ramseshk
b59dcc3629
Set up the directory structure and wrote placeholder logic for: - Order Book Imbalance: trades on L2 bid/ask skew - Iceberg/TWAP detection: follows whale accumulation patterns - Funding rate arbitrage: delta-neutral carry on perp funding - Pairs trading: BTC/ETH spread mean reversion - Avellaneda-Stoikov market making: optimal bid/ask quoting Also added shared risk manager, portfolio tracker, and a plain-language strategy walkthrough in docs/.
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
"""
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Performance metrics.
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Sharpe ratio, Sortino ratio, max drawdown, win rate.
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Standard toolbox for evaluating a trading strategy.
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"""
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import numpy as np
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def sharpe(returns: list[float], rf: float = 0.0, periods: int = 365) -> float:
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if len(returns) < 2:
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return 0.0
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excess = np.mean(returns) - rf
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std = np.std(returns, ddof=1)
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return (excess / std) * np.sqrt(periods) if std > 0 else 0.0
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def sortino(returns: list[float], rf: float = 0.0, periods: int = 365) -> float:
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if len(returns) < 2:
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return 0.0
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excess = np.mean(returns) - rf
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downside = [r for r in returns if r < 0]
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d_std = np.std(downside, ddof=1) if downside else 0.0
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return (excess / d_std) * np.sqrt(periods) if d_std > 0 else 0.0
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def max_drawdown(equity: list[float]) -> float:
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if not equity:
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return 0.0
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peak = equity[0]
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worst = 0.0
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for v in equity:
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if v > peak:
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peak = v
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dd = (peak - v) / peak if peak > 0 else 0.0
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worst = max(worst, dd)
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return worst
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def win_rate(trades: list[dict]) -> float:
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if not trades:
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return 0.0
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return sum(1 for t in trades if t.get("pnl", 0) > 0) / len(trades)
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