21 lines
1.1 KiB
Python
21 lines
1.1 KiB
Python
 | import numpy as np
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def monte_carlo(trades, initial, runs=2000, ruin_dd_pct=30.0, seed=42):
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"""Rééchantillonne les trades (rendement par trade, composé) pour estimer la dispersion des résultats."""
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if len(trades) < 10:
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return None
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rets = (trades.pnl / trades.equity_before).to_numpy()
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rng = np.random.default_rng(seed)
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idx = rng.integers(0, len(rets), size=(runs, len(rets)))
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paths = initial * np.cumprod(1 + rets[idx], axis=1)
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paths = np.concatenate([np.full((runs, 1), initial), paths], axis=1)
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peak = np.maximum.accumulate(paths, axis=1)
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dd = ((peak - paths) / peak).max(axis=1) * 100
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final = paths[:, -1]
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q = lambda x, p: float(np.percentile(x, p))
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return {"runs": runs, "trades": len(rets),
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"final_p5": q(final, 5), "final_p50": q(final, 50), "final_p95": q(final, 95),
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"dd_p50": q(dd, 50), "dd_p95": q(dd, 95),
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"prob_loss": float((final < initial).mean() * 100),
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"prob_dd10": float((dd >= 10).mean() * 100),
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"prob_ruin": float((dd >= ruin_dd_pct).mean() * 100), "ruin_dd_pct": ruin_dd_pct}
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