finish experiment 37 (exp/37-q05-label22d-22d-forward-return-label-vs)

add q05 label22d workflow
start experiment 37 (exp/37-q05-label22d-22d-forward-return-label-vs)
2026-08-19 23:04:55 +00:00 · 2026-08-19 23:01:29 +00:00 · 2026-08-19 23:01:24 +00:00 · 2026-08-19 23:00:45 +00:00 · 2026-08-19 22:57:50 +00:00 · 2026-08-19 22:57:35 +00:00
18 changed files with 404 additions and 1449 deletions
@@ -1,5 +1,5 @@
 # TradeAC custom-qlib-code snapshot (auto-generated)
-# parent repo HEAD : 22a57a81c62aa2d99f9362a50b52fb4c0d82d1c2
+# parent repo HEAD : 39d330a36a3225c242626cf791748a9fb99791fc
 #   tac-qlib/tac_qlib/contrib
 #   tac-qlib/tac_qlib/data
 # per-file hashes (git hash-object):
@@ -15,14 +15,10 @@
  3e80f2e08b661ddd2f58ffe5a6196063fa41ae51  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
-  c4ef84ffda2a611262412fe1127689c667f3d0c1  tac-qlib/tac_qlib/contrib/strategy/__init__.py
+  4afcf9058231111c412925f4c4b84e81d656db87  tac-qlib/tac_qlib/contrib/strategy/__init__.py
  6ad10c2ebe37c16417e67c7aeb731ad1fcb6da2f  tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
  8d684b3216b040071d9ee4fa920a0e0c7486d278  tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
-  896ef74ae47bcd1ed388e1e5d9c8d70c28097fe9  tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py
  79aaad9e39fcc740a773f4f63c512ce1086cfde0  tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
-  5b9acfb4340111b204249add7760bd53c6ae03f1  tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
-  aa1ee880d52ceb5821d65973962099c2254f710a  tac-qlib/tac_qlib/contrib/strategy/top_bottom.py
-  fe60bacdfedd48617863be31f24b7c7daebfac5a  tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py
  92e6e90eb0cd0a25142034560f27adb6b705b1a8  tac-qlib/tac_qlib/data/__init__.py
  7c4e6c345fad1978efe8860c0d977d0c02d6f8d9  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
  99e602392d51663cb06d5c425000b1ed1e5a916b  tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
@@ -1,13 +1,3 @@
-from .kelly_dropout import FractionalKellyDropoutStrategy  # noqa: F401
 from .optimal_stop import OptimalStopControl  # noqa: F401
-from .regime_gate import RegimeGateDropoutStrategy  # noqa: F401
-from .top_bottom import TopBottomDropoutStrategy  # noqa: F401
-from .weekly_rebalance import WeeklyRebalanceDropoutStrategy  # noqa: F401

