finish experiment 56 (exp/56-window-staleness-isolation-the-m2-sharpe)

start experiment 56 (exp/56-window-staleness-isolation-the-m2-sharpe)
finish experiment 55 (exp/55-adaptive-short-window-retrain-test-the-4)
2026-08-20 11:42:18 +00:00 · 2026-08-20 11:36:19 +00:00 · 2026-08-20 11:35:04 +00:00 · 2026-08-20 11:16:30 +00:00 · 2026-08-20 11:10:05 +00:00 · 2026-08-20 10:19:52 +00:00
46 changed files with 2626 additions and 918 deletions
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-"""Precompute the signal-quality gate series and save to pickle.
-
-Usage:
-    python precompute_signal_quality_gate.py <pred_path> <output_path> [topk] [lookback] [threshold]
-
-Example:
-    python precompute_signal_quality_gate.py \
-        /home/data/lake/mlruns/49/34165f27e4a34378ad54843a079a78c0/artifacts/pred.pkl \
-        /app/experiments/book/data/signal_quality_gate/sq_gate_5d_0.50.pkl \
-        10 5 0.5
-"""
-import sys
-import pickle
-from pathlib import Path
-
-# Add tac-qlib to path
-sys.path.insert(0, "/app/tac-qlib")
-
-from tac_qlib.contrib.strategy.signal_quality_gate import compute_signal_quality_gate
-
-if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print(__doc__)
-        sys.exit(1)
-
-    pred_path = sys.argv[1]
-    output_path = sys.argv[2]
-    topk = int(sys.argv[3]) if len(sys.argv) > 3 else 10
-    lookback = int(sys.argv[4]) if len(sys.argv) > 4 else 5
-    threshold = float(sys.argv[5]) if len(sys.argv) > 5 else 0.5
-
-    lake_root = "/home/data/lake"
-
-    print(f"Computing signal-quality gate: topk={topk}, lookback={lookback}, threshold={threshold}")
-    gate = compute_signal_quality_gate(
-        pred_path,
-        lake_root=lake_root,
-        topk=topk,
-        lookback=lookback,
-        threshold=threshold,
-    )
-
-    print(f"Gate: {gate.sum()}/{len(gate)} days open ({gate.mean():.1%})")
-
-    with open(output_path, "wb") as f:
-        pickle.dump(gate, f)
-    print(f"Saved to {output_path}")
@@ -1,115 +0,0 @@
-# Signal-quality gate backtest for 2021
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-{# Gate computed on-the-fly from signal + lake close prices #}
-
-qlib_init:
-    provider_uri: "{{ LAKE }}"
-    region: us
-    expression_cache: null
-    dataset_cache: null
-    calendar_provider:
-        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    instrument_provider:
-        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
-    feature_provider:
-        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    exp_manager:
-        class: MLflowExpManager
-        module_path: qlib.workflow.expm
-        kwargs:
-            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-sq-gate-2021"
-
-task:
-    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
-        kwargs:
-            loss: mse
-            learning_rate: 0.05
-            num_leaves: 15
-            n_estimators: 200
-            colsample_bytree: 0.8
-            subsample: 0.8
-            subsample_freq: 1
-            reg_alpha: 0.01
-            reg_lambda: 0.01
-
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: TACHandler
-                module_path: tac_qlib.contrib.data.handler
-                kwargs:
-                    instruments: "{{ UNIVERSE }}"
-                    start_time: 2015-01-03
-                    end_time: 2021-12-31
-                    fit_start_time: 2015-01-03
-                    fit_end_time: 2021-01-03
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
-                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
-            segments:
-                train: [2015-01-03, 2020-09-01]
-                valid: [2020-09-03, 2021-01-03]
-                test: [2021-01-04, 2021-12-31]
-
-    record:
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: {}
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: { ana_long_short: true, ann_scaler: 252 }
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: SignalQualityGateStrategy
-                      module_path: tac_qlib.contrib.strategy.signal_quality_gate
-                      kwargs:
-                          signal: "<PRED>"
-                          lake_root: "{{ LAKE }}"
-                          gate_topk: 10
-                          gate_lookback: 5
-                          gate_threshold: 0.5
-                          gate_start: "2015-01-03"
-                          gate_end: "2021-12-31"
-                          topk: 10
-                          n_drop: 1
-                          only_tradable: true
-                          risk_degree: 0.95
-                  backtest:
-                      start_time: 2021-01-04
-                      end_time: 2021-12-31
-                      account: 1000000
-                      benchmark: SPY
-                      exchange_kwargs:
-                          codes: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d
@@ -1,115 +0,0 @@
-# Signal-quality gate backtest for 2024
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-{# Gate computed on-the-fly from signal + lake close prices #}
-
-qlib_init:
-    provider_uri: "{{ LAKE }}"
-    region: us
-    expression_cache: null
-    dataset_cache: null
-    calendar_provider:
-        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    instrument_provider:
-        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
-    feature_provider:
-        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    exp_manager:
-        class: MLflowExpManager
-        module_path: qlib.workflow.expm
-        kwargs:
-            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-sq-gate-2024"
-
-task:
-    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
-        kwargs:
-            loss: mse
-            learning_rate: 0.05
-            num_leaves: 15
-            n_estimators: 200
-            colsample_bytree: 0.8
-            subsample: 0.8
-            subsample_freq: 1
-            reg_alpha: 0.01
-            reg_lambda: 0.01
-
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: TACHandler
-                module_path: tac_qlib.contrib.data.handler
-                kwargs:
-                    instruments: "{{ UNIVERSE }}"
-                    start_time: 2015-01-03
-                    end_time: 2024-12-31
-                    fit_start_time: 2015-01-03
-                    fit_end_time: 2024-01-03
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
-                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
-            segments:
-                train: [2015-01-03, 2023-09-01]
-                valid: [2023-09-03, 2024-01-03]
-                test: [2024-01-02, 2024-12-31]
-
-    record:
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: {}
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: { ana_long_short: true, ann_scaler: 252 }
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: SignalQualityGateStrategy
-                      module_path: tac_qlib.contrib.strategy.signal_quality_gate
-                      kwargs:
-                          signal: "<PRED>"
-                          lake_root: "{{ LAKE }}"
-                          gate_topk: 10
-                          gate_lookback: 5
-                          gate_threshold: 0.5
-                          gate_start: "2015-01-03"
-                          gate_end: "2024-12-31"
-                          topk: 10
-                          n_drop: 1
-                          only_tradable: true
-                          risk_degree: 0.95
-                  backtest:
-                      start_time: 2024-01-02
-                      end_time: 2024-12-31
-                      account: 1000000
-                      benchmark: SPY
-                      exchange_kwargs:
-                          codes: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d
@@ -1,5 +1,5 @@
 # TradeAC custom-qlib-code snapshot (auto-generated)
-# parent repo HEAD : e952feed0a66a20439f4f24ad5524233429cd0c3
+# parent repo HEAD : f4f6e9291b1bdc69373d06710aa5f9aa396e900c
 #   tac-qlib/tac_qlib/contrib
 #   tac-qlib/tac_qlib/data
 # per-file hashes (git hash-object):
@@ -15,17 +15,11 @@
  74d0da348cbcc3700c96b6f4fe4391488e61efc5  tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
  d3f051f3a8650c42fedc7b367b966f7c74fb5789  tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
  d03e6611338918d4aac5eea4adf26f85a3763652  tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
-  2c2f167b693f4366a769998e3c9d4804f29e31e0  tac-qlib/tac_qlib/contrib/strategy/__init__.py
-  f9cd9ab729e3248542ccc490af7adc2e51c71914  tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
-  cd3133cfbd2556b25c106e39ae97fb128df0e326  tac-qlib/tac_qlib/contrib/strategy/__pycache__/ic_gate.cpython-312.pyc
+  b68a974a0bc87a279caed90cf2be4d7e78f9e59f  tac-qlib/tac_qlib/contrib/strategy/__init__.py
+  c29e45e562256bf786c36f91a971b097467276e9  tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
  6dd1c568a2961842793674390d5abffd1a0e71b8  tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
-  03b5e4d80da00800f1b108bee0735d3d18d856d1  tac-qlib/tac_qlib/contrib/strategy/__pycache__/regime_gate.cpython-312.pyc
-  a6a1c21ab71b62080830df45c4784b73c1531036  tac-qlib/tac_qlib/contrib/strategy/__pycache__/signal_quality_gate.cpython-312.pyc
-  755e3b139496a5e22b0328db45c8199c33029fbc  tac-qlib/tac_qlib/contrib/strategy/__pycache__/weekly_rebalance.cpython-312.pyc
  519a1f4c05dbe0ac018ab8b779eb33d53b4dd545  tac-qlib/tac_qlib/contrib/strategy/ic_gate.py
  79aaad9e39fcc740a773f4f63c512ce1086cfde0  tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
-  7bcee5f0b09cfa721440f1354f16f2dd9a112b12  tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
-  16ab80b731aab6d8bb818525615d77c2d1fcb0c8  tac-qlib/tac_qlib/contrib/strategy/signal_quality_gate.py
  fe60bacdfedd48617863be31f24b7c7daebfac5a  tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py
  92e6e90eb0cd0a25142034560f27adb6b705b1a8  tac-qlib/tac_qlib/data/__init__.py
  316bf4aa160cc8d15929ea648be03f4b4999667d  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
@@ -1,11 +1,5 @@
 from .ic_gate import ICGateTopkDropoutStrategy  # noqa: F401
 from .optimal_stop import OptimalStopControl  # noqa: F401
-from .regime_gate import RegimeGateTopkDropoutStrategy  # noqa: F401
 from .weekly_rebalance import WeeklyRebalanceDropoutStrategy  # noqa: F401

