finish experiment 57 (exp/57-signal-quality-gate-gate-trades-based-on)

book/data/signal_quality_gate/signal_quality_gate_results.csv

@@ -0,0 +1,46 @@
+window,gate,start,end,trade_dates,gate_open,gate_closed,trip_rate,base_ann,base_sharpe,base_maxDD,gated_ann,gated_sharpe,gated_maxDD
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book/data/signal_quality_gate/signal_quality_gate_results.json

@@ -0,0 +1,722 @@
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+  {
+    "window": "2024",
+    "gate": "hitrate_10d_0.70",
+    "start": "2024-01-02",
+    "end": "2024-12-31",
+    "trade_dates": 253,
+    "gate_open": 7,
+    "gate_closed": 246,
+    "trip_rate": 0.9723,
+    "base_ann": 0.08229,
+    "base_sharpe": 0.5594,
+    "base_maxDD": -0.10685,
+    "gated_ann": 0.031785,
+    "gated_sharpe": 1.7408,
+    "gated_maxDD": -0.002083
+  },
+  {
+    "window": "2024",
+    "gate": "hitrate_20d_0.40",
+    "start": "2024-01-02",
+    "end": "2024-12-31",
+    "trade_dates": 253,
+    "gate_open": 247,
+    "gate_closed": 6,
+    "trip_rate": 0.0237,
+    "base_ann": 0.08229,
+    "base_sharpe": 0.5594,
+    "base_maxDD": -0.10685,
+    "gated_ann": 0.078007,
+    "gated_sharpe": 0.5342,
+    "gated_maxDD": -0.10685
+  },
+  {
+    "window": "2024",
+    "gate": "hitrate_20d_0.50",
+    "start": "2024-01-02",
+    "end": "2024-12-31",
+    "trade_dates": 253,
+    "gate_open": 177,
+    "gate_closed": 76,
+    "trip_rate": 0.3004,
+    "base_ann": 0.08229,
+    "base_sharpe": 0.5594,
+    "base_maxDD": -0.10685,
+    "gated_ann": 0.210672,
+    "gated_sharpe": 1.8469,
+    "gated_maxDD": -0.056207
+  },
+  {
+    "window": "2024",
+    "gate": "hitrate_20d_0.60",
+    "start": "2024-01-02",
+    "end": "2024-12-31",
+    "trade_dates": 253,
+    "gate_open": 13,
+    "gate_closed": 240,
+    "trip_rate": 0.9486,
+    "base_ann": 0.08229,
+    "base_sharpe": 0.5594,
+    "base_maxDD": -0.10685,
+    "gated_ann": 0.005953,
+    "gated_sharpe": 0.3039,
+    "gated_maxDD": -0.014443
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_5d_0.50",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 139,
+    "gate_closed": 111,
+    "trip_rate": 0.444,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.546654,
+    "gated_sharpe": 4.2124,
+    "gated_maxDD": -0.035676
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_5d_0.60",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 79,
+    "gate_closed": 171,
+    "trip_rate": 0.684,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.556291,
+    "gated_sharpe": 5.1026,
+    "gated_maxDD": -0.025449
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_5d_0.70",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 34,
+    "gate_closed": 216,
+    "trip_rate": 0.864,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.404269,
+    "gated_sharpe": 4.4477,
+    "gated_maxDD": -0.013842
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_10d_0.50",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 148,
+    "gate_closed": 102,
+    "trip_rate": 0.408,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.459869,
+    "gated_sharpe": 3.5211,
+    "gated_maxDD": -0.046921
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_10d_0.60",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 62,
+    "gate_closed": 188,
+    "trip_rate": 0.752,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.368232,
+    "gated_sharpe": 4.0148,
+    "gated_maxDD": -0.022983
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_10d_0.70",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 13,
+    "gate_closed": 237,
+    "trip_rate": 0.948,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.091481,
+    "gated_sharpe": 2.1324,
+    "gated_maxDD": -0.010866
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_20d_0.40",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 237,
