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

backtest/research/burst_profile.py

@@ -0,0 +1,42 @@
+"""Burst-candle frequency by UTC hour — the empirical "hedge fund hours".
+
+Usage:
+    python backtest/research/burst_profile.py PARQUET_GLOB
+
+Per UTC bar-close hour of the given bars: % of bars whose range is
+>= K x the PRIOR bar's Wilder ATR(14), for K in {3,4,5}; mean bar
+range; and mean signed drift over the 6 bars following a >=4x burst
+(does price continue or snap back?). Analysis tooling only.
+"""
+import glob
+import sys
+
+import pandas as pd
+
+
+def burst_table(parquet_glob: str, bar_minutes: int = 5):
+    files = sorted(glob.glob(parquet_glob))
+    if not files:
+        raise SystemExit(f"no parquet matches {parquet_glob}")
+    df = pd.concat([pd.read_parquet(f) for f in files]).sort_index()
+    df = df[~df.index.duplicated(keep="last")]
+    h, l, c = df["High"], df["Low"], df["Close"]
+    tr = pd.concat([h - l, (h - c.shift()).abs(), (l - c.shift()).abs()],
+                   axis=1).max(axis=1)
+    atr = tr.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()
+    ratio = (h - l) / atr.shift(1)
+    close_hour = (df.index + pd.Timedelta(minutes=bar_minutes)).hour
+    fwd6 = c.shift(-6) - c  # signed move over the 6 bars after this one
+    out = pd.DataFrame({
+        "bars": ratio.groupby(close_hour).size(),
+        **{f"pct_ge_{k}x": (ratio >= k).groupby(close_hour).mean() * 100
+           for k in (3, 4, 5)},
+        "mean_range": (h - l).groupby(close_hour).mean(),
+        "post4x_drift6": fwd6[ratio >= 4].groupby(close_hour[ratio >= 4]).mean(),
+    })
+    return out.round(3)
+
+
+if __name__ == "__main__":
+    print(burst_table(sys.argv[1],
+                      int(sys.argv[2]) if len(sys.argv) > 2 else 5).to_string())