-__all__ = [
-    "ICGateTopkDropoutStrategy",
-    "OptimalStopControl",
-    "RegimeGateTopkDropoutStrategy",
-    "WeeklyRebalanceDropoutStrategy",
-]
+__all__ = ["ICGateTopkDropoutStrategy", "OptimalStopControl", "WeeklyRebalanceDropoutStrategy"]
@@ -1,215 +0,0 @@
-"""Regime-gate TopkDropout strategy.
-
-Subclass of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`` that
-holds the book (issues NO orders) while a regime detector says the market is in
-an unfavorable state.  When the gate is open it behaves exactly like the
-reference TopkDropoutStrategy.
-
-Three detector types are supported (all causal — no lookahead):
-
-* ``dispersion``: cross-sectional standard deviation of 22-day rolling returns
-  across the universe.  Gate closes when CS dispersion < threshold (low
-  dispersion means the spread between winners and losers is too narrow for
-  TopkDropout to exploit).
-* ``vol``: cross-sectional mean of 22-day rolling realized volatility.  Gate
-  closes when avg vol is outside a band ``[vol_low, vol_high]`` (strategy
-  needs moderate vol — too calm or too turbulent both hurt).
-* ``hmm``: pre-computed HMM posterior for regime 1 (``sp_hmm_p_regime1``).
-  Gate closes when posterior < threshold (model is not confident the calm
-  regime is active).
-
-The gate is provided as a precomputed ``pd.Series`` of booleans indexed by
-datetime (True = trade allowed).  The companion ``compute_regime_gate``
-function builds this series from lake bars; call it once before backtesting
-and pass the result as the ``regime_gate`` parameter.
-"""
-
-from __future__ import annotations
-
-import pandas as pd
-
-from qlib.backtest.decision import TradeDecisionWO
-from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
-
-__all__ = ["RegimeGateTopkDropoutStrategy", "compute_regime_gate"]
-
-
-class RegimeGateTopkDropoutStrategy(TopkDropoutStrategy):
-    """TopkDropout with a regime-gate circuit breaker.
-
-    Parameters
-    ----------
-    topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
-    forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
-    regime_gate : pd.Series — precomputed per-date gate (bool indexed by
-        datetime).  True = trade allowed, False = no orders.  Missing dates
-        default to open (trade allowed).
-    """
-
-    def __init__(self, *, regime_gate=None, **kwargs):
-        super().__init__(**kwargs)
-        self._regime_gate = regime_gate
-
-    def _gate_open(self, trade_start_time) -> bool:
-        if self._regime_gate is None:
-            return True
-        ts = pd.Timestamp(trade_start_time)
-        known = self._regime_gate[self._regime_gate.index <= ts]
-        if len(known):
-            return bool(known.iloc[-1])
-        return True  # default open if no history yet
-
-    def generate_trade_decision(self, execute_result=None):
-        trade_step = self.trade_calendar.get_trade_step()
-        trade_start_time, _ = self.trade_calendar.get_step_time(trade_step)
-        if not self._gate_open(trade_start_time):
-            return TradeDecisionWO([], self)
-        return super().generate_trade_decision(execute_result)
-
-
-# ---------------------------------------------------------------------------
-# Precomputation helper
-# ---------------------------------------------------------------------------
-
-def compute_regime_gate(
-    detector: str,
-    threshold: float = 0.0,
-    *,
-    lake_root: str = "",
-    market: str = "US",
-    start: str = "2015-01-03",
-    end: str = "2026-08-19",
-    vol_low: float = 0.0,
-    vol_high: float = 999.0,
-    hmm_field: str = "sp_hmm_p_regime1",
-) -> pd.Series:
-    """Build a per-date regime gate series from lake bars.
-
-    Parameters
-    ----------
-    detector : str — ``"dispersion"``, ``"vol"``, or ``"hmm"``.
-    threshold : float — for ``dispersion``: min CS dispersion to allow trading.
-        For ``hmm``: min HMM posterior to allow trading.
-        Ignored for ``vol`` (uses ``vol_low``/``vol_high`` band instead).
-    lake_root, market : str — lake location.
-    start, end : str — date window.
-    vol_low, vol_high : float — annualized vol band for the ``vol`` detector.
-    hmm_field : str — HMM feature column name for the ``hmm`` detector.
-
-    Returns
-    -------
-    pd.Series — bool, indexed by datetime.  True = trade allowed.
-    """
-    from tac_qlib.data.config import LakeConfig, resolve_lake_root
-
-    cfg = LakeConfig(resolve_lake_root(lake_root or None), market)
-    symbols = _universe_symbols(cfg)
-    close_df, vol_df = _load_daily_bars(symbols, cfg, start, end)
-    if close_df.empty:
-        return pd.Series(dtype=bool)
-
-    if detector == "dispersion":
-        return _dispersion_gate(close_df, threshold)
-    elif detector == "vol":
-        return _vol_gate(close_df, vol_low, vol_high)
-    elif detector == "hmm":
-        return _hmm_gate(cfg, symbols, threshold, start, end, hmm_field)
-    else:
-        raise ValueError(f"Unknown detector: {detector!r}")
-
-
-def _universe_symbols(cfg) -> list:
-    """Read symbols from the lake symbols.parquet."""
-    import pathlib
-
-    sp = cfg.lake_root / "symbols.parquet"
-    if sp.exists():
-        df = pd.read_parquet(sp)
-        col = "symbol" if "symbol" in df.columns else df.columns[0]
-        return sorted(df[col].astype(str).str.upper().tolist())
-    return []
-
-
-def _load_daily_bars(symbols, cfg, start, end):
-    """Load daily close prices for all symbols into a wide DataFrame."""
-    closes = {}
-    vols = {}
-    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()
-        df = df.loc[start:end]
-        if len(df) < 22:
-            continue
-        closes[sym] = df["c"]
-        if "v" in df.columns:
-            vols[sym] = df["v"]
-    close_df = pd.DataFrame(closes)
-    vol_df = pd.DataFrame(vols) if vols else None
-    return close_df, vol_df
-
-
-def _dispersion_gate(close_df, threshold):
-    """Cross-sectional dispersion of 22-day rolling returns."""
-    if close_df.empty or close_df.shape[1] < 2:
-        return pd.Series(dtype=bool)
-    ret = close_df.pct_change(22)
-    cs_disp = ret.std(axis=1)
-    gate = cs_disp >= threshold
-    gate.iloc[:22] = True  # warmup: allow trading
-    return gate
-
-
-def _vol_gate(close_df, vol_low, vol_high):
-    """Cross-sectional mean of 22-day rolling realized vol."""
-    if close_df.empty or close_df.shape[1] < 2:
-        return pd.Series(dtype=bool)
-    import numpy as np
-    log_ret = np.log(close_df / close_df.shift(1))
-    rv22 = log_ret.rolling(22).std() * (252 ** 0.5)
-    cs_mean_vol = rv22.mean(axis=1)
-    gate = (cs_mean_vol >= vol_low) & (cs_mean_vol <= vol_high)
-    gate.iloc[:22] = True  # warmup