-__all__ = [
-    "OptimalStopControl",
-    "FractionalKellyDropoutStrategy",
-    "WeeklyRebalanceDropoutStrategy",
-    "TopBottomDropoutStrategy",
-    "RegimeGateDropoutStrategy",
-]
+__all__ = ["OptimalStopControl"]
@@ -1,201 +0,0 @@
-"""Fractional-Kelly dropout strategy for cross-sectional signals.
-
-Sizing rule variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
-the topk/n_drop SELECTION is identical to the reference, but the buy size is
-proportional to the score MAGNITUDE (edge) instead of equal-weight, capped at a
-fraction ``cap_frac`` of the equal-weight notional so a single name cannot
-over-concentrate the book.
-
-``cap_frac`` is the fraction of the equal-weight per-name notional that a top
-signal can deploy at most (e.g. 0.5 = at most half the equal-weight size).
-Names whose score is below the median of the buy set get a proportionally
-smaller slice; the residual stays in cash (that is the point of the rule:
-throw away less edge per name, deploy less capital when conviction is low).
-"""
-
-from __future__ import annotations
-
-from typing import List
-
-import numpy as np
-import pandas as pd
-
-from qlib.backtest import Order
-from qlib.backtest.decision import OrderDir, TradeDecisionWO
-from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
-
-__all__ = ["FractionalKellyDropoutStrategy"]
-
-DEFAULT_CAP_FRAC = 0.5
-
-
-class FractionalKellyDropoutStrategy(TopkDropoutStrategy):
-    """TopkDropout selection with score-magnitude (fractional-Kelly) sizing.
-
-    Parameters
-    ----------
-    topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
-    forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
-    cap_frac : max buy notional as a fraction of the equal-weight notional.
-    """
-
-    def __init__(self, *, topk, n_drop, cap_frac: float = DEFAULT_CAP_FRAC, **kwargs):
-        super().__init__(topk=topk, n_drop=n_drop, **kwargs)
-        self.cap_frac = cap_frac
-
-    def generate_trade_decision(self, execute_result=None):
-        import copy
-
-        trade_step = self.trade_calendar.get_trade_step()
-        trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
-        pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
-        pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
-        if isinstance(pred_score, pd.DataFrame):
-            pred_score = pred_score.iloc[:, 0]
-        if pred_score is None:
-            return TradeDecisionWO([], self)
-
-        if self.only_tradable:
-
-            def get_first_n(li, n, reverse=False):
-                cur_n = 0
-                res = []
-                for si in reversed(li) if reverse else li:
-                    if self.trade_exchange.is_stock_tradable(
-                        stock_id=si, start_time=trade_start_time, end_time=trade_end_time
-                    ):
-                        res.append(si)
-                        cur_n += 1
-                        if cur_n >= n:
-                            break
-                return res[::-1] if reverse else res
-
-            def get_last_n(li, n):
-                return get_first_n(li, n, reverse=True)
-
-            def filter_stock(li):
-                return [
-                    si
-                    for si in li
-                    if self.trade_exchange.is_stock_tradable(
-                        stock_id=si, start_time=trade_start_time, end_time=trade_end_time
-                    )
-                ]
-
-        else:
-
-            def get_first_n(li, n):
-                return list(li)[:n]
-
-            def get_last_n(li, n):
-                return list(li)[-n:]
-
-            def filter_stock(li):
-                return li
-
-        current_temp: "object" = copy.deepcopy(self.trade_position)
-        sell_order_list: List[Order] = []
-        buy_order_list: List[Order] = []
-        cash = current_temp.get_cash()
-        current_stock_list = current_temp.get_stock_list()
-        last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
-
-        if self.method_buy == "top":
-            today = get_first_n(
-                pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
-                self.n_drop + self.topk - len(last),
-            )
-        elif self.method_buy == "random":
-            topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
-            candi = list(filter(lambda x: x not in last, topk_candi))
-            n = self.n_drop + self.topk - len(last)
-            try:
-                today = np.random.choice(candi, n, replace=False)
-            except ValueError:
-                today = candi
-        else:
-            raise NotImplementedError(f"This type of input is not supported")
-
-        comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
-
-        if self.method_sell == "bottom":
-            sell = last[last.isin(get_last_n(comb, self.n_drop))]
-        elif self.method_sell == "random":
-            candi = filter_stock(last)
-            try:
-                sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
-            except ValueError:
-                sell = candi
-        else:
-            raise NotImplementedError(f"This type of input is not supported")
-
-        buy = today[: len(sell) + self.topk - len(last)]
-        for code in current_stock_list:
-            if not self.trade_exchange.is_stock_tradable(
-                stock_id=code,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
-            ):
-                continue
-            if code in sell:
-                time_per_step = self.trade_calendar.get_freq()
-                if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
-                    continue
-                sell_amount = current_temp.get_stock_amount(code=code)
-                sell_order = Order(
-                    stock_id=code,
-                    amount=sell_amount,
-                    start_time=trade_start_time,
-                    end_time=trade_end_time,
-                    direction=Order.SELL,
-                )
-                if self.trade_exchange.check_order(sell_order):
-                    sell_order_list.append(sell_order)
-                    trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
-                        sell_order, position=current_temp
-                    )
-                    cash += trade_val - trade_cost
-
-        if len(buy) == 0:
-            return TradeDecisionWO(sell_order_list, self)
-
-        # ---- fractional-Kelly sizing --------------------------------------
-        # equal-weight notional (reference baseline)
-        eq_notional = cash * self.risk_degree / len(buy)
-        buy_scores = pred_score.reindex(buy).astype(float)
-        lo, hi = buy_scores.min(), buy_scores.max()
-        if hi == lo:
-            w = pd.Series(1.0, index=buy_scores.index)
-        else:
-            w = (buy_scores - lo) / (hi - lo)  # [0,1] edge magnitude
-            w = w.clip(lower=0.0)
-            w_max = w.max()
-            w = w / w_max if w_max > 0 else w  # max == 1.0
-        for code in buy:
-            if not self.trade_exchange.is_stock_tradable(
-                stock_id=code,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
-            ):
-                continue
-            buy_price = self.trade_exchange.get_deal_price(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
-            )
-            notional = eq_notional * min(self.cap_frac, float(w.get(code, 0.0)))
-            buy_amount = notional / buy_price
-            factor = self.trade_exchange.get_factor(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time
-            )
-            buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
-            buy_order = Order(
-                stock_id=code,
-                amount=buy_amount,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=Order.BUY,
-            )
-            buy_order_list.append(buy_order)
-
-        return TradeDecisionWO(sell_order_list + buy_order_list, self)
@@ -1,231 +0,0 @@
-"""HMM-regime overlay TopkDropout strategy.
-
-Regime-gate overlay on ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
-selection and sizing are identical to the reference, but a name is only BOUGHT
-(entry gate) when its per-symbol HMM regime posterior ``sp_hmm_p_regime1`` on
-the signal date is >= ``regime_threshold``; otherwise it is held in cash instead
-of being opened.
-
-The regime posterior is read from the lake feature provider on the fly via
-``qlib.data.D.features`` (field ``$sp_hmm_p_regime1``) for the signal window, so
-no regime column needs to enter the model's ``feature_fields`` — the gate is a
-pure overlay (book ch.01: regime flags regressed as model features, survived
-only as an overlay). The HMM itself was fit with ``fit_end=<train end>`` when
-the lake features were backfilled, so there is no lookahead.
-
-Names already held are NOT force-sold when the regime turns unfavourable
-(entry gate only, matching the queue-10 design).
-"""
-
-from __future__ import annotations
-
-from typing import List
-
-import numpy as np
-import pandas as pd
-
-from qlib.backtest import Order
-from qlib.backtest.decision import OrderDir, TradeDecisionWO
-from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
-
-try:
-    from qlib.data import D
-except ImportError:  # pragma: no cover - qlib always present in this stack
-    D = None
-
-__all__ = ["RegimeGateDropoutStrategy"]
-
-DEFAULT_REGIME_THRESHOLD = 0.5
-REGIME_FIELD = "$sp_hmm_p_regime1"
-
-
-class RegimeGateDropoutStrategy(TopkDropoutStrategy):
-    """TopkDropout with an HMM-regime entry gate on buy candidates.
-
-    Parameters
-    ----------
-    topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
-    forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
-    regime_threshold : minimum ``sp_hmm_p_regime1`` posterior required to open a
-        new position (default 0.5).
-    """
-
-    def __init__(self, *, topk, n_drop, regime_threshold: float = DEFAULT_REGIME_THRESHOLD, **kwargs):
-        super().__init__(topk=topk, n_drop=n_drop, **kwargs)
-        self.regime_threshold = regime_threshold
-
-    def _regime_for(self, codes, pred_start, pred_end) -> pd.Series:
-        """Return {code: sp_hmm_p_regime1} for the signal window (last day)."""
-        if D is None:
-            return pd.Series(dtype=float)
-        try:
-            df = D.features(list(codes), [REGIME_FIELD], start_time=pred_start, end_time=pred_end, freq="day")
-        except Exception:  # noqa: BLE001 - a regime read failure should gate open, not crash
-            return pd.Series(dtype=float)
-        if df is None or len(df) == 0:
-            return pd.Series(dtype=float)
-        # df index is MultiIndex (datetime, instrument); take the last day's values
-        df = df.reset_index()
-        ts_col = "datetime" if "datetime" in df.columns else df.columns[0]
-        sym_col = "instrument" if "instrument" in df.columns else df.columns[1]
-        last_ts = df[ts_col].max()
-        last = df[df[ts_col] == last_ts]
-        out = {}
-        for _, row in last.iterrows():
