Harwinder Sekhon
042b079861
Gate 6h (big-candle entry gate): blocks all XAU entries (core, XAU_PB, XAU_CP) when the primary TF's last 24h contains a bar with range >= K x prior-bar ATR. Ships default-OFF: validation rejected every threshold (K 3-6 on M5/M15 12mo) — removed trades were net-POSITIVE at every cell; M5 quiet-window trades are the only losing bucket. Config knobs big_candle_atr_multiple/lookback_hours, --big-candle backtest flag, Module 17 tests. M5 relaxation SHIPPED (validated 2026-07-30, Oanda bars, $0.30 spread): m5_min_abs_score 30->25, m5_min_confirmation_score 10->5, hour-21 UTC unblocked M5-only. Beats control in ALL 6 calendar years of the 5yr replay (+230.9R/2319 PF 1.26 DD 21.3 vs +168.0R/2048 PF 1.21 DD 27.0), 12mo +107.4R PF 1.36 vs +84.6R PF 1.33, OOS +26.1R vs +17.2R, $0.70 spread +86.9R vs +66.4R. M15: every relaxation retested negative — config unchanged. Declined-but-open: M5 hour-22 block, M15 hour-16 unblock (both fail 5yr/OOS). Evidence in the factory docstring. Tooling: generic --set PATH=JSON research override in gpu_backtest (typo-proof, logged); diagnose_filters filter_tags refreshed for gates 6d-6i with a staleness-guard test (Module 18); research scripts hour_analysis/burst_profile/book_diff. Suite: 268 tests. Claude-Session: https://claude.ai/code/session_014jqHpmNYmwYMowF6bPZyeD
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
"""Per-hour P&L breakdown of a backtest trade book.
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Usage:
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python backtest/research/hour_analysis.py BOOK.csv BAR_MINUTES
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Reports, per UTC hour (both entry hour and DECISION hour = entry_time
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minus one bar, the clock blocked_hours_utc evaluates): trade count,
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total R, mean R, win rate, profit factor. Also a per-(hour, direction)
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table. Analysis tooling only — never imported by the live code.
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"""
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import sys
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import pandas as pd
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def hour_tables(book_csv: str, bar_minutes: int):
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df = pd.read_csv(book_csv)
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et = pd.to_datetime(df["entry_time"], utc=True, format="mixed")
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df["entry_hour"] = et.dt.hour
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df["decision_hour"] = (et - pd.Timedelta(minutes=bar_minutes)).dt.hour
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def table(group_cols):
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g = df.groupby(group_cols)["r_multiple"]
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out = g.agg(count="count", total_r="sum", mean_r="mean",
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wr=lambda r: (r > 0).mean() * 100)
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out["pf"] = g.apply(
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lambda r: r[r > 0].sum() / max(-r[r <= 0].sum(), 1e-9))
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return out.round(2)
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return (table("decision_hour"), table(["decision_hour", "direction"]),
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table("entry_hour"))
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if __name__ == "__main__":
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book, mins = sys.argv[1], int(sys.argv[2])
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by_dec, by_dec_dir, by_ent = hour_tables(book, mins)
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print(f"\n── {book} · by DECISION hour (bar-close clock) ──")
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print(by_dec.to_string())
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print(f"\n── by (decision hour, direction) ──")
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print(by_dec_dir.to_string())
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