-    return gate
-
-
-def _hmm_gate(cfg, symbols, threshold, start, end, hmm_field):
-    """HMM regime posterior gate from persisted SP features."""
-    feat_root = cfg.lake_root / "features"
-    all_posteriors = {}
-    for sym in symbols:
-        # check both ta and sp family paths
-        for family in ("sp", "ta"):
-            p = feat_root / f"market=US" / f"timeframe=1d" / f"family={family}" / f"symbol={sym}.parquet"
-            if not p.exists():
-                continue
-            try:
-                df = pd.read_parquet(p)
-            except Exception:
-                continue
-            if hmm_field not in df.columns:
-                continue
-            tcol = df["t"] if "t" in df.columns else df["date"]
-            ts = pd.to_datetime(tcol)
-            s = pd.Series(df[hmm_field].values, index=ts, name=sym)
-            s = s.loc[start:end].dropna()
-            if len(s) > 0:
-                all_posteriors[sym] = s
-                break
-    if not all_posteriors:
-        # no HMM features found — default open
-        idx = pd.date_range(start, end, freq="B")
-        return pd.Series(True, index=idx)
-    post_df = pd.DataFrame(all_posteriors)
-    cs_mean = post_df.mean(axis=1)
-    gate = cs_mean >= threshold
-    return gate
@@ -1,301 +0,0 @@
-"""Signal-quality gate TopkDropout strategy.
-
-Subclass of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`` that
-holds the book (issues NO orders) when the model's recent prediction accuracy
-is below a threshold.  When the gate is open it behaves exactly like the
-reference TopkDropoutStrategy.
-
-Unlike the regime gate (which asks "is the market calm?"), the signal-quality
-gate asks "are my predictions accurate?" — and works across ALL years.
-
-The gate is provided as a precomputed ``pd.Series`` of booleans indexed by
-datetime (True = trade allowed).  The companion ``compute_signal_quality_gate``
-function builds this series from a pred.pkl and lake bars; call it once before
-backtesting and pass the result as the ``signal_quality_gate`` parameter.
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from qlib.backtest.decision import TradeDecisionWO
-from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
-
-__all__ = ["SignalQualityGateStrategy", "compute_signal_quality_gate"]
-
-
-class SignalQualityGateStrategy(TopkDropoutStrategy):
-    """TopkDropout with signal-quality gate overlay.
-
-    When ``lake_root`` is provided the gate is computed on-the-fly from the
-    signal (``<PRED>``) and close prices — no precomputed gate file needed.
-    This ensures the gate matches the model that is actually generating the
-    predictions (critical when the model is retrained each year).
-
-    Parameters
-    ----------
-    topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
-    forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
-    signal_quality_gate : pd.Series — precomputed per-date gate (bool).
-    signal_quality_gate_path : str — path to pickled gate Series.
-    lake_root : str — lake root for on-the-fly gate computation (preferred).
-    gate_topk, gate_lookback, gate_threshold : int/float — gate params.
-    gate_start, gate_end : str — date window for loading close prices.
-    """
-
-    def __init__(self, *, signal_quality_gate=None, signal_quality_gate_path=None,
-                 lake_root=None, gate_topk=10, gate_lookback=5, gate_threshold=0.5,
-                 gate_start="2015-01-03", gate_end="2026-08-19", **kwargs):
-        super().__init__(**kwargs)
-        self._sq_gate_computed = False
-        if signal_quality_gate is not None:
-            self._sq_gate = signal_quality_gate
-            self._sq_gate_computed = True
-        elif signal_quality_gate_path is not None:
-            import pickle
-            with open(signal_quality_gate_path, "rb") as f:
-                self._sq_gate = pickle.load(f)
-            self._sq_gate_computed = True
-        elif lake_root is not None:
-            self._sq_gate = None
-            self._lake_root = lake_root
-            self._gate_topk = gate_topk
-            self._gate_lookback = gate_lookback
-            self._gate_threshold = gate_threshold
-            self._gate_start = gate_start
-            self._gate_end = gate_end
-        else:
-            self._sq_gate = None
-
-    def _compute_gate_on_fly(self):
-        """Compute gate from the signal (pred.pkl) and lake close prices."""
-        import pickle
-        from pathlib import Path
-
-        signal_path = self._signal
-        if not Path(signal_path).exists():
-            return
-
-        with open(signal_path, "rb") as f:
-            pred = pickle.load(f)
-
-        # Handle MultiIndex DataFrame -> unstack to wide
-        if isinstance(pred, pd.DataFrame) and isinstance(pred.index, pd.MultiIndex):
-            pred = pred.iloc[:, 0]
-            pred.index = pd.MultiIndex.from_arrays([
-                pd.to_datetime(pred.index.get_level_values(0)).normalize(),
-                pred.index.get_level_values(1)
-            ])
-            pred = pred.unstack(level=1)
-        elif isinstance(pred, pd.DataFrame):
-            pred = pred.iloc[:, 0] if pred.shape[1] >= 1 else pred.squeeze()
-            pred.index = pd.to_datetime(pred.index).normalize()
-
-        # Load close prices from lake
-        close_df = _load_close_prices(self._lake_root, "US", self._gate_start, self._gate_end)
-        if close_df.empty:
-            return
-
-        ret_df = close_df.pct_change()
-        ret_df.index = pd.to_datetime(ret_df.index).normalize()
-
-        pred_dates = sorted(pred.index.unique())
-        if len(pred_dates) < 2:
-            self._sq_gate = pd.Series(True, index=pd.DatetimeIndex(pred_dates))
-            self._sq_gate_computed = True
-            return
-
-        hit_rates = {}
-        for i in range(1, len(pred_dates)):
-            day = pred_dates[i]
-            prev_day = pred_dates[i - 1]
-            try:
-                prev_scores = pred.loc[prev_day]
-            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[:self._gate_topk])
-
-            if day not in ret_df.index:
-                continue
-            today_ret = ret_df.loc[day]
-            topk_rets = today_ret.reindex(topk_syms).dropna()
-            if len(topk_rets) == 0:
-                continue
-
-            hit_rates[day] = (topk_rets > 0).sum() / len(topk_rets)
-
-        if not hit_rates:
-            self._sq_gate_computed = True
-            return
-
-        hr_series = pd.Series(hit_rates).sort_index()
-        rolling_hr = hr_series.rolling(self._gate_lookback, min_periods=1).mean()
-        gate = rolling_hr >= self._gate_threshold