-            sym = str(row[sym_col]).split("/")[-1].upper()
-            val = row.iloc[-1]
-            out[sym] = float(val) if val == val else np.nan
-        return pd.Series(out)
-
-    def generate_trade_decision(self, execute_result=None):
-        import copy
-
-        trade_step = self.trade_calendar.get_trade_step()
-        trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
-        pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
-        pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
-        if isinstance(pred_score, pd.DataFrame):
-            pred_score = pred_score.iloc[:, 0]
-        if pred_score is None:
-            return TradeDecisionWO([], self)
-
-        if self.only_tradable:
-
-            def get_first_n(li, n, reverse=False):
-                cur_n = 0
-                res = []
-                for si in reversed(li) if reverse else li:
-                    if self.trade_exchange.is_stock_tradable(
-                        stock_id=si, start_time=trade_start_time, end_time=trade_end_time
-                    ):
-                        res.append(si)
-                        cur_n += 1
-                        if cur_n >= n:
-                            break
-                return res[::-1] if reverse else res
-
-            def get_last_n(li, n):
-                return get_first_n(li, n, reverse=True)
-
-            def filter_stock(li):
-                return [
-                    si
-                    for si in li
-                    if self.trade_exchange.is_stock_tradable(
-                        stock_id=si, start_time=trade_start_time, end_time=trade_end_time
-                    )
-                ]
-
-        else:
-
-            def get_first_n(li, n):
-                return list(li)[:n]
-
-            def get_last_n(li, n):
-                return list(li)[-n:]
-
-            def filter_stock(li):
-                return li
-
-        current_temp: "object" = copy.deepcopy(self.trade_position)
-        sell_order_list: List[Order] = []
-        buy_order_list: List[Order] = []
-        cash = current_temp.get_cash()
-        current_stock_list = current_temp.get_stock_list()
-        last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
-
-        if self.method_buy == "top":
-            today = get_first_n(
-                pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
-                self.n_drop + self.topk - len(last),
-            )
-        elif self.method_buy == "random":
-            topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
-            candi = list(filter(lambda x: x not in last, topk_candi))
-            n = self.n_drop + self.topk - len(last)
-            try:
-                today = np.random.choice(candi, n, replace=False)
-            except ValueError:
-                today = candi
-        else:
-            raise NotImplementedError(f"This type of input is not supported")
-
-        comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
-
-        if self.method_sell == "bottom":
-            sell = last[last.isin(get_last_n(comb, self.n_drop))]
-        elif self.method_sell == "random":
-            candi = filter_stock(last)
-            try:
-                sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
-            except ValueError:
-                sell = candi
-        else:
-            raise NotImplementedError(f"This type of input is not supported")
-
-        buy = today[: len(sell) + self.topk - len(last)]
-
-        # ---- regime gate -----------------------------------------------------
-        if buy:
-            regime = self._regime_for(buy, pred_start_time, pred_end_time)
-            gated = [c for c in buy if regime.get(c, np.nan) >= self.regime_threshold]
-        else:
-            gated = []
-
-        for code in current_stock_list:
-            if not self.trade_exchange.is_stock_tradable(
-                stock_id=code,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
-            ):
-                continue
-            if code in sell:
-                time_per_step = self.trade_calendar.get_freq()
-                if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
-                    continue
-                sell_amount = current_temp.get_stock_amount(code=code)
-                sell_order = Order(
-                    stock_id=code,
-                    amount=sell_amount,
-                    start_time=trade_start_time,
-                    end_time=trade_end_time,
-                    direction=Order.SELL,
-                )
-                if self.trade_exchange.check_order(sell_order):
-                    sell_order_list.append(sell_order)
-                    trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
-                        sell_order, position=current_temp
-                    )
-                    cash += trade_val - trade_cost
-
-        if len(gated) == 0:
-            return TradeDecisionWO(sell_order_list, self)
-
-        value = cash * self.risk_degree / len(gated)
-        for code in gated:
-            if not self.trade_exchange.is_stock_tradable(
-                stock_id=code,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
-            ):
-                continue
-            buy_price = self.trade_exchange.get_deal_price(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
-            )
-            buy_amount = value / buy_price
-            factor = self.trade_exchange.get_factor(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time
-            )
-            buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
-            buy_order = Order(
-                stock_id=code,
-                amount=buy_amount,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=Order.BUY,
-            )
-            buy_order_list.append(buy_order)
-
-        return TradeDecisionWO(sell_order_list + buy_order_list, self)
@@ -1,169 +0,0 @@
-"""Market-neutral top/bottom long-short strategy for cross-sectional signals.
-
-Captures the cross-sectional long-short spread net of costs: buys the top-ranked
-``topk`` names and shorts the bottom-ranked ``topk`` names, equal-weight per
-side, sized to ``risk_degree`` of total value per side. Rebalances daily to the
-current rank (dropout-free: the book converges to the latest top/bottom sets).
-
-The long and short legs use equal notional per side (gross exposure ~2x
-``risk_degree`` of NAV, i.e. approximately market neutral before transaction
-costs). Benchmark neutrality (SPY beta ~ 0) is the secondary sanity metric.
-"""
-
-from __future__ import annotations
-
-from typing import List
-
-import copy
-
-import pandas as pd
-
-from qlib.backtest import Order
-from qlib.backtest.decision import OrderDir, TradeDecisionWO
-from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
-
-__all__ = ["TopBottomDropoutStrategy"]
-
-DEFAULT_SHORT_LEG = True
-DEFAULT_REBALANCE_DAILY = True
-
-
-class TopBottomDropoutStrategy(BaseSignalStrategy):
-    """Long top-k / short bottom-k equal-weight market-neutral book.
-
-    Parameters
-    ----------
-    topk : number of names on each side (long top-k and short bottom-k).
-    short_leg : whether to open the short side (if False, long-only topk).
-    rebalance_daily : if True rebalance to current rank every day; else keep
-        positions and only refresh on score changes (dropout-style).
-    risk_degree : fraction of total value deployed per side.
-    """
-
-    def __init__(
-        self,
-        *,
-        topk: int = 10,
-        short_leg: bool = DEFAULT_SHORT_LEG,
-        rebalance_daily: bool = DEFAULT_REBALANCE_DAILY,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.topk = topk
-        self.short_leg = short_leg
-        self.rebalance_daily = rebalance_daily
-        self._prev_longs = set()
-        self._prev_shorts = set()
-
-    def generate_trade_decision(self, execute_result=None):
-        trade_step = self.trade_calendar.get_trade_step()
-        trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
-        pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
-        pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
-        if isinstance(pred_score, pd.DataFrame):
-            pred_score = pred_score.iloc[:, 0]
-        if pred_score is None or len(pred_score) == 0:
-            return TradeDecisionWO([], self)
-
-        # rank all names; topk longs and topk shorts
-        ranked = pred_score.sort_values(ascending=False)
-        longs = list(ranked.index[: self.topk])
-        shorts = list(ranked.index[-self.topk :]) if self.short_leg else []
-
-        current_temp: "object" = copy.deepcopy(self.trade_position)
-        current_codes = set(current_temp.get_stock_list())
-        holdings = {c: current_temp for c in current_codes if abs(current_temp.get_stock_amount(c)) > 1e-6}
-
-        sell_orders: List[Order] = []
-        buy_orders: List[Order] = []
-
-        def _tradable(code, direction):
-            try:
-                return self.trade_exchange.is_stock_tradable(
-                    stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=direction
-                )
-            except TypeError:
-                return self.trade_exchange.is_stock_tradable(
-                    stock_id=code, start_time=trade_start_time, end_time=trade_end_time
-                )
-
-        # determine target set (long/short)
-        target_longs = set(longs)
-        target_shorts = set(shorts)
-
-        # close positions not in the target book
-        for code in list(holdings):
-            if code in target_longs or code in target_shorts:
-                continue
-            amt = abs(current_temp.get_stock_amount(code))
-            o = Order(
-                stock_id=code,
-                amount=amt,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=Order.SELL if code in target_longs else Order.SELL,
-            )
-            if self.trade_exchange.check_order(o):
-                sell_orders.append(o)
-                self.trade_exchange.deal_order(o, position=current_temp)
-
-        # equal-weight notional per side
-        total_value = current_temp.get_cash()
-        for code, pos in holdings.items():
-            if code in target_longs or code in target_shorts:
-                mark = self.trade_exchange.get_deal_price(
-                    stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
-                )
-                if mark is not None and mark == mark:
-                    total_value += abs(current_temp.get_stock_amount(code)) * mark
-
-        side_notional = total_value * self.risk_degree / max(1, self.topk)
-
-        for code in longs:
-            if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
-                continue
-            px = self.trade_exchange.get_deal_price(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.BUY
-            )
-            if px is None or px != px or px <= 0:
-                continue
-            amount = side_notional / px
-            factor = self.trade_exchange.get_factor(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time
