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