+        // The BacktestScorer already includes all important metrics with proper weighting
+        return backtest.Score;
     }
 }