-            )
-            amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
-            o = Order(
-                stock_id=code,
-                amount=amount,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=Order.BUY,
-            )
-            if self.trade_exchange.check_order(o):
-                buy_orders.append(o)
-
-        if self.short_leg:
-            for code in shorts:
-                if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
-                    continue
-                px = self.trade_exchange.get_deal_price(
-                    stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
-                )
-                if px is None or px != px or px <= 0:
-                    continue
-                amount = side_notional / px
-                factor = self.trade_exchange.get_factor(
-                    stock_id=code, start_time=trade_start_time, end_time=trade_end_time
-                )
-                amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
-                o = Order(
-                    stock_id=code,
-                    amount=amount,
-                    start_time=trade_start_time,
-                    end_time=trade_end_time,
-                    direction=Order.SELL,
-                )
-                if self.trade_exchange.check_order(o):
-                    sell_orders.append(o)
-
-        return TradeDecisionWO(sell_orders + buy_orders, self)
@@ -1,202 +0,0 @@
-"""Weekly-rebalance TopkDropout strategy.
-
-Turnover-reduction variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
-the topk/n_drop selection and sizing are identical to the reference, but the
-target book is recomputed only on the first trading day of each ISO week; on the
-other days the strategy issues NO orders (holds the book untouched).
-
-The weekly cadence is derived from the qlib trade calendar: a rebalance happens
-when the current trade step's date belongs to a different ISO ``(year, week)``
-than the previous trade step. ``hold_band_pct`` (default 0) optionally skips
-tiny rebalances: when a name's existing position differs from the new target by
-less than this fraction, no order is generated for it.
-"""
-
-from __future__ import annotations
-
-from typing import List
-
-import numpy as np
-import pandas as pd
-
-from qlib.backtest import Order
-from qlib.backtest.decision import OrderDir, TradeDecisionWO
-from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
-
-__all__ = ["WeeklyRebalanceDropoutStrategy"]
-
-DEFAULT_HOLD_BAND_PCT = 0.0
-
-
-class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
-    """TopkDropout rebalanced once per ISO week; holds otherwise.
-
-    Parameters
-    ----------
-    topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
-    forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
-    hold_band_pct : skip order for a name whose deviation from target weight is
-        below this fraction of the target (no-trade buffer band).
-    """
-
-    def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT, **kwargs):
-        super().__init__(topk=topk, n_drop=n_drop, **kwargs)
-        self.hold_band_pct = hold_band_pct
-
-    @staticmethod
-    def _iso_week(ts) -> tuple:
-        return (ts.year, ts.week)
-
-    def generate_trade_decision(self, execute_result=None):
-        import copy
-
-        trade_step = self.trade_calendar.get_trade_step()
-        trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
-
-        cur_week = self._iso_week(trade_start_time)
-        prev_week = getattr(self, "_last_week", None)
-        self._last_week = cur_week
-
-        if prev_week is not None and prev_week == cur_week:
-            # not the first trading day of this ISO week -> hold
-            return TradeDecisionWO([], self)
-
-        pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
-        pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
-        if isinstance(pred_score, pd.DataFrame):
-            pred_score = pred_score.iloc[:, 0]
-        if pred_score is None:
-            return TradeDecisionWO([], self)
-
-        if self.only_tradable:
-
-            def get_first_n(li, n, reverse=False):
-                cur_n = 0
-                res = []
-                for si in reversed(li) if reverse else li:
-                    if self.trade_exchange.is_stock_tradable(
-                        stock_id=si, start_time=trade_start_time, end_time=trade_end_time
-                    ):
-                        res.append(si)
-                        cur_n += 1
-                        if cur_n >= n:
-                            break
-                return res[::-1] if reverse else res
-
-            def get_last_n(li, n):
-                return get_first_n(li, n, reverse=True)
-
-            def filter_stock(li):
-                return [
-                    si
-                    for si in li
-                    if self.trade_exchange.is_stock_tradable(
-                        stock_id=si, start_time=trade_start_time, end_time=trade_end_time
-                    )
-                ]
-
-        else:
-
-            def get_first_n(li, n):
-                return list(li)[:n]
-
-            def get_last_n(li, n):
-                return list(li)[-n:]
-
-            def filter_stock(li):
-                return li
-
-        current_temp: "object" = copy.deepcopy(self.trade_position)
-        sell_order_list: List[Order] = []
-        buy_order_list: List[Order] = []
-        cash = current_temp.get_cash()
-        current_stock_list = current_temp.get_stock_list()
-        last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
-
-        if self.method_buy == "top":
-            today = get_first_n(
-                pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
-                self.n_drop + self.topk - len(last),
-            )
-        elif self.method_buy == "random":
-            topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
-            candi = list(filter(lambda x: x not in last, topk_candi))
-            n = self.n_drop + self.topk - len(last)
-            try:
-                today = np.random.choice(candi, n, replace=False)
-            except ValueError:
-                today = candi
-        else:
-            raise NotImplementedError(f"This type of input is not supported")
-
-        comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
-
-        if self.method_sell == "bottom":
-            sell = last[last.isin(get_last_n(comb, self.n_drop))]
-        elif self.method_sell == "random":
-            candi = filter_stock(last)
-            try:
-                sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
-            except ValueError:
-                sell = candi
-        else:
-            raise NotImplementedError(f"This type of input is not supported")
-
-        buy = today[: len(sell) + self.topk - len(last)]
-        for code in current_stock_list:
-            if not self.trade_exchange.is_stock_tradable(
-                stock_id=code,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
-            ):
-                continue
-            if code in sell:
-                time_per_step = self.trade_calendar.get_freq()
-                if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
-                    continue
-                sell_amount = current_temp.get_stock_amount(code=code)
-                sell_order = Order(
-                    stock_id=code,
-                    amount=sell_amount,
-                    start_time=trade_start_time,
-                    end_time=trade_end_time,
-                    direction=Order.SELL,
-                )
-                if self.trade_exchange.check_order(sell_order):
-                    sell_order_list.append(sell_order)
-                    trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
-                        sell_order, position=current_temp
-                    )
-                    cash += trade_val - trade_cost
-
-        if len(buy) == 0:
-            return TradeDecisionWO(sell_order_list, self)
-
-        value = cash * self.risk_degree / len(buy)
-        for code in buy:
-            if not self.trade_exchange.is_stock_tradable(
-                stock_id=code,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
-            ):
-                continue
-            buy_price = self.trade_exchange.get_deal_price(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
-            )
-            buy_amount = value / buy_price
-            factor = self.trade_exchange.get_factor(
-                stock_id=code, start_time=trade_start_time, end_time=trade_end_time
-            )
-            buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
-            buy_order = Order(
-                stock_id=code,
-                amount=buy_amount,
-                start_time=trade_start_time,
-                end_time=trade_end_time,
-                direction=Order.BUY,
-            )
-            buy_order_list.append(buy_order)
-
-        return TradeDecisionWO(sell_order_list + buy_order_list, self)
@@ -1,23 +1,22 @@
 # -----------------------------------------------------------------------------
-# EXP 18 - Risk-limit control: reference model + TopkDropout baseline (A).
+# QUEUE-01 — M2 reproduction: risk-adjusted 22d Sharpe drift (sp_sharpe_22).
 #
-# Signal/model identical to the reference (tac-rd-rank-ensemble-isolated,
-# run 0cea66d9...): RankICEnsembleLGBModel (parallel, 5 seeds) on the 50-ETF
-# SP-5d panel, test 2026-01-04..2026-08-10. This workflow reproduces the
-# unconstrained TopkDropout baseline net-of-cost so the risk-limited variant
-# (same pred, liquidity/size/concentration caps) can be compared 1:1.
+# Hypothesis (book ch.01/ch.07, EVIDENCE#018 -> exp 30): adding the
+# risk-adjusted 22d Sharpe drift feature (sp_sharpe_22) to the compact
+# stochastic reference IMPROVES net portfolio performance (exp 30: net +6.53%
+# IR 0.62 vs reference +2.13% IR 0.21) while rank metrics dip (RankIC 0.0576 vs
+# 0.0663). exp 30 is a SINGLE clean-lake run, unreproduced -> HYPOTHESIS.
 #
-# The risk_limits spec itself is applied via rd_backtest / rd_strategy_targets
-# (tool-level param, not a YAML key); this run records the unconstrained
-# baseline that the limit A/B is measured against.
+# Change vs exp-26 reference (EVIDENCE#015, run 21afc6af...): ONE feature added,
+# feature_fields = compact set + sp_sharpe_22. Everything else byte-identical.
 #
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp18-risk-limit/a_baseline.yaml \
-#       experiment_name=tac-rd-risk-limit
+# Acceptance: net_ann_return > +2.13% AND net_IR > 0.21 (else HYPOTHESIS -> REFUTED).
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q01_m2_sharpe22_repro.yaml \
+#        experiment_name=tac-rd-q01-m2-sharpe22-repro
 # -----------------------------------------------------------------------------
 {%- 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" %}
+{%- set FEATURES = "$open,$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" %}

