68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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import numpy as np
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import pandas as pd
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@dataclass
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class Perf:
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total_return: float
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max_drawdown: float
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sharpe_ratio: float
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win_rate: float
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num_trades: int
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final_equity: float
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initial_equity: float
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num_stop_losses: int
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total_fees: float
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total_slippage_usd: float
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avg_slippage_bps: float
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def compute_metrics(equity_curve: pd.Series, trades: list[dict], periods_per_year: int = None) -> Perf:
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"""
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Compute backtest performance metrics.
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Args:
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equity_curve: Series of portfolio values over time
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trades: List of trade event dictionaries
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periods_per_year: Number of periods in a year for Sharpe annualization.
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Defaults to hourly (252 * 24 = 6048).
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For minute bars: 252 * 24 * 60 = 525600
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For daily bars: 252
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"""
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if periods_per_year is None:
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periods_per_year = 252 * 24 # Default to hourly bars
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ret = equity_curve.pct_change().fillna(0.0)
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total_return = equity_curve.iat[-1] / equity_curve.iat[0] - 1.0
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cummax = equity_curve.cummax()
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dd = (equity_curve / cummax - 1.0).min()
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max_drawdown = dd
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if ret.std(ddof=0) > 0:
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sharpe = (ret.mean() / ret.std(ddof=0)) * np.sqrt(periods_per_year)
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else:
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sharpe = 0.0
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closes = [t for t in trades if t.get("side") == "SELL"]
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wins = [t for t in closes if t.get("pnl", 0.0) > 0]
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win_rate = (len(wins) / len(closes)) if closes else 0.0
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fees = sum(t.get("fee", 0.0) for t in trades)
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slip = sum(t.get("slippage", 0.0) for t in trades)
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slippage_bps = [t.get("slippage_bps", 0.0) for t in trades if "slippage_bps" in t]
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return Perf(
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total_return=total_return,
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max_drawdown=max_drawdown,
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sharpe_ratio=sharpe,
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win_rate=win_rate,
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num_trades=len(closes),
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final_equity=float(equity_curve.iat[-1]),
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initial_equity=float(equity_curve.iat[0]),
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num_stop_losses=sum(1 for t in closes if t.get("reason") == "stop"),
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total_fees=fees,
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total_slippage_usd=slip,
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avg_slippage_bps=float(np.mean(slippage_bps)) if slippage_bps else 0.0,
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) |