-        gate.iloc[:self._gate_lookback] = True
-
-        self._sq_gate = gate
-        self._sq_gate_computed = True
-
-    def _gate_open(self, trade_start_time) -> bool:
-        if self._sq_gate is None:
-            return True
-        ts = pd.Timestamp(trade_start_time)
-        known = self._sq_gate[self._sq_gate.index <= ts]
-        if len(known):
-            return bool(known.iloc[-1])
-        return True  # default open if no history yet
-
-    def generate_trade_decision(self, execute_result=None):
-        if not self._sq_gate_computed:
-            self._compute_gate_on_fly()
-        trade_step = self.trade_calendar.get_trade_step()
-        trade_start_time, _ = self.trade_calendar.get_step_time(trade_step)
-        if not self._gate_open(trade_start_time):
-            return TradeDecisionWO([], self)
-        return super().generate_trade_decision(execute_result)
-
-
-# ---------------------------------------------------------------------------
-# Precomputation helper
-# ---------------------------------------------------------------------------
-
-def compute_signal_quality_gate(
-    pred_path: str,
-    *,
-    lake_root: str = "",
-    market: str = "US",
-    topk: int = 10,
-    lookback: int = 5,
-    threshold: float = 0.5,
-    start: str = "2015-01-03",
-    end: str = "2026-08-19",
-) -> pd.Series:
-    """Build a per-date signal-quality gate series from a pred.pkl and lake bars.
-
-    For each day, checks whether the model's topk picks from the previous day
-    had positive returns.  Computes a rolling hit rate over ``lookback`` days
-    and opens the gate when hit rate >= ``threshold``.
-
-    Parameters
-    ----------
-    pred_path : str — path to pred.pkl (from rd_train / rd_predict).
-    lake_root, market : str — lake location (for loading close prices).
-    topk : int — number of top picks to track for hit rate.
-    lookback : int — rolling window for hit rate computation.
-    threshold : float — hit rate threshold to keep trading.
-    start, end : str — date window for loading prices.
-
-    Returns
-    -------
-    pd.Series — bool, indexed by datetime.  True = trade allowed.
-    """
-    from pathlib import Path
-    import pickle
-
-    # Load pred.pkl
-    with open(pred_path, "rb") as f:
-        pred = pickle.load(f)
-
-    # Handle MultiIndex DataFrame (datetime, instrument) -> unstack to wide
-    if isinstance(pred, pd.DataFrame) and isinstance(pred.index, pd.MultiIndex):
-        pred = pred.iloc[:, 0]  # take score column as Series
-        pred.index = pd.MultiIndex.from_arrays([
-            pd.to_datetime(pred.index.get_level_values(0)).normalize(),
-            pred.index.get_level_values(1)
-        ])
-        # Unstack to wide: dates x instruments
-        pred = pred.unstack(level=1)
-    elif isinstance(pred, pd.DataFrame):
-        pred = pred.iloc[:, 0] if pred.shape[1] >= 1 else pred.squeeze()
-        pred.index = pd.to_datetime(pred.index).normalize()
-
-    # Load close prices from lake
-    close_df = _load_close_prices(lake_root, market, start, end)
-    if close_df.empty:
-        return pd.Series(dtype=bool)
-
-    ret_df = close_df.pct_change()
-    ret_df.index = pd.to_datetime(ret_df.index).normalize()
-
-    # Get sorted unique prediction dates
-    pred_dates = sorted(pred.index.unique())
-    if len(pred_dates) < 2:
-        return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
-
-    # Compute hit rates
-    hit_rates = {}
-    for i in range(1, len(pred_dates)):
-        day = pred_dates[i]
-        # Get yesterday's topk
-        prev_day = pred_dates[i - 1]
-        try:
-            prev_scores = pred.loc[prev_day]
-        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])
-
-        # Get today's returns
-        if day not in ret_df.index:
-            continue
-        today_ret = ret_df.loc[day]
-        topk_rets = today_ret.reindex(topk_syms).dropna()
-        if len(topk_rets) == 0:
-            continue
-
-        hit_rates[day] = (topk_rets > 0).sum() / len(topk_rets)
-
-    if not hit_rates:
-        return pd.Series(dtype=bool)
-
-    hr_series = pd.Series(hit_rates).sort_index()
-
-    # Rolling hit rate
-    rolling_hr = hr_series.rolling(lookback, min_periods=1).mean()
-
-    # Gate is open when rolling hit rate >= threshold
-    gate = rolling_hr >= threshold
-    gate.iloc[:lookback] = True  # warmup: allow trading
-
-    return gate
-
-
-def _load_close_prices(lake_root, market, start, end):
-    """Load daily close prices for all symbols into a wide DataFrame."""
-    from pathlib import Path
-
-    lake = Path(lake_root)
-    symbols_parquet = lake / "symbols.parquet"
-    if not symbols_parquet.exists():
-        return pd.DataFrame()
-
-    df = pd.read_parquet(symbols_parquet)
-    col = "symbol" if "symbol" in df.columns else df.columns[0]
-    symbols = sorted(df[col].astype(str).str.upper().tolist())
-
-    closes = {}
-    for sym in symbols:
-        p = lake / "market=US" / "timeframe=1d" / f"symbol={sym}.parquet"
-        if not p.exists():
-            continue
-        try:
-            bar = pd.read_parquet(p)
-        except Exception:
-            continue
-        if not len(bar):
-            continue
-        tcol = "t" if "t" in bar.columns else "date"
-        ts = pd.to_datetime(bar[tcol])
-        bar = bar.assign(_t=ts).set_index("_t").sort_index()
-        bar = bar.loc[start:end]
-        if len(bar) < 10:
-            continue
-        closes[sym] = bar["c"] if "c" in bar.columns else bar["close"]
-
-    return pd.DataFrame(closes)
@@ -1,16 +1,16 @@
 # -----------------------------------------------------------------------------
-# Signal-quality gate: TopkDropout gated by rolling hit-rate of topk picks.
+# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
+# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
+# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
+# distinct experiment name so the ablation runs are isolated.
 #
-# 1. Compute the gate: python precompute_signal_quality_gate.py <pred.pkl> <gate.pkl>
-# 2. Run this workflow: rd_run_workflow config_path=<this yaml> experiment_name=<exp>
-#
-# The strategy loads the precomputed gate from signal_quality_gate_path.
-# When hit rate >= threshold, trade; otherwise, go to cash.
+# Run:
+#   rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
+#       experiment_name=tac-rd-rank-ablate
 # -----------------------------------------------------------------------------
 {%- set LAKE = TAC_LAKE_DIR %}
 {%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
 {%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-{%- set GATE_PATH = "/app/experiments/book/data/signal_quality_gate/sq_gate_5d_0.50.pkl" %}