 qlib_init:
    provider_uri: "{{ LAKE }}"
@@ -47,7 +46,7 @@ qlib_init:
        module_path: qlib.workflow.expm
        kwargs:
            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-risk-limit"
+            default_exp_name: "tac-rd-q01-m2-sharpe22-repro"

 task:
    model:
@@ -80,14 +79,14 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2026-08-14
+                    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_FIELDS }}"
+                    feature_fields: "{{ FEATURES }}"
                    infer_processors:
                        - class: DropAllNaN
                          kwargs: {}
@@ -123,7 +122,7 @@ task:
                      kwargs:
                          signal: "<PRED>"
                          topk: 10
-                          n_drop: 2
+                          n_drop: 1
                          only_tradable: true
                          risk_degree: 0.95
                  backtest:
@@ -138,4 +137,4 @@ task:
                          open_cost: 0.0005
                          close_cost: 0.0015
                          min_cost: 5.0
-              risk_analysis_freq: 1d
+              risk_analysis_freq: 1d
@@ -1,17 +1,23 @@
 # -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
+# QUEUE-04 — Non-overlapping 10-day label horizon.
 #
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
+# Hypothesis (book ch.01/chat-ideas): the 5d label is the campaign's best IC
+# lever but sees short-horizon reversal only; a non-overlapping 10d label
+# (`Ref($close,-11)/Ref($close,-1)-1`) tests whether a longer, cleaner horizon
+# captures trend/reversal better and survives cost (lower effective turnover).
 #
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
+# Change vs exp-26 reference: ONE variable — label 5d -> 10d. Everything else
+# identical (features, model, strategy).
+#
+# Acceptance: net_IR > 0.21 AND net_ann_return > +2.13%; secondary: ICIR and
+#   L/S Sharpe >= reference. A flat-but-not-worse result still settles the
+#   horizon-decomposition question (TODO: 5d can't see 1-12m drift).
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q04_label10d.yaml \
+#        experiment_name=tac-rd-q04-label10d
 # -----------------------------------------------------------------------------
 {%- 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" %}
+{%- set FEATURES = "$open,$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" %}

