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https://github.com/KhizarImran/backtestingfx.git
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KhizarImran
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
"""
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Grid-search an SMA crossover over fast/slow periods.
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The strategy is written as a *signal function* rather than a Strategy subclass:
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it returns the target lot size for every bar in one vectorised pass. That runs
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once per parameter combination, so the per-bar work happens entirely in Rust
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and the whole grid runs on parallel threads.
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"""
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import time
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import numpy as np
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import pandas as pd
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from backtestingfx import Backtest
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LOT = 0.1
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def sma_cross(df, fast, slow):
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"""Long LOT lots while the fast SMA is above the slow one, otherwise flat."""
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fast_sma = df["close"].rolling(fast).mean()
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slow_sma = df["close"].rolling(slow).mean()
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# NaN during warmup compares False, so the untradeable head comes out flat
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return np.where(fast_sma > slow_sma, LOT, 0.0)
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df = pd.read_csv("data/EURUSD_1H.csv")
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backtest = Backtest(df, cash=10_000, commission=3.5, spread=0.00002)
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fast_range = range(5, 26)
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slow_range = range(30, 101, 5)
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combos = len(fast_range) * len(slow_range)
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start = time.perf_counter()
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results = backtest.optimize(
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sma_cross,
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maximize="total_return_pct",
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fast=fast_range,
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slow=slow_range,
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)
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elapsed = time.perf_counter() - start
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print(f"{combos} combinations over {len(df):,} bars in {elapsed:.2f}s "
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f"({combos * len(df) / elapsed / 1e6:.1f}M bar-sims/sec)\n")
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print(f"{'fast':>6}{'slow':>6}{'return %':>12}{'trades':>9}{'win %':>8}{'max dd %':>10}")
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print("-" * 51)
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for params, stats in results[:10]:
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print(
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f"{params['fast']:>6}{params['slow']:>6}{stats.total_return_pct:>12.2f}"
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f"{stats.num_trades:>9}{stats.win_rate_pct:>8.1f}{stats.max_drawdown_pct:>10.2f}"
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)
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best_params, best_stats = results[0]
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print(f"\nBest: {best_params}")
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print(best_stats)
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