627b2326df
Support builtin and custom generate_signals strategies with SQLite persistence, exhaustive grid scans (VectorBT comb optimization for MA crossover), professional backtest/optimize UI, and split harvester/app requirements with BuildKit pip cache.
62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
"""Portfolio metric helpers shared by engine and optimizers."""
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from __future__ import annotations
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from typing import Any
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import pandas as pd
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import vectorbt as vbt
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def safe_float(value: Any) -> float:
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try:
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if value is None or (isinstance(value, float) and value != value):
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return 0.0
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return float(value)
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except (TypeError, ValueError):
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return 0.0
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def run_from_signals(
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close: pd.Series,
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entries: pd.Series,
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exits: pd.Series,
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init_cash: float,
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fees: float,
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params: dict[str, Any],
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metric: str = "sharpe_ratio",
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) -> dict[str, Any]:
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portfolio = vbt.Portfolio.from_signals(
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close,
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entries=entries,
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exits=exits,
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init_cash=init_cash,
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fees=fees,
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freq="1D",
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)
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stats = portfolio.stats()
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sharpe = safe_float(stats.get("Sharpe Ratio"))
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sortino = safe_float(stats.get("Sortino Ratio"))
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max_dd = safe_float(stats.get("Max Drawdown [%]")) / 100.0
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total_return = safe_float(stats.get("Total Return [%]")) / 100.0
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win_rate = safe_float(stats.get("Win Rate [%]")) / 100.0
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total_trades = int(stats.get("Total Trades", 0) or 0)
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score_map = {
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"sharpe_ratio": sharpe,
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"sortino_ratio": sortino,
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"total_return": total_return,
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"max_drawdown": -max_dd,
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}
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return {
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**params,
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"sharpe_ratio": sharpe,
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"sortino_ratio": sortino,
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"max_drawdown": max_dd,
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"total_return": total_return,
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"win_rate": win_rate,
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"total_trades": total_trades,
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"score": score_map.get(metric, sharpe),
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}
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