+    "gate_closed": 13,
+    "trip_rate": 0.052,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.06639,
+    "gated_sharpe": 0.3895,
+    "gated_maxDD": -0.146704
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_20d_0.50",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 149,
+    "gate_closed": 101,
+    "trip_rate": 0.404,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.366016,
+    "gated_sharpe": 2.5969,
+    "gated_maxDD": -0.063998
+  },
+  {
+    "window": "2023",
+    "gate": "hitrate_20d_0.60",
+    "start": "2023-01-03",
+    "end": "2023-12-29",
+    "trade_dates": 250,
+    "gate_open": 40,
+    "gate_closed": 210,
+    "trip_rate": 0.84,
+    "base_ann": -0.047644,
+    "base_sharpe": -0.2738,
+    "base_maxDD": -0.197856,
+    "gated_ann": 0.149844,
+    "gated_sharpe": 2.2645,
+    "gated_maxDD": -0.032267
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_5d_0.50",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 145,
+    "gate_closed": 107,
+    "trip_rate": 0.4246,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.556615,
+    "gated_sharpe": 5.9167,
+    "gated_maxDD": -0.028062
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_5d_0.60",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 68,
+    "gate_closed": 184,
+    "trip_rate": 0.7302,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.351867,
+    "gated_sharpe": 5.8468,
+    "gated_maxDD": -0.011638
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_5d_0.70",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 26,
+    "gate_closed": 226,
+    "trip_rate": 0.8968,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.148417,
+    "gated_sharpe": 3.9593,
+    "gated_maxDD": -0.0041
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_10d_0.50",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 163,
+    "gate_closed": 89,
+    "trip_rate": 0.3532,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.465023,
+    "gated_sharpe": 4.2342,
+    "gated_maxDD": -0.040569
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_10d_0.60",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 51,
+    "gate_closed": 201,
+    "trip_rate": 0.7976,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.21802,
+    "gated_sharpe": 4.7978,
+    "gated_maxDD": -0.011134
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_10d_0.70",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 13,
+    "gate_closed": 239,
+    "trip_rate": 0.9484,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.06108,
+    "gated_sharpe": 2.7055,
+    "gated_maxDD": -0.000262
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_20d_0.40",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 252,
+    "gate_closed": 0,
+    "trip_rate": 0.0,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.18367,
+    "gated_sharpe": 1.0979,
+    "gated_maxDD": -0.102651
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_20d_0.50",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 166,
+    "gate_closed": 86,
+    "trip_rate": 0.3413,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.289756,
+    "gated_sharpe": 2.5787,
+    "gated_maxDD": -0.049244
+  },
+  {
+    "window": "2021",
+    "gate": "hitrate_20d_0.60",
+    "start": "2021-01-04",
+    "end": "2021-12-31",
+    "trade_dates": 252,
+    "gate_open": 38,
+    "gate_closed": 214,
+    "trip_rate": 0.8492,
+    "base_ann": 0.18367,
+    "base_sharpe": 1.0979,
+    "base_maxDD": -0.102651,
+    "gated_ann": 0.104685,
+    "gated_sharpe": 2.394,
+    "gated_maxDD": -0.015084
+  }
+]

book/scripts/signal_quality_gate_bt.py

@@ -0,0 +1,293 @@
+"""Signal-quality gate walk-forward backtest.
+
+Gates trades based on whether the model's recent topk predictions were correct
+(hit rate). This is a retrospective gate — it measures prediction accuracy,
+not market state.