 qlib_init:
    provider_uri: "{{ LAKE }}"
@@ -41,7 +47,7 @@ qlib_init:
        module_path: qlib.workflow.expm
        kwargs:
            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-risk-limit"
+            default_exp_name: "tac-rd-q04-label10d"

 task:
    model:
@@ -62,8 +68,6 @@ task:
            reg_alpha: 0.1
            reg_lambda: 1.0
            seeds: "42,7,2026,99,123"
-            weight_mode: rolling_ic
-            rolling_ic_window: 21
            parallel: 5

    dataset:
@@ -76,14 +80,14 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2026-08-14
+                    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_FIELDS }}"
+                    label: "Ref($close,-11)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
                    infer_processors:
                        - class: DropAllNaN
                          kwargs: {}
@@ -119,7 +123,7 @@ task:
                      kwargs:
                          signal: "<PRED>"
                          topk: 10
-                          n_drop: 2
+                          n_drop: 1
                          only_tradable: true
                          risk_degree: 0.95
                  backtest:
@@ -134,4 +138,4 @@ task:
                          open_cost: 0.0005
                          close_cost: 0.0015
                          min_cost: 5.0
-              risk_analysis_freq: 1d
+              risk_analysis_freq: 1d
@@ -1,22 +1,19 @@
-# QUEUE-me
-# HMM regime overlay (Q10): identical TopkDropout selection, entry gated on sp_hmm_p_regime1 >= 0.5.
-# Acceptance: net_max_drawdown < 7.69% AND net_IR >= 0.21. No regime column enters feature_fields.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+# -----------------------------------------------------------------------------
+# QUEUE-05 — Non-overlapping 22-day label horizon.
+#
+# Hypothesis (book ch.01/chat-ideas): a 22d (~monthly) non-overlapping label
+# tests true trend-following — the 5d label can't distinguish a 1-12m drift
+# (submartingale) from short-horizon reversal. Long-horizon labels also cut
+# the rebalance-implied turnover, attacking the cost constraint directly.
+#
+# Change vs exp-26 reference: ONE variable — label 5d -> 22d
+#   (`Ref($close,-23)/Ref($close,-1)-1`). Everything else identical.
+#
+# Acceptance: net_IR > 0.21 AND net_ann_return > +2.13%; secondary: does the
+#   long-horizon signal survive cost with LOWER total_cost than the 5d book?
+# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q05_label22d.yaml \
+#        experiment_name=tac-rd-q05-label22d
+# -----------------------------------------------------------------------------
 {%- 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 FEATURES = "$open,$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" %}
@@ -49,7 +46,7 @@ qlib_init:
        module_path: qlib.workflow.expm
        kwargs:
            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-q10-regime"
+            default_exp_name: "tac-rd-q05-label22d"

