63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
"""
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Grid-search optimizer.
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Iterates over a parameter grid, re-instantiates the strategy for each
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combination, runs the backtest on the in-sample slice, and ranks by a
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composite score (Sharpe × expectancy × return).
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"""
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from __future__ import annotations
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import itertools
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from dataclasses import dataclass
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from typing import Any, Type
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import numpy as np
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import pandas as pd
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from .backtest import BacktestEngine
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from .metrics import Metrics, compute
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@dataclass
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class OptResult:
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params: dict[str, Any]
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metrics: Metrics
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score: float
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def grid_search(
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StrategyClass: Type,
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param_grid: dict[str, list],
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df: pd.DataFrame,
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engine: BacktestEngine,
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*,
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verbose: bool = False,
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) -> list[OptResult]:
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"""
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Run every combination of parameters in `param_grid`.
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Returns results sorted best-first by composite score.
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"""
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keys = list(param_grid.keys())
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values = list(param_grid.values())
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combos = list(itertools.product(*values))
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results: list[OptResult] = []
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for idx, combo in enumerate(combos):
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params = dict(zip(keys, combo))
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strategy = StrategyClass(**params)
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trades, equity = engine.run(df, strategy)
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m = compute(trades, equity, engine.initial_balance)
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s = m.score()
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results.append(OptResult(params=params, metrics=m, score=s))
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if verbose and (idx + 1) % 50 == 0:
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print(f" [{idx+1}/{len(combos)}] best so far: "
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f"{max(results, key=lambda r: r.score).score:.4f}")
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results.sort(key=lambda r: r.score, reverse=True)
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return results
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