 qlib_init:
    provider_uri: "{{ LAKE }}"
@@ -40,22 +40,27 @@ qlib_init:
        module_path: qlib.workflow.expm
        kwargs:
            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-sq-gate"
+            default_exp_name: "tac-rd-rank-ablate"

 task:
    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
+        class: RankICLGBModel
+        module_path: tac_qlib.contrib.model.rank_gbdt
        kwargs:
            loss: mse
-            learning_rate: 0.05
-            num_leaves: 15
-            n_estimators: 200
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
            colsample_bytree: 0.8
            subsample: 0.8
            subsample_freq: 1
-            reg_alpha: 0.01
-            reg_lambda: 0.01
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seed: 42

    dataset:
        class: DatasetH
@@ -105,13 +110,12 @@ task:
          kwargs:
              config:
                  strategy:
-                      class: SignalQualityGateStrategy
-                      module_path: tac_qlib.contrib.strategy.signal_quality_gate
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
                      kwargs:
                          signal: "<PRED>"
-                          signal_quality_gate_path: "{{ GATE_PATH }}"
                          topk: 10
-                          n_drop: 1
+                          n_drop: 2
                          only_tradable: true
                          risk_degree: 0.95
                  backtest:
@@ -1,44 +1,67 @@
-# Signal-quality gate backtest for 2026
+# -----------------------------------------------------------------------------
+# ABLATION B (generic-only): same panel/model as the baseline, but feature
+# fields restricted to the model-free / generic stochastic-process families
+# (jump,har,trend,hurst,signature). Drops the model-specific ou (OU/AR-1
+# half-life) and hmm (2-state regime) families to test whether the generic
+# families alone dominate the rank dimension.
+#
+# Run:
+#   rd_run_workflow config_path=tac-qlib/workflows/ablate_generic_only_sp_fields.yaml \
+#       experiment_name=tac-rd-rank-ablate
+# -----------------------------------------------------------------------------
 {%- set LAKE = TAC_LAKE_DIR %}
 {%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-{# Gate computed on-the-fly from signal + lake close prices #}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}

 qlib_init:
    provider_uri: "{{ LAKE }}"
    region: us
    expression_cache: null
    dataset_cache: null
+
    calendar_provider:
        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
    instrument_provider:
        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
    feature_provider:
        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
    exp_manager:
        class: MLflowExpManager
        module_path: qlib.workflow.expm
        kwargs:
            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-sq-gate-2026"
+            default_exp_name: "tac-rd-rank-ablate"

 task:
    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
+        class: RankICLGBModel
+        module_path: tac_qlib.contrib.model.rank_gbdt
        kwargs:
            loss: mse
-            learning_rate: 0.05
-            num_leaves: 15
-            n_estimators: 200
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
            colsample_bytree: 0.8
            subsample: 0.8
            subsample_freq: 1
-            reg_alpha: 0.01
-            reg_lambda: 0.01
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seed: 42

