| """Summarise WarriorGapFade real-tick tester runs: python gf_summary.py claude_gf2_EURCHF_d0 ..."""
|
| from __future__ import annotations
|
| import sys
|
| import numpy as np
|
| sys.path.insert(0, __file__.rsplit("\\", 1)[0] if "\\" in __file__ else ".")
|
| import grid_summary as gs # noqa: E402
|
|
|
| COMMON = gs.COMMON
|
|
|
| if __name__ == "__main__":
|
| print(f"{'run':<24}{'n':>5}{'net':>9}{'PF':>6}{'eqDD':>7}{'win':>6}{'target':>8}{'stop':>6}{'time':>6}"
|
| f"{'bp/trade':>10}{'2016-20':>9}{'2021-26':>9}")
|
| for nm in sys.argv[1:]:
|
| try:
|
| rp = gs.report(nm)
|
| raw = np.genfromtxt(rf"{COMMON}\gapfade_trades_{nm}.csv", delimiter=",", skip_header=1,
|
| dtype=str, encoding="ansi")
|
| except Exception as e: # noqa: BLE001
|
| print(f"{nm:<24} unreadable: {e}")
|
| continue
|
| if raw.ndim == 1:
|
| raw = raw[None, :]
|
| net = raw[:, 7].astype(float)
|
| ep = raw[:, 3].astype(float)
|
| vol = raw[:, 4].astype(float)
|
| why = raw[:, 8]
|
| yr = np.array([int(x[:4]) for x in raw[:, 2]])
|
| #--- price return per trade, independent of lot size, in bp
|
| xp = raw[:, 6].astype(float)
|
| side = raw[:, 12].astype(float)
|
| bp = side * (xp - ep) / ep * 1e4
|
| early, late = bp[yr <= 2020], bp[yr >= 2021]
|
| print(f"{nm:<24}{len(net):>5}{net.sum():>+9.0f}{rp.get('pf', float('nan')):>6.2f}{rp.get('eqdd', float('nan')):>7.2%}"
|
| f"{np.mean(net > 0):>6.0%}{np.sum(why == 'target'):>8}{np.sum(why == 'stop'):>6}{np.sum(why == 'expert'):>6}"
|
| f"{bp.mean():>+10.1f}{early.mean() if len(early) else float('nan'):>+9.1f}{late.mean() if len(late) else float('nan'):>+9.1f}")
|
| print("bp/trade = fill-to-fill price move (spread paid at both fills); 'target' exits are in-EA closes"
|
| " counted by the journal as expert, so the split is approximate.")
|
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