mirror of
https://github.com/KhizarImran/backtestingfx.git
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Khizar Imran
872c8a30c8
Reuse the Python callback method-name string and expose constant-time position count and presence. Preserve dynamic callbacks, Bar identity, and snapshot behavior with regression tests. Add isolated callback measurements and replace approximate cross-library comparisons with matched trades and equity assertions, separate setup/run timings, and reproducible reports. Track the remaining ingestion bottleneck in #53. Refs #48, #38, #39, #53 Codex 🤖, On behalf of Khizar
82 lines
3.7 KiB
Python
82 lines
3.7 KiB
Python
"""Time the matched comparison from compare_bt.py only after equivalence passes.
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Run against installed release packages outside the checkout. See benchmark.md.
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"""
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import argparse
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import importlib.metadata
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import json
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from pathlib import Path
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import statistics
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import time
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from benchmark_callbacks import metadata
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from compare_bt import Comparison, load_data
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def measure(size, commission, repeats):
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start = time.perf_counter()
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comparison = Comparison(load_data(size), commission=commission)
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common_setup = time.perf_counter() - start
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# Untimed equivalence check and warmup before collecting any timing samples.
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reference, _ = comparison.run("backtesting.py")
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actual, _ = comparison.run("backtestingfx")
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comparison.verify(reference, actual)
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samples = {name: [] for name in ("backtesting.py", "backtestingfx")}
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for repetition in range(repeats):
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order = list(samples)
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if repetition % 2:
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order.reverse()
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results = {}
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for name in order:
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result, timings = comparison.run(name)
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results[name] = result
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samples[name].append(timings)
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# Every measured pair must also agree. Assertions are outside timing.
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comparison.verify(results["backtesting.py"], results["backtestingfx"])
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report = {"bars": size, "commission_per_lot_per_side": commission,
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"common_preparation_seconds": common_setup,
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"matching_trades": actual.num_trades, "final_equity": actual.final_cash,
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"equivalence": "passed", "libraries": {}}
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for name, times in samples.items():
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report["libraries"][name] = {
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**{key: statistics.median(row[key] for row in times) for key in times[0]},
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"samples": times,
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}
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return report
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--sizes", type=int, nargs="+", default=[2000, 10_000, 50_000])
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parser.add_argument("--commissions", type=float, nargs="+", default=[0.0, 7.0])
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parser.add_argument("--repeats", type=int, default=5)
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parser.add_argument("--output", type=Path)
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args = parser.parse_args()
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if args.repeats < 1 or any(size < 3 for size in args.sizes):
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parser.error("sizes must be at least three and repeats must be positive")
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report = {"metadata": metadata(), "repeats": args.repeats, "results": []}
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report["metadata"]["package_versions"]["backtesting"] = importlib.metadata.version("backtesting")
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print("Times are medians; run includes simulation, finalization, and each library's statistics.")
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for size in args.sizes:
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for commission in args.commissions:
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result = measure(size, commission, args.repeats)
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report["results"].append(result)
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print(f"\nPASS: {size:,} bars, commission {commission}/lot/side, "
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f"{result['matching_trades']} matching trades, equity {result['final_equity']:.8f}",
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flush=True)
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for name, timings in result["libraries"].items():
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print(f" {name:<15} setup {timings['setup_seconds']:.4f} s "
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f"run {timings['simulation_seconds']:.4f} s "
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f"total {timings['total_seconds']:.4f} s", flush=True)
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bt = result["libraries"]["backtesting.py"]
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fx = result["libraries"]["backtestingfx"]
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print(f" backtestingfx speed ratio: run {bt['simulation_seconds'] / fx['simulation_seconds']:.2f}x; "
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f"total {bt['total_seconds'] / fx['total_seconds']:.2f}x. Above 1 means faster.", flush=True)
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if args.output:
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args.output.write_text(json.dumps(report, indent=2) + "\n")
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if __name__ == "__main__":
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main()
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