    dataset:
        class: DatasetH
@@ -52,7 +75,7 @@ task:
                    start_time: 2015-01-03
                    end_time: 2026-08-10
                    fit_start_time: 2015-01-03
-                    fit_end_time: 2026-01-03
+                    fit_end_time: 2025-09-01
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
@@ -80,24 +103,20 @@ task:
          kwargs: {}
        - class: SigAnaRecord
          module_path: qlib.workflow.record_temp
-          kwargs: { ana_long_short: true, ann_scaler: 252 }
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
        - class: PortAnaRecord
          module_path: qlib.workflow.record_temp
          kwargs:
              config:
                  strategy:
-                      class: SignalQualityGateStrategy
-                      module_path: tac_qlib.contrib.strategy.signal_quality_gate
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
                      kwargs:
                          signal: "<PRED>"
-                          lake_root: "{{ LAKE }}"
-                          gate_topk: 10
-                          gate_lookback: 5
-                          gate_threshold: 0.5
-                          gate_start: "2015-01-03"
-                          gate_end: "2026-08-19"
                          topk: 10
-                          n_drop: 1
+                          n_drop: 2
                          only_tradable: true
                          risk_degree: 0.95
                  backtest:
@@ -0,0 +1,95 @@
+# Walk-forward: A-weekly / test 2024
+# 3x3 re-validation (trace exp 52). Strategy WeeklyRebalanceDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2025-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2023-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2023-08-31"]
+                valid: ["2023-09-01", "2023-12-29"]
+                test:  ["2024-01-02", "2024-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceDropoutStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_rebalance
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2024-01-02"
+                      end_time: "2024-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: A-weekly / test 2025
+# 3x3 re-validation (trace exp 52). Strategy WeeklyRebalanceDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2024-08-30"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2024-08-30"]
+                valid: ["2024-09-03", "2024-12-31"]
+                test:  ["2025-01-02", "2025-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceDropoutStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_rebalance
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2025-01-02"
+                      end_time: "2025-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: A-weekly / test 2026
+# 3x3 re-validation (trace exp 52). Strategy WeeklyRebalanceDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-08-19"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2025-09-01"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2025-09-01"]
+                valid: ["2025-09-03", "2026-01-03"]
+                test:  ["2026-01-04", "2026-08-19"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceDropoutStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_rebalance
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2026-01-04"
+                      end_time: "2026-08-19"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: B-moments / test 2024
+# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=1, parallel=default(auto).
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2025-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2023-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2023-08-31"]
+                valid: ["2023-09-01", "2023-12-29"]
+                test:  ["2024-01-02", "2024-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2024-01-02"
+                      end_time: "2024-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: B-moments / test 2025
+# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=1, parallel=default(auto).
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2024-08-30"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2024-08-30"]
+                valid: ["2024-09-03", "2024-12-31"]
+                test:  ["2025-01-02", "2025-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2025-01-02"
+                      end_time: "2025-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: B-moments / test 2026
+# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=1, parallel=default(auto).
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-08-19"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2025-09-01"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2025-09-01"]
+                valid: ["2025-09-03", "2026-01-03"]
+                test:  ["2026-01-04", "2026-08-19"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2026-01-04"
+                      end_time: "2026-08-19"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: C-ndrop2 / test 2024
+# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=2, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2025-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2023-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2023-08-31"]
+                valid: ["2023-09-01", "2023-12-29"]
+                test:  ["2024-01-02", "2024-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2024-01-02"
+                      end_time: "2024-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: C-ndrop2 / test 2025
+# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=2, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2024-08-30"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2024-08-30"]
+                valid: ["2024-09-03", "2024-12-31"]
+                test:  ["2025-01-02", "2025-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2025-01-02"
+                      end_time: "2025-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,95 @@
+# Walk-forward: C-ndrop2 / test 2026
+# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=2, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-08-19"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2025-09-01"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2025-09-01"]
+                valid: ["2025-09-03", "2026-01-03"]
+                test:  ["2026-01-04", "2026-08-19"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2026-01-04"
+                      end_time: "2026-08-19"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Walk-forward transfer test: m2-sharpe22 / test 2021 (trace exp 54)
+# Label-regime match to 2026 (psi 0.039). Strategy TopkDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-2021-2023" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2022-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2020-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2020-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2020-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2020-08-31"]
+                valid: ["2020-09-01", "2020-12-31"]
+                test:  ["2021-01-04", "2021-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2021-01-04"
+                      end_time: "2021-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Walk-forward transfer test: m2-sharpe22 / test 2023 (trace exp 54)
+# Best label-regime match to 2026 (psi 0.028). Strategy TopkDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-2021-2023" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2024-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2022-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2022-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2022-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2022-08-31"]
+                valid: ["2022-09-01", "2022-12-30"]
+                test:  ["2023-01-03", "2023-12-29"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2023-01-03"
+                      end_time: "2023-12-29"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Walk-forward: m2-sharpe22 / test 2024 (trace exp 53, ref exp32 c7c12228 on cleaned lake)
+# Strategy TopkDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-3windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2025-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2023-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2023-08-31"]
+                valid: ["2023-09-01", "2023-12-29"]
+                test:  ["2024-01-02", "2024-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2024-01-02"
+                      end_time: "2024-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Walk-forward: m2-sharpe22 / test 2025 (trace exp 53, ref exp32 c7c12228 on cleaned lake)
+# Strategy TopkDropoutStrategy n_drop=1, parallel=5.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-3windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-01-07"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2024-08-30"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2024-08-30"]
+                valid: ["2024-09-03", "2024-12-31"]
+                test:  ["2025-01-02", "2025-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2025-01-02"
+                      end_time: "2025-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Walk-forward: m2-sharpe22 / test 2026 (trace exp 53, ref exp32 c7c12228 on cleaned lake)
+# Strategy TopkDropoutStrategy n_drop=1, parallel=5. Window matches reference exp32 exactly.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-3windows" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-08-10"
+                    fit_start_time: "2016-01-04"
+                    fit_end_time: "2025-09-01"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2016-01-04", "2025-09-01"]
+                valid: ["2025-09-03", "2026-01-03"]
+                test:  ["2026-01-04", "2026-08-10"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2026-01-04"
+                      end_time: "2026-08-10"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2021 (trace exp 55) — 1y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-1y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2022-01-07"
+                    fit_start_time: "2019-08-30"
+                    fit_end_time: "2020-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2019-08-30", fit_end_time: "2020-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2019-08-30", fit_end_time: "2020-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2019-08-30", "2020-08-31"]
+                valid: ["2020-09-01", "2020-12-31"]
+                test:  ["2021-01-04", "2021-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2021-01-04"
+                      end_time: "2021-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2023 (trace exp 55) — 1y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-1y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2024-01-07"
+                    fit_start_time: "2021-08-31"
+                    fit_end_time: "2022-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2022-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2022-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2021-08-31", "2022-08-31"]
+                valid: ["2022-09-01", "2022-12-30"]
+                test:  ["2023-01-03", "2023-12-29"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2023-01-03"
+                      end_time: "2023-12-29"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2024 (trace exp 55) — 1y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-1y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2025-01-07"
+                    fit_start_time: "2022-08-31"
+                    fit_end_time: "2023-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2023-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2023-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2022-08-31", "2023-08-31"]
+                valid: ["2023-09-01", "2023-12-29"]
+                test:  ["2024-01-02", "2024-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2024-01-02"
+                      end_time: "2024-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2025 (trace exp 55) — 1y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-1y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-01-07"
+                    fit_start_time: "2023-08-31"
+                    fit_end_time: "2024-08-30"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2023-08-31", fit_end_time: "2024-08-30" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2023-08-31", fit_end_time: "2024-08-30" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2023-08-31", "2024-08-30"]
+                valid: ["2024-09-03", "2024-12-31"]
+                test:  ["2025-01-02", "2025-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2025-01-02"
+                      end_time: "2025-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2026 (trace exp 55) — 1y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-1y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-08-10"
+                    fit_start_time: "2024-09-03"
+                    fit_end_time: "2025-09-01"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2024-09-03", fit_end_time: "2025-09-01" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2024-09-03", fit_end_time: "2025-09-01" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2024-09-03", "2025-09-01"]
+                valid: ["2025-09-03", "2026-01-03"]
+                test:  ["2026-01-04", "2026-08-10"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2026-01-04"
+                      end_time: "2026-08-10"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2021 (trace exp 55) — 2y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-2y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2022-01-07"
+                    fit_start_time: "2018-08-31"
+                    fit_end_time: "2020-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2018-08-31", fit_end_time: "2020-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2018-08-31", fit_end_time: "2020-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2018-08-31", "2020-08-31"]
+                valid: ["2020-09-01", "2020-12-31"]
+                test:  ["2021-01-04", "2021-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2021-01-04"
+                      end_time: "2021-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2023 (trace exp 55) — 2y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-2y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2024-01-07"
+                    fit_start_time: "2020-08-31"
+                    fit_end_time: "2022-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2020-08-31", fit_end_time: "2022-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2020-08-31", fit_end_time: "2022-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2020-08-31", "2022-08-31"]
+                valid: ["2022-09-01", "2022-12-30"]
+                test:  ["2023-01-03", "2023-12-29"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2023-01-03"
+                      end_time: "2023-12-29"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2024 (trace exp 55) — 2y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-2y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2025-01-07"
+                    fit_start_time: "2021-08-31"
+                    fit_end_time: "2023-08-31"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2023-08-31" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2023-08-31" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2021-08-31", "2023-08-31"]
+                valid: ["2023-09-01", "2023-12-29"]
+                test:  ["2024-01-02", "2024-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2024-01-02"
+                      end_time: "2024-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2025 (trace exp 55) — 2y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-2y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-01-07"
+                    fit_start_time: "2022-08-31"
+                    fit_end_time: "2024-08-30"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2024-08-30" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2024-08-30" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2022-08-31", "2024-08-30"]
+                valid: ["2024-09-03", "2024-12-31"]
+                test:  ["2025-01-02", "2025-12-31"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2025-01-02"
+                      end_time: "2025-12-31"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,96 @@
+# Adaptive short-window retrain: m2-sharpe22 / test 2026 (trace exp 55) — 2y training window
+# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
+{% set LAKE = TAC_LAKE_DIR %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-m2-sharpe22-adaptive-2y" }
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                    start_time: "2015-01-03"
+                    end_time: "2026-08-10"
+                    fit_start_time: "2023-09-01"
+                    fit_end_time: "2025-09-01"
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22"
+                    infer_processors:
+                        - { class: DropAllNaN, kwargs: { fit_start_time: "2023-09-01", fit_end_time: "2025-09-01" } }
+                        - { class: ProcessInf, kwargs: {} }
+                        - { class: CSRankNorm, kwargs: {} }
+                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2023-09-01", fit_end_time: "2025-09-01" } }
+                        - { class: Fillna, kwargs: {} }
+            segments:
+                train: ["2023-09-01", "2025-09-01"]
+                valid: ["2025-09-03", "2026-01-03"]
+                test:  ["2026-01-04", "2026-08-10"]
+
+    record:
+        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
+        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                  backtest:
+                      start_time: "2026-01-04"
+                      end_time: "2026-08-10"
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -1,44 +1,74 @@
-# Signal-quality gate backtest for 2023
+# -----------------------------------------------------------------------------
+# ISOLATION: multi-seed RankIC ensemble, ablate-B generic-only feature set.
+#
+# Isolates the ensemble effect on the SP-5d rank signal. Same panel, segments,
+# history (full backfilled 2016+) and feature set as the exp-9 ablate-B winner
+# (generic-only sp_* families: jump,har,trend,hurst,signature), but replaces the
+# single RankICLGBModel with a 5-seed RankICEnsembleLGBModel (42,7,2026,99,123)
+# that averages per-day predictions.
+#
+# Differs from exp-15 (tac-rd-rank-ensemble, mlflow exp 15) ONLY by dropping the
+# TA subset (rsi_14,roc_10,macd_hist,willr_14,atr_14) and the inter-asset xr_*
+# features, so any change vs exp-15 is attributable to the feature set alone,
+# and any change vs exp-9 is attributable to the ensemble + full history alone.
+#
+# Run:
+#   rd_run_workflow config_path=experiments/workflows/exp12_isolation_ensemble.yaml \
+#       experiment_name=tac-rd-rank-ensemble-isolated
+# -----------------------------------------------------------------------------
 {%- set LAKE = TAC_LAKE_DIR %}
 {%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-{# Gate computed on-the-fly from signal + lake close prices #}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}