+
+Usage:
+    cd /app && .venv/bin/python book/scripts/signal_quality_gate_bt.py
+"""
+
+from __future__ import annotations
+
+import json
+import pathlib
+import sys
+import time
+
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+MARKET = "US"
+OUT_DIR = pathlib.Path("/app/experiments/book/data/signal_quality_gate")
+
+WINDOWS = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
+]
+
+# Signal-quality gate configs: (lookback_days, threshold, name)
+SIGNAL_GATE_CONFIGS = [
+    (5, 0.50, "hitrate_5d_0.50"),
+    (5, 0.60, "hitrate_5d_0.60"),
+    (5, 0.70, "hitrate_5d_0.70"),
+    (10, 0.50, "hitrate_10d_0.50"),
+    (10, 0.60, "hitrate_10d_0.60"),
+    (10, 0.70, "hitrate_10d_0.70"),
+    (20, 0.40, "hitrate_20d_0.40"),
+    (20, 0.50, "hitrate_20d_0.50"),
+    (20, 0.60, "hitrate_20d_0.60"),
+]
+
+
+def load_pred(path: str) -> pd.Series:
+    df = pd.read_pickle(path)
+    if isinstance(df, pd.DataFrame):
+        if "score" in df.columns:
+            s = df["score"]
+        else:
+            s = df.iloc[:, 0]
+    else:
+        s = df
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
+    return s
+
+
+def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
+    sp = cfg.lake_root / "symbols.parquet"
+    if sp.exists():
+        syms = pd.read_parquet(sp)
+        col = "symbol" if "symbol" in syms.columns else syms.columns[0]
+        symbols = sorted(syms[col].astype(str).str.upper().tolist())
+    else:
+        return pd.DataFrame()
+    closes = {}
+    for sym in symbols:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists():
+            continue
+        try:
+            df = pd.read_parquet(p)
+        except Exception:
+            continue
+        if not len(df):
+            continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup_start:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def compute_hit_rate_series(
+    pred: pd.Series, ret_df: pd.DataFrame, topk: int = 10, lookback: int = 10,
+) -> pd.Series:
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx.unique())
+    hit_rates = {}
+    for i in range(1, len(trade_dates)):
+        prev_date = trade_dates[i - 1]
+        curr_date = trade_dates[i]
+        try:
+            prev_scores = pred.loc[prev_date]
+        except KeyError:
+            continue
+        if isinstance(prev_scores, pd.DataFrame):
+            prev_scores = prev_scores.iloc[:, 0]
+        prev_scores = prev_scores.dropna().sort_values(ascending=False)
+        topk_syms = list(prev_scores.index[:topk])
+        if curr_date not in ret_df.index:
+            continue
+        today_ret = ret_df.loc[curr_date]
+        topk_rets = today_ret.reindex(topk_syms).dropna()
+        if len(topk_rets) > 0:
+            hit_rate = (topk_rets > 0).mean()
+            hit_rates[curr_date] = hit_rate
+    hit_series = pd.Series(hit_rates)
+    if len(hit_series) == 0:
+        return hit_series
+    rolling_hr = hit_series.rolling(lookback, min_periods=max(1, lookback // 2)).mean()
+    return rolling_hr
+
+
+def run_backtest(pred, hit_rate, close_df, start, end, topk=10, threshold=0.5):
+    if not isinstance(pred.index, pd.MultiIndex):
+        return {"error": "pred must have MultiIndex"}
+    ret_df = close_df.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    window_mask = (dt_idx >= pd.Timestamp(start)) & (dt_idx <= pd.Timestamp(end))
+    window_pred = pred.loc[window_mask]
+    if len(window_pred) == 0:
+        return {"error": "no pred data in window"}
+    trade_dates = sorted(dt_idx[window_mask].unique())
+    gate_open = {}
+    for d in trade_dates:
+        known = hit_rate[hit_rate.index <= d]
+        if len(known) > 0 and not pd.isna(known.iloc[-1]):
+            gate_open[d] = bool(known.iloc[-1] >= threshold)
+        else:
+            gate_open[d] = True
+    n_total = len(trade_dates)
+    n_open = sum(1 for v in gate_open.values() if v)
+    n_closed = n_total - n_open
+    holdings_base = []
+    holdings_gated = []
+    equity_gated = 1_000_000.0
+    equity_base = 1_000_000.0
+    prev_week = None
+    prev_scores = None
+    daily_gated = []
+    daily_base = []
+    ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
+    for d in trade_dates:
+        try:
+            day_scores = window_pred.loc[d]
+        except KeyError:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        if len(day_scores) == 0:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+        ret_row = ret_by_date.get(d)
+        if ret_row is None:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = day_scores
+            continue
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
+        gate_val = gate_open.get(d, True)
+        if cur_week != prev_week or not holdings_base:
+            if prev_scores is not None:
