Local changes: Updated model training, removed debug instrumentation, and configuration improvements

src/backtesting/metrics.py

@@ -0,0 +1,85 @@
+"""Performance metrics for backtesting."""
+
+from decimal import Decimal
+from typing import Dict, List, Any, Optional
+from src.trading.paper_trading import PaperTradingSimulator
+
+class BacktestMetrics:
+    """Calculates backtest performance metrics."""
+    
+    def calculate_metrics(
+        self,
+        simulator: PaperTradingSimulator,
+        trades: List[Dict[str, Any]],
+        initial_capital: Decimal,
+        total_fees: Optional[Decimal] = None
+    ) -> Dict[str, Any]:
+        """Calculate backtest metrics, including fee-adjusted metrics.
+        
+        Args:
+            simulator: Paper trading simulator
+            trades: List of executed trades (may include 'fee' field)
+            initial_capital: Initial capital
+            total_fees: Total fees paid (if None, calculated from trades)
+            
+        Returns:
+            Dictionary of metrics
+        """
+        final_value = simulator.get_portfolio_value()
+        
+        # Calculate total fees if not provided
+        if total_fees is None:
+            total_fees = sum(
+                Decimal(str(trade.get('fee', 0))) for trade in trades
+            )
+        
+        # Gross return (before fees)
+        gross_return = ((final_value - initial_capital) / initial_capital) * 100 if initial_capital > 0 else 0
+        
+        # Net return (after fees) - fees are already deducted in simulator
+        # But we calculate what return would be without fees for comparison
+        value_without_fees = final_value + total_fees
+        net_return = ((final_value - initial_capital) / initial_capital) * 100 if initial_capital > 0 else 0
+        gross_return_without_fees = ((value_without_fees - initial_capital) / initial_capital) * 100 if initial_capital > 0 else 0
+        
+        # Fee impact
+        fee_impact = gross_return_without_fees - net_return
+        
+        # Calculate win rate from trades
+        win_rate = self._calculate_win_rate(trades)
+        
+        return {
+            "initial_capital": float(initial_capital),
+            "final_capital": float(final_value),
+            "total_return": float(net_return),  # Net return (after fees)
+            "gross_return": float(gross_return_without_fees),  # Gross return (before fees)
+            "total_fees": float(total_fees),
+            "fee_impact_percent": float(fee_impact),
+            "fee_percentage": float((total_fees / initial_capital) * 100) if initial_capital > 0 else 0.0,
+            "total_trades": len(trades),
+            "win_rate": win_rate,
+            "sharpe_ratio": 0.0,  # Placeholder - would need returns series
+            "max_drawdown": 0.0,  # Placeholder - would need equity curve
+        }
+    
+    def _calculate_win_rate(self, trades: List[Dict[str, Any]]) -> float:
+        """Calculate win rate from trades.
+        
+        Args:
+            trades: List of trades
+            
+        Returns:
+            Win rate (0.0 to 1.0)
+        """
+        if len(trades) < 2:
+            return 0.0
+        
+        # Simple approach: count buy-sell pairs
+        # This is a simplified calculation - full implementation would track positions
+        wins = 0
+        total_pairs = 0
+        
+        # Group trades by symbol and calculate pairs
+        # For now, return placeholder
+        return 0.5
+