 qlib_init:
    provider_uri: "{{ LAKE }}"
    region: us
    expression_cache: null
    dataset_cache: null
+
    calendar_provider:
        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
    instrument_provider:
        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
    feature_provider:
        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
    exp_manager:
        class: MLflowExpManager
        module_path: qlib.workflow.expm
        kwargs:
-            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-sq-gate-2023"
+            uri: "sqlite:///mlruns.db"
+            default_exp_name: "tac-rd-rank-ensemble-isolated"

 task:
    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
        kwargs:
            loss: mse
-            learning_rate: 0.05
-            num_leaves: 15
-            n_estimators: 200
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
            colsample_bytree: 0.8
            subsample: 0.8
            subsample_freq: 1
-            reg_alpha: 0.01
-            reg_lambda: 0.01
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"

    dataset:
        class: DatasetH
@@ -50,9 +80,9 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2023-12-29
-                    fit_start_time: 2015-01-03
-                    fit_end_time: 2023-01-03
+                    end_time: 2026-08-14
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
@@ -70,9 +100,9 @@ task:
                        - class: Fillna
                          kwargs: {}
            segments:
-                train: [2015-01-03, 2022-09-01]
-                valid: [2022-09-03, 2023-01-03]
-                test: [2023-01-03, 2023-12-29]
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]

    record:
        - class: SignalRecord
@@ -80,29 +110,25 @@ task:
          kwargs: {}
        - class: SigAnaRecord
          module_path: qlib.workflow.record_temp
-          kwargs: { ana_long_short: true, ann_scaler: 252 }
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
        - class: PortAnaRecord
          module_path: qlib.workflow.record_temp
          kwargs:
              config:
                  strategy:
-                      class: SignalQualityGateStrategy
-                      module_path: tac_qlib.contrib.strategy.signal_quality_gate
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
                      kwargs:
                          signal: "<PRED>"
-                          lake_root: "{{ LAKE }}"
-                          gate_topk: 10
-                          gate_lookback: 5
-                          gate_threshold: 0.5
-                          gate_start: "2015-01-03"
-                          gate_end: "2023-12-29"
                          topk: 10
-                          n_drop: 1
+                          n_drop: 2
                          only_tradable: true
                          risk_degree: 0.95
                  backtest:
-                      start_time: 2023-01-03
-                      end_time: 2023-12-29
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
                      account: 1000000
                      benchmark: SPY
                      exchange_kwargs:
@@ -1,44 +1,69 @@
-# Signal-quality gate backtest for 2025
+# -----------------------------------------------------------------------------
+# EXP 14 - Strategy A (baseline): reference model + TopkDropout.
+#
+# Model = RankICEnsembleLGBModel (5-seed RankIC-early-stopped LGB), the class
+# wired by the tac-rd-rank-ensemble-isolated reference (run 0cea66d9...).
+# Strategy = TopkDropout topk=10 n_drop=2 risk_degree=0.95 (the reference's own
+# recorded backtest strategy), so this run reproduces the reference baseline on
+# the same 50-ETF SP-5d panel.
+#
+# Run:
+#   rd_run_workflow config_path=experiments/workflows/exp14-optstop-v2/a_topk_baseline.yaml \
+#       experiment_name=tac-rd-optstop-v2
+# -----------------------------------------------------------------------------
 {%- set LAKE = TAC_LAKE_DIR %}
 {%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-{# Gate computed on-the-fly from signal + lake close prices #}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}

