forked from mnbvc188199/Warrior_EA
6192393511
Hypotheses were registered in FX_PLAN.md before each round. Trend, breakout, cross reversion, hour seasonality, month-end USD, carry-cross dip-buy, metals dip-buy and flight-to-safety all fail the bar. The weekend-gap fade looked like the best result of the project on bar data (OOS t 20, 28/28 pairs) and loses on real ticks (EURCHF PF 0.52, AUDNZD PF 0.53): the Sunday-open spread is as wide as the gap. WarriorGapFade is kept as the research artifact that proved it and is flagged DO NOT TRADE. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
37 lines
1.9 KiB
Python
37 lines
1.9 KiB
Python
"""Summarise WarriorGapFade real-tick tester runs: python gf_summary.py claude_gf2_EURCHF_d0 ..."""
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from __future__ import annotations
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import sys
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import numpy as np
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sys.path.insert(0, __file__.rsplit("\\", 1)[0] if "\\" in __file__ else ".")
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import grid_summary as gs # noqa: E402
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COMMON = gs.COMMON
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if __name__ == "__main__":
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print(f"{'run':<24}{'n':>5}{'net':>9}{'PF':>6}{'eqDD':>7}{'win':>6}{'target':>8}{'stop':>6}{'time':>6}"
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f"{'bp/trade':>10}{'2016-20':>9}{'2021-26':>9}")
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for nm in sys.argv[1:]:
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try:
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rp = gs.report(nm)
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raw = np.genfromtxt(rf"{COMMON}\gapfade_trades_{nm}.csv", delimiter=",", skip_header=1,
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dtype=str, encoding="ansi")
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except Exception as e: # noqa: BLE001
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print(f"{nm:<24} unreadable: {e}")
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continue
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if raw.ndim == 1:
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raw = raw[None, :]
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net = raw[:, 7].astype(float)
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ep = raw[:, 3].astype(float)
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vol = raw[:, 4].astype(float)
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why = raw[:, 8]
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yr = np.array([int(x[:4]) for x in raw[:, 2]])
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#--- price return per trade, independent of lot size, in bp
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xp = raw[:, 6].astype(float)
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side = raw[:, 12].astype(float)
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bp = side * (xp - ep) / ep * 1e4
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early, late = bp[yr <= 2020], bp[yr >= 2021]
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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%}"
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f"{np.mean(net > 0):>6.0%}{np.sum(why == 'target'):>8}{np.sum(why == 'stop'):>6}{np.sum(why == 'expert'):>6}"
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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}")
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print("bp/trade = fill-to-fill price move (spread paid at both fills); 'target' exits are in-EA closes"
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" counted by the journal as expert, so the split is approximate.")
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