 task:
    model:
@@ -88,7 +85,7 @@ task:
                    freq: day
                    lake_root: "{{ LAKE }}"
                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    label: "Ref($close,-23)/Ref($close,-1)-1"
                    feature_fields: "{{ FEATURES }}"
                    infer_processors:
                        - class: DropAllNaN
@@ -120,15 +117,14 @@ task:
          kwargs:
              config:
                  strategy:
-                      class: RegimeGateDropoutStrategy
-                      module_path: tac_qlib.contrib.strategy.regime_gate
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
                      kwargs:
                          signal: "<PRED>"
                          topk: 10
                          n_drop: 1
                          only_tradable: true
                          risk_degree: 0.95
-                          regime_threshold: 0.5
                  backtest:
                      start_time: 2026-01-04
                      end_time: 2026-08-10
@@ -1,135 +0,0 @@
-# -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
-#
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
-{%- 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-risk-limit"
-
-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"
-            parallel: 2
-
-    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: 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: 2026-08-10
-                      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
@@ -1,136 +0,0 @@
-# -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
-#
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
-{%- 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-risk-limit"
-
-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: "{{ 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: MomentumGateTopk
-                      module_path: tac_qlib.contrib.strategy.momentum_gate
-                      kwargs:
-                          signal: "<PRED>"
-                          topk: 10
-                          n_drop: 2
-                          min_momentum: 0.0
-                          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: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d
@@ -1,138 +0,0 @@
-# -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
-#
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
-{%- 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-risk-limit"
-
-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: "{{ 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: HmmRiskTopk
-                      module_path: tac_qlib.contrib.strategy.hmm_risk
-                      kwargs:
-                          signal: "<PRED>"
-                          topk: 10
-                          n_drop: 2
-                          hmm_pause_pct: 0.70
-                          drawdown_pause_pct: 8.0
-                          liquidity_floor_adv: 5000000
-                          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: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d
@@ -1,47 +1,26 @@
-# -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
-#
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
+# Re-run of experiment 16 with validated family=ta and family=sp lake features.
 {%- set LAKE = TAC_LAKE_DIR %}
 {%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sma_5,sma_20,ema_12,ema_26,rsi_14,macd,macd_signal,macd_hist,bb_upper,bb_middle,bb_lower,atr_14,adx_14,sp_ret,sp_ou_half_life,sp_ou_revert,sp_ou_zscore,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_down,sp_max_move,sp_max_up,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5" %}

 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-risk-limit"
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp16-db-ta-sp" }

 task:
    model:
@@ -62,7 +41,6 @@ task:
            reg_alpha: 0.1
            reg_lambda: 1.0
            seeds: "42,7,2026,99,123"
-            parallel: 1

    dataset:
        class: DatasetH
@@ -74,39 +52,28 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2026-08-14
+                    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_FIELDS }}"
+                    feature_fields: "{{ FEATURES }}"
                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
+                        - { 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: 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:
@@ -114,12 +81,7 @@ task:
                  strategy:
                      class: TopkDropoutStrategy
                      module_path: qlib.contrib.strategy
-                      kwargs:
-                          signal: "<PRED>"
-                          topk: 10
-                          n_drop: 2
-                          only_tradable: true
-                          risk_degree: 0.95
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
                  backtest:
                      start_time: 2026-01-04
                      end_time: 2026-08-10
@@ -1,47 +1,26 @@
-# -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
-#
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
+# General stochastic-process feature ablation: no TA, HMM, or OU fields.
 {%- 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" %}
+{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_down,sp_max_move,sp_max_up,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5" %}

 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-risk-limit"
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp22-stochastic-general" }

 task:
    model:
@@ -62,7 +41,6 @@ task:
            reg_alpha: 0.1
            reg_lambda: 1.0
            seeds: "42,7,2026,99,123"
-            parallel: 5

    dataset:
        class: DatasetH
@@ -74,39 +52,28 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2026-08-14
+                    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_FIELDS }}"
+                    feature_fields: "{{ FEATURES }}"
                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
+                        - { 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: 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:
@@ -114,12 +81,7 @@ task:
                  strategy:
                      class: TopkDropoutStrategy
                      module_path: qlib.contrib.strategy
-                      kwargs:
-                          signal: "<PRED>"
-                          topk: 10
-                          n_drop: 2
-                          only_tradable: true
-                          risk_degree: 0.95
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
                  backtest:
                      start_time: 2026-01-04
                      end_time: 2026-08-10
@@ -1,47 +1,26 @@
-# -----------------------------------------------------------------------------
-# EXP 20 - R1: 2-seed ensemble (seeds 42,7), TopkDropout baseline.
-#
-# Runtime cut: 2 seeds instead of 5. Everything else identical to the reference
-# (test 2026-01-04..2026-08-10, SPY, costs 5bp/15bp). Measures whether the
-# 2-seed ensemble keeps the reference quality at ~2/5 the training time.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp20-risk-limit-improve/r1_2seed.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
+# Exact compact stochastic feature set requested for a new run in MLflow exp 25.
 {%- 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,sma_3,ema_3" %}
+{%- set FEATURES = "$open,$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" %}

