mirror of
https://github.com/xavierchuan/FX-ML-Trading-Engine.git
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xiaochuan
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
from __future__ import annotations
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import argparse
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from pathlib import Path
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from typing import List
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import pandas as pd
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from loguru import logger
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METRIC_COLS = [
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"sharpe",
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"ann_return",
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"ann_vol",
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"max_drawdown",
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"expectancy",
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"trades",
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"win_rate",
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"return_pct",
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"rr",
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"median_hold",
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"symbol",
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"source_file",
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]
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def main():
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parser = argparse.ArgumentParser(description="Compare zero-cost vs real-cost grid results.")
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parser.add_argument("--zero", required=True, help="CSV from zero-cost grid run.")
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parser.add_argument("--real", required=True, help="CSV from real-cost grid run.")
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parser.add_argument("--out", default="data/grid/cost_comparison.csv", help="Output CSV path.")
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parser.add_argument("--top", type=int, default=20, help="Print top-N rows with smallest Sharpe delta.")
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args = parser.parse_args()
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df_zero = pd.read_csv(args.zero)
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df_real = pd.read_csv(args.real)
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shared_cols = [c for c in df_zero.columns if c in df_real.columns]
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if not shared_cols:
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raise ValueError("No overlapping columns between zero and real CSVs.")
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param_cols: List[str] = [c for c in shared_cols if c not in METRIC_COLS]
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if not param_cols:
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raise ValueError("Unable to infer parameter columns for join; ensure CSVs contain metrics from METRIC_COLS.")
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z = df_zero.rename(columns={col: f"{col}_zero" for col in METRIC_COLS if col in df_zero.columns})
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r = df_real.rename(columns={col: f"{col}_real" for col in METRIC_COLS if col in df_real.columns})
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merged = z.merge(r, on=param_cols, how="inner", suffixes=("_zero", "_real"))
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if merged.empty:
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raise RuntimeError("Join result is empty; ensure both CSVs share the same parameter combinations.")
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if "sharpe_zero" in merged.columns and "sharpe_real" in merged.columns:
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merged["delta_sharpe"] = merged["sharpe_real"] - merged["sharpe_zero"]
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if "ann_return_zero" in merged.columns and "ann_return_real" in merged.columns:
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merged["delta_ann_return"] = merged["ann_return_real"] - merged["ann_return_zero"]
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if "expectancy_zero" in merged.columns and "expectancy_real" in merged.columns:
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merged["delta_expectancy"] = merged["expectancy_real"] - merged["expectancy_zero"]
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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merged.to_csv(out_path, index=False)
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logger.info(f"Cost comparison saved to {out_path} (rows={len(merged)})")
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if "delta_sharpe" in merged.columns:
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top_df = merged.sort_values("delta_sharpe", ascending=False).head(args.top)
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print(top_df[param_cols + ["sharpe_zero", "sharpe_real", "delta_sharpe"]].to_string(index=False))
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if __name__ == "__main__":
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main()
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