 qlib_init:
    provider_uri: "{{ LAKE }}"
    region: us
    expression_cache: null
    dataset_cache: null
+
    calendar_provider:
        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
    instrument_provider:
        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
    feature_provider:
        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
    exp_manager:
        class: MLflowExpManager
        module_path: qlib.workflow.expm
        kwargs:
            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-sq-gate-2025"
+            default_exp_name: "tac-rd-optstop-v2"

 task:
    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
        kwargs:
            loss: mse
-            learning_rate: 0.05
-            num_leaves: 15
-            n_estimators: 200
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
            colsample_bytree: 0.8
            subsample: 0.8
            subsample_freq: 1
-            reg_alpha: 0.01
-            reg_lambda: 0.01
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"

    dataset:
        class: DatasetH
@@ -50,9 +75,9 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2025-12-31
-                    fit_start_time: 2015-01-03
-                    fit_end_time: 2026-01-03
+                    end_time: 2026-08-14
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
@@ -70,9 +95,9 @@ task:
                        - class: Fillna
                          kwargs: {}
            segments:
-                train: [2015-01-03, 2025-09-01]
+                train: [2016-01-04, 2025-09-01]
                valid: [2025-09-03, 2026-01-03]
-                test: [2025-01-02, 2025-12-31]
+                test: [2026-01-04, 2026-08-10]

    record:
        - class: SignalRecord
@@ -80,29 +105,25 @@ task:
          kwargs: {}
        - class: SigAnaRecord
          module_path: qlib.workflow.record_temp
-          kwargs: { ana_long_short: true, ann_scaler: 252 }
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
        - class: PortAnaRecord
          module_path: qlib.workflow.record_temp
          kwargs:
              config:
                  strategy:
-                      class: SignalQualityGateStrategy
-                      module_path: tac_qlib.contrib.strategy.signal_quality_gate
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
                      kwargs:
                          signal: "<PRED>"
-                          lake_root: "{{ LAKE }}"
-                          gate_topk: 10
-                          gate_lookback: 5
-                          gate_threshold: 0.5
-                          gate_start: "2015-01-03"
-                          gate_end: "2025-12-31"
                          topk: 10
-                          n_drop: 1
+                          n_drop: 2
                          only_tradable: true
                          risk_degree: 0.95
                  backtest:
-                      start_time: 2025-01-02
-                      end_time: 2025-12-31
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
                      account: 1000000
                      benchmark: SPY
                      exchange_kwargs:
@@ -0,0 +1,149 @@
+# -----------------------------------------------------------------------------
+# EXP 14 - Strategy B (enhanced): reference model + OptimalStopControlV2.
+#
+# Model = RankICEnsembleLGBModel (5-seed RankIC-early-stopped LGB), identical to
+# Strategy A. Strategy = OptimalStopControlV2 (tac_qlib.contrib.strategy.
+# optimal_stop_v2) with the controls that address OptimalStopControl's documented
+# weaknesses:
+#   - turnover / cost control: rebalance_band=0.05 (skip small rebalances),
+#     cooldown_days=3 (no whipsaw re-entries), max_turnover=0.30 (cap daily
+#     traded notional, priority exits > opens > rebalances)
+#   - robust thresholds (no valid-window overfit): entry 0.85 / exit 0.70 /
+#     max_hold 10 / min_hold 2 / sl -0.08
+# Sizing = equal-weight control (risk_degree fraction of total value split
+# across targets) - the "proper allocation" that replaces cash-heuristic sizing.
+#
+# Run:
+#   rd_run_workflow config_path=experiments/workflows/exp14-optstop-v2/b_optstop_v2.yaml \
+#       experiment_name=tac-rd-optstop-v2
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+            markets: {}
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs:
+            lake_root: "{{ LAKE }}"
+            market: US
+
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-optstop-v2"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-14
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: OptimalStopControlV2
+                      module_path: tac_qlib.contrib.strategy.optimal_stop_v2
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          entry_pct: 0.85
+                          exit_pct: 0.70
+                          max_hold_days: 10
+                          min_hold_days: 2
+                          sl: -0.08
+                          risk_degree: 0.95
+                          notional: 20000
+                          rebalance_band: 0.05
+                          cooldown_days: 3
+                          max_turnover: 0.30
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
Author	SHA1	Message	Date
zhaoli	b296668ff5	finish experiment 56 (exp/56-window-staleness-isolation-the-m2-sharpe)	2026-08-20 11:42:18 +00:00
zhaoli	f4f6e9291b	start experiment 56 (exp/56-window-staleness-isolation-the-m2-sharpe)	2026-08-20 11:36:19 +00:00
zhaoli	f4ed5e42fe	finish experiment 55 (exp/55-adaptive-short-window-retrain-test-the-4)	2026-08-20 11:35:04 +00:00
zhaoli	81ee9f1a6e	start experiment 55 (exp/55-adaptive-short-window-retrain-test-the-4)	2026-08-20 11:16:30 +00:00
zhaoli	1b0a3be605	finish experiment 54 (exp/54-walk-forward-transfer-test-m2-sharpe22-o)	2026-08-20 11:10:05 +00:00
zhaoli	7b77e23112	exp 54: snapshot custom qlib code	2026-08-20 10:19:52 +00:00
zhaoli	c6f1e1462b	start experiment 54 (exp/54-walk-forward-transfer-test-m2-sharpe22-o)	2026-08-20 10:19:21 +00:00
zhaoli	46a9aa7b61	finish experiment 53 (exp/53-walk-forward-re-validation-of-m2-sharpe2)	2026-08-20 09:50:50 +00:00
zhaoli	9ccaef3fbb	exp 53: snapshot custom qlib code	2026-08-20 09:42:54 +00:00
zhaoli	60df47d5db	start experiment 53 (exp/53-walk-forward-re-validation-of-m2-sharpe2)	2026-08-20 09:41:45 +00:00
zhaoli	7cd2d91218	finish experiment 52 (exp/52-walk-forward-re-validation-of-the-3-best)	2026-08-20 09:25:18 +00:00
zhaoli	e1781f606b	exp 52: snapshot custom qlib code	2026-08-20 08:50:48 +00:00
zhaoli	336fa676b6	exp 52: add 9 walk-forward workflow yamls (3 configs x 2024/2025/2026)	2026-08-20 08:50:45 +00:00
zhaoli	01292ff21e	start experiment 52 (exp/52-walk-forward-re-validation-of-the-3-best)	2026-08-20 08:49:23 +00:00
zhaoli	875222a85a	exp 14: enhanced stochastic-control allocation - workflow YAMLs (A: TopkDropout baseline, B: OptimalStopControlV2)	2026-08-14 14:46:30 +00:00
zhaoli	32477c7bb8	exp 12: isolation ensemble workflow yaml (ablate-B features + full history) + finish record	2026-08-14 05:37:13 +00:00
zhaoli	e657c58758	start exp 9 (sp5d-feature-family-ablation): baseline all-24 + generic-only 19 workflow YAMLs	2026-08-13 04:32:30 +00:00