 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-risk-limit"
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp22-stochastic-general" }

 task:
    model:
@@ -62,7 +41,6 @@ task:
            reg_alpha: 0.1
            reg_lambda: 1.0
            seeds: "42,7,2026,99,123"
-            parallel: 5

    dataset:
        class: DatasetH
@@ -74,39 +52,28 @@ task:
                kwargs:
                    instruments: "{{ UNIVERSE }}"
                    start_time: 2015-01-03
-                    end_time: 2026-08-14
+                    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_FIELDS }}"
+                    feature_fields: "{{ FEATURES }}"
                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
+                        - { 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: 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:
@@ -114,12 +81,7 @@ task:
                  strategy:
                      class: TopkDropoutStrategy
                      module_path: qlib.contrib.strategy
-                      kwargs:
-                          signal: "<PRED>"
-                          topk: 10
-                          n_drop: 2
-                          only_tradable: true
-                          risk_degree: 0.95
+                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
                  backtest:
                      start_time: 2026-01-04
                      end_time: 2026-08-10
@@ -0,0 +1,98 @@
+# Compact stochastic feature set with reduced turnover: n_drop=1 instead of 2.
+# Same setup as exp24 (compact baseline) but replacing the TopkDropout n_drop 2 with 1.
+{%- 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 FEATURES = "$open,$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" %}
+
+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-exp22-stochastic-general" }
+
+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: 2026-08-10
+                    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: "{{ FEATURES }}"
+                    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: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,99 @@
+# M2 isolation run: base compact set + risk-adjusted drift sp_sharpe_22.
+# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
+# except feature_fields. 5-seed ensemble.
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$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" %}
+
+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-exp30-m2-sharpe" }
+
+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: 2026-08-10
+                    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: "{{ FEATURES }}"
+                    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: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d
@@ -0,0 +1,99 @@
+# M3 isolation run: base compact set + GARCH(1,1) vol-regime trio.
+# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
+# except feature_fields. 5-seed ensemble.
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set FEATURES = "$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_garch_cond_var,sp_garch_persistence,sp_garch_std_resid" %}
+
+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-exp31-m3-garch" }
+
+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: 2026-08-10
+                    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: "{{ FEATURES }}"
+                    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: "{{ 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	df44edc51a	finish experiment 37 (exp/37-q05-label22d-22d-forward-return-label-vs)	2026-08-19 23:04:55 +00:00
zhaoli	39d330a36a	add q05 label22d workflow	2026-08-19 23:01:29 +00:00
zhaoli	3c9cbb6027	start experiment 37 (exp/37-q05-label22d-22d-forward-return-label-vs)	2026-08-19 23:01:24 +00:00
zhaoli	4971a9fe83	finish experiment 36 (exp/36-q04-label10d-10d-forward-return-label-vs)	2026-08-19 23:00:45 +00:00
zhaoli	a4bfb93098	add q04 label10d workflow	2026-08-19 22:57:50 +00:00
zhaoli	2472f458f2	start experiment 36 (exp/36-q04-label10d-10d-forward-return-label-vs)	2026-08-19 22:57:35 +00:00
zhaoli	66cf0a149f	finish experiment 33 (exp/33-q01-m2-reproduction-add-spsharpe22-to-th)	2026-08-19 22:40:28 +00:00
zhaoli	6a1b05db2e	add q01 m2 repro workflow	2026-08-19 22:30:10 +00:00
zhaoli	c7fc73180c	start experiment 33 (exp/33-q01-m2-reproduction-add-spsharpe22-to-th)	2026-08-19 22:05:08 +00:00
zhaoli	483a86e47f	finish experiment 31 (exp/31-isolation-run-m3-does-adding-garch11-vol)	2026-08-18 21:41:40 +00:00
zhaoli	e660b4f2dd	finish experiment 30 (exp/30-isolation-run-m2-does-adding-risk-adjust)	2026-08-18 20:13:22 +00:00
zhaoli	4e1debccba	M2 isolation: base + sp_sharpe_22 (trace 30)	2026-08-18 15:54:24 +00:00
zhaoli	cb18467a6d	start experiment 30 (exp/30-isolation-run-m2-does-adding-risk-adjust)	2026-08-18 15:52:33 +00:00
zhaoli	894ac260a6	finish experiment 26 (exp/26-test-whether-reducing-topkdropout-daily)	2026-08-18 14:05:47 +00:00
zhaoli	c455000a1e	exp26: compact stochastic, n_drop 1 (workflow only)	2026-08-18 12:10:29 +00:00
zhaoli	5c7b2265f4	start experiment 26 (exp/26-test-whether-reducing-topkdropout-daily)	2026-08-18 10:03:21 +00:00
zhaoli	c724682f3a	finish experiment 24 (exp/24-run-the-rankic-ensemble-in-mlflow-experi)	2026-08-18 09:00:11 +00:00
zhaoli	59e733d88d	exp 24: add exact compact stochastic feature workflow	2026-08-18 08:03:37 +00:00
zhaoli	1fcadbfe86	start experiment 24 (exp/24-run-the-rankic-ensemble-in-mlflow-experi)	2026-08-18 08:02:27 +00:00
zhaoli	a718424340	finish experiment 23 (exp/23-test-whether-the-5-day-rankic-ensemble-i)	2026-08-18 07:58:20 +00:00
zhaoli	adf0bfa812	exp 23: add general stochastic feature ablation workflow	2026-08-18 07:21:33 +00:00
zhaoli	b5054ccc25	start experiment 23 (exp/23-test-whether-the-5-day-rankic-ensemble-i)	2026-08-18 07:20:27 +00:00
zhaoli	3d845306fe	finish experiment 22 (exp/22-re-run-experiment-16s-5-day-rankic-ensem)	2026-08-18 07:17:36 +00:00
zhaoli	1075525d6e	exp 22: add validated TA SP ensemble workflow	2026-08-18 06:38:55 +00:00
zhaoli	63c1ea763e	start experiment 22 (exp/22-re-run-experiment-16s-5-day-rankic-ensem)	2026-08-18 06:38:28 +00:00
zhaoli	2c2684b103	start experiment 16 (16-scheduled-algo-retrain-on-20260814-tacrd)	2026-08-15 05:14:48 +00:00