Big-candle gate (default-OFF) + M5 relaxation campaign + --set tooling
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. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014jqHpmNYmwYMowF6bPZyeD
This commit is contained in:
Harwinder Sekhon
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"""Trade-book diff: relaxed/gated run vs its control.
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Usage:
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python backtest/research/book_diff.py CONTROL.csv CANDIDATE.csv BAR_MINUTES
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Decision numbers are RUN-LEVEL (ΔtotalR, ΔPF, ΔmaxDD, Δtrades) — never
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the added-trade bucket alone: the 5-bar entry cooldown means new
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entries displace existing ones. The added/removed decomposition and the
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per-decision-hour histogram of added trades are diagnostics.
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Shifted trades = removed+added pairs in the same direction within
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±5 bars (cooldown displacement, not a genuinely new/lost signal).
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"""
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import sys
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import pandas as pd
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def _metrics(df):
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r = df["r_multiple"]
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gains, losses = r[r > 0].sum(), -r[r <= 0].sum()
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cum = r.cumsum()
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return dict(trades=len(df), total_r=r.sum(),
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pf=gains / max(losses, 1e-9),
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wr=(r > 0).mean() * 100 if len(df) else 0.0,
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max_dd=(cum.cummax() - cum).max() if len(cum) else 0.0)
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def diff_books(control_csv, candidate_csv, bar_minutes, split="2026-01-29"):
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ctrl, cand = pd.read_csv(control_csv), pd.read_csv(candidate_csv)
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for df in (ctrl, cand):
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df["_t"] = pd.to_datetime(df["entry_time"], utc=True, format="mixed")
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df["_key"] = df["entry_time"].astype(str) + "|" + df["direction"]
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added = cand[~cand["_key"].isin(ctrl["_key"])].copy()
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removed = ctrl[~ctrl["_key"].isin(cand["_key"])].copy()
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# cooldown displacement: same-direction removed/added within ±5 bars
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tol = pd.Timedelta(minutes=5 * bar_minutes)
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shifted = 0
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rem_pool = removed.copy()
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for _, a in added.iterrows():
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m = rem_pool[(rem_pool["direction"] == a["direction"])
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& ((rem_pool["_t"] - a["_t"]).abs() <= tol)]
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if len(m):
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shifted += 1
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rem_pool = rem_pool.drop(m.index[0])
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mc, mk = _metrics(ctrl), _metrics(cand)
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split_ts = pd.Timestamp(split, tz="UTC")
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halves = (cand[cand["_t"] < split_ts]["r_multiple"].sum(),
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cand[cand["_t"] >= split_ts]["r_multiple"].sum())
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hour_hist = None
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if len(added):
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added["dec_hour"] = (added["_t"]
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- pd.Timedelta(minutes=bar_minutes)).dt.hour
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hour_hist = added.groupby("dec_hour")["r_multiple"] \
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.agg(["count", "sum"]).round(2)
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return dict(
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control=mc, candidate=mk,
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delta_r=mk["total_r"] - mc["total_r"],
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delta_pf=mk["pf"] - mc["pf"],
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delta_dd=mk["max_dd"] - mc["max_dd"],
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delta_trades=mk["trades"] - mc["trades"],
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added_n=len(added), added_r=added["r_multiple"].sum(),
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removed_n=len(removed), removed_r=removed["r_multiple"].sum(),
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shifted=shifted, halves=halves, added_by_hour=hour_hist,
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)
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if __name__ == "__main__":
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d = diff_books(sys.argv[1], sys.argv[2], int(sys.argv[3]))
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c, k = d["control"], d["candidate"]
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print(f"control : {c['trades']:4d} trades {c['total_r']:+7.1f}R "
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f"PF {c['pf']:.2f} WR {c['wr']:.1f}% DD {c['max_dd']:.1f}")
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print(f"candidate: {k['trades']:4d} trades {k['total_r']:+7.1f}R "
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f"PF {k['pf']:.2f} WR {k['wr']:.1f}% DD {k['max_dd']:.1f}")
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print(f"ΔR {d['delta_r']:+.1f} ΔPF {d['delta_pf']:+.2f} "
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f"ΔDD {d['delta_dd']:+.1f} Δtrades {d['delta_trades']:+d}")
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print(f"added {d['added_n']} ({d['added_r']:+.1f}R) "
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f"removed {d['removed_n']} ({d['removed_r']:+.1f}R) "
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f"shifted≈{d['shifted']}")
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print(f"candidate halves: H1 {d['halves'][0]:+.1f}R H2 {d['halves'][1]:+.1f}R")
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if d["added_by_hour"] is not None:
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print("\nadded trades by decision hour:")
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print(d["added_by_hour"].to_string())
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"""Burst-candle frequency by UTC hour — the empirical "hedge fund hours".
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Usage:
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python backtest/research/burst_profile.py PARQUET_GLOB
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Per UTC bar-close hour of the given bars: % of bars whose range is
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>= K x the PRIOR bar's Wilder ATR(14), for K in {3,4,5}; mean bar
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range; and mean signed drift over the 6 bars following a >=4x burst
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(does price continue or snap back?). Analysis tooling only.
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"""
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import glob
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import sys
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import pandas as pd
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def burst_table(parquet_glob: str, bar_minutes: int = 5):
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files = sorted(glob.glob(parquet_glob))
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if not files:
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raise SystemExit(f"no parquet matches {parquet_glob}")
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df = pd.concat([pd.read_parquet(f) for f in files]).sort_index()
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df = df[~df.index.duplicated(keep="last")]
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h, l, c = df["High"], df["Low"], df["Close"]
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tr = pd.concat([h - l, (h - c.shift()).abs(), (l - c.shift()).abs()],
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axis=1).max(axis=1)
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atr = tr.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()
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ratio = (h - l) / atr.shift(1)
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close_hour = (df.index + pd.Timedelta(minutes=bar_minutes)).hour
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fwd6 = c.shift(-6) - c # signed move over the 6 bars after this one
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out = pd.DataFrame({
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"bars": ratio.groupby(close_hour).size(),
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**{f"pct_ge_{k}x": (ratio >= k).groupby(close_hour).mean() * 100
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for k in (3, 4, 5)},
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"mean_range": (h - l).groupby(close_hour).mean(),
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"post4x_drift6": fwd6[ratio >= 4].groupby(close_hour[ratio >= 4]).mean(),
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})
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return out.round(3)
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if __name__ == "__main__":
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print(burst_table(sys.argv[1],
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int(sys.argv[2]) if len(sys.argv) > 2 else 5).to_string())
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"""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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