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. Claude-Session: https://claude.ai/code/session_014jqHpmNYmwYMowF6bPZyeD
2026-07-30 11:30:20 -04:00
parent dc7beb2877
commit 042b079861
9 changed files with 621 additions and 36 deletions
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+"""Trade-book diff: relaxed/gated run vs its control.
+
+Usage:
+    python backtest/research/book_diff.py CONTROL.csv CANDIDATE.csv BAR_MINUTES
+
+Decision numbers are RUN-LEVEL (ΔtotalR, ΔPF, ΔmaxDD, Δtrades) — never
+the added-trade bucket alone: the 5-bar entry cooldown means new
+entries displace existing ones. The added/removed decomposition and the
+per-decision-hour histogram of added trades are diagnostics.
+
+Shifted trades = removed+added pairs in the same direction within
+±5 bars (cooldown displacement, not a genuinely new/lost signal).
+"""
+import sys
+
+import pandas as pd
+
+
+def _metrics(df):
+    r = df["r_multiple"]
+    gains, losses = r[r > 0].sum(), -r[r <= 0].sum()
+    cum = r.cumsum()
+    return dict(trades=len(df), total_r=r.sum(),
+                pf=gains / max(losses, 1e-9),
+                wr=(r > 0).mean() * 100 if len(df) else 0.0,
+                max_dd=(cum.cummax() - cum).max() if len(cum) else 0.0)
+
+
+def diff_books(control_csv, candidate_csv, bar_minutes, split="2026-01-29"):
+    ctrl, cand = pd.read_csv(control_csv), pd.read_csv(candidate_csv)
+    for df in (ctrl, cand):
+        df["_t"] = pd.to_datetime(df["entry_time"], utc=True, format="mixed")
+        df["_key"] = df["entry_time"].astype(str) + "|" + df["direction"]
+
+    added = cand[~cand["_key"].isin(ctrl["_key"])].copy()
+    removed = ctrl[~ctrl["_key"].isin(cand["_key"])].copy()
+
+    # cooldown displacement: same-direction removed/added within ±5 bars
+    tol = pd.Timedelta(minutes=5 * bar_minutes)
+    shifted = 0
+    rem_pool = removed.copy()
+    for _, a in added.iterrows():
+        m = rem_pool[(rem_pool["direction"] == a["direction"])
+                     & ((rem_pool["_t"] - a["_t"]).abs() <= tol)]
+        if len(m):
+            shifted += 1
+            rem_pool = rem_pool.drop(m.index[0])
+
+    mc, mk = _metrics(ctrl), _metrics(cand)
+    split_ts = pd.Timestamp(split, tz="UTC")
+    halves = (cand[cand["_t"] < split_ts]["r_multiple"].sum(),
+              cand[cand["_t"] >= split_ts]["r_multiple"].sum())
+    hour_hist = None
+    if len(added):
+        added["dec_hour"] = (added["_t"]
+                             - pd.Timedelta(minutes=bar_minutes)).dt.hour
+        hour_hist = added.groupby("dec_hour")["r_multiple"] \
+                         .agg(["count", "sum"]).round(2)
+    return dict(
+        control=mc, candidate=mk,
+        delta_r=mk["total_r"] - mc["total_r"],
+        delta_pf=mk["pf"] - mc["pf"],
+        delta_dd=mk["max_dd"] - mc["max_dd"],
+        delta_trades=mk["trades"] - mc["trades"],
+        added_n=len(added), added_r=added["r_multiple"].sum(),
+        removed_n=len(removed), removed_r=removed["r_multiple"].sum(),
+        shifted=shifted, halves=halves, added_by_hour=hour_hist,
+    )
+
+
+if __name__ == "__main__":
+    d = diff_books(sys.argv[1], sys.argv[2], int(sys.argv[3]))
+    c, k = d["control"], d["candidate"]
+    print(f"control  : {c['trades']:4d} trades  {c['total_r']:+7.1f}R  "
+          f"PF {c['pf']:.2f}  WR {c['wr']:.1f}%  DD {c['max_dd']:.1f}")
+    print(f"candidate: {k['trades']:4d} trades  {k['total_r']:+7.1f}R  "
+          f"PF {k['pf']:.2f}  WR {k['wr']:.1f}%  DD {k['max_dd']:.1f}")
+    print(f"ΔR {d['delta_r']:+.1f}  ΔPF {d['delta_pf']:+.2f}  "
+          f"ΔDD {d['delta_dd']:+.1f}  Δtrades {d['delta_trades']:+d}")
+    print(f"added {d['added_n']} ({d['added_r']:+.1f}R)  "
+          f"removed {d['removed_n']} ({d['removed_r']:+.1f}R)  "
+          f"shifted≈{d['shifted']}")
+    print(f"candidate halves: H1 {d['halves'][0]:+.1f}R  H2 {d['halves'][1]:+.1f}R")
+    if d["added_by_hour"] is not None:
+        print("\nadded trades by decision hour:")
+        print(d["added_by_hour"].to_string())