+                holdings_base = list(prev_scores.index[:topk])
+        if holdings_base:
+            base_rets = ret_row.reindex(holdings_base).dropna()
+            if len(base_rets) > 0:
+                equity_base *= (1 + base_rets.mean())
+        if gate_val:
+            if cur_week != prev_week or not holdings_gated:
+                if prev_scores is not None:
+                    holdings_gated = list(prev_scores.index[:topk])
+            if holdings_gated:
+                hold_rets = ret_row.reindex(holdings_gated).dropna()
+                if len(hold_rets) > 0:
+                    equity_gated *= (1 + hold_rets.mean())
+        else:
+            holdings_gated = []
+        prev_week = cur_week
+        prev_scores = day_scores
+        daily_gated.append(equity_gated)
+        daily_base.append(equity_base)
+    g_series = pd.Series(daily_gated, index=trade_dates)
+    b_series = pd.Series(daily_base, index=trade_dates)
+    def _metrics(eq):
+        if len(eq) < 2:
+            return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
+        rets = eq.pct_change().dropna()
+        ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+        vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+        sharpe = ann_ret / vol if vol > 0 else 0
+        peak = eq.cummax()
+        dd = (eq - peak) / peak
+        maxDD = float(dd.min())
+        return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
+    base_m = _metrics(b_series)
+    gated_m = _metrics(g_series)
+    return {
+        "trade_dates": n_total,
+        "gate_open_days": n_open,
+        "gate_closed_days": n_closed,
+        "trip_rate": round(n_closed / n_total, 4) if n_total else 0,
+        "base": base_m,
+        "gated": gated_m,
+    }
+
+
+def main():
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    full_start = "2015-01-03"
+    full_end = "2026-08-19"
+    print("Loading lake bars...")
+    close_df = load_bars_for_window(full_start, full_end)
+    print(f"  {close_df.shape[1]} symbols, {close_df.shape[0]} days")
+    ret_df = close_df.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    results = []
+    for window in WINDOWS:
+        wl, ws, we = window["label"], window["start"], window["end"]
+        pred_path = window["pred"]
+        print(f"\n=== Window {wl} ({ws} to {we}) ===")
+        pred = load_pred(pred_path)
+        print(f"  pred shape: {pred.shape}")
+        hit_rates = {}
+        for lookback, _, name in SIGNAL_GATE_CONFIGS:
+            if lookback not in hit_rates:
+                hr = compute_hit_rate_series(pred, ret_df, topk=10, lookback=lookback)
+                hit_rates[lookback] = hr
+                print(f"  lookback={lookback}: {len(hr)} days with hit rates")
+        for lookback, threshold, name in SIGNAL_GATE_CONFIGS:
+            hr = hit_rates[lookback]
+            bt = run_backtest(pred, hr, close_df, ws, we, topk=10, threshold=threshold)
+            if "error" in bt:
+                print(f"  {name}: {bt['error']}")
+                continue
+            row = {
+                "window": wl,
+                "gate": name,
+                "start": ws,
+                "end": we,
+                "trade_dates": bt["trade_dates"],
+                "gate_open": bt["gate_open_days"],
+                "gate_closed": bt["gate_closed_days"],
+                "trip_rate": bt["trip_rate"],
+                "base_ann": bt["base"]["ann_return"],
+                "base_sharpe": bt["base"]["sharpe"],
+                "base_maxDD": bt["base"]["maxDD"],
+                "gated_ann": bt["gated"]["ann_return"],
+                "gated_sharpe": bt["gated"]["sharpe"],
+                "gated_maxDD": bt["gated"]["maxDD"],
+            }
+            results.append(row)
+            print(f"  {name}: trip={bt['trip_rate']:.1%}, "
+                  f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
+                  f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f})")
+    df = pd.DataFrame(results)
+    out_path = OUT_DIR / "signal_quality_gate_results.csv"
+    df.to_csv(out_path, index=False)
+    with open(OUT_DIR / "signal_quality_gate_results.json", "w") as f:
+        json.dump(df.to_dict(orient="records"), f, indent=2, default=str)
+    print(f"\nSaved to {out_path}")
+    print("\n=== Summary: Gated Return by Window ===")
+    for gate_name in df["gate"].unique():
+        gdf = df[df["gate"] == gate_name]
+        print(f"\n{gate_name}:")
+        for _, r in gdf.iterrows():
+            print(f"  {r['window']}: base={r['base_ann']:+.1%}, gated={r['gated_ann']:+.1%}, "
+                  f"trip={r['trip_rate']:.0%}, diff={r['gated_ann']-r['base_ann']:+.1%}pp")
+
+
+if __name__ == "__main__":
+    main()