finish experiment 36 (exp/36-q04-label10d-10d-forward-return-label-vs)

code/MANIFEST.txt

@@ -1,5 +1,5 @@
 # TradeAC custom-qlib-code snapshot (auto-generated)
-# parent repo HEAD : bba17bd401cd9cca714c329f7eb3ab71c36a490b
+# parent repo HEAD : a4bfb930986d8c86edc547e0b5d50fed680bbd56
 #   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

code/tac-qlib/tac_qlib/contrib/strategy/__init__.py

@@ -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"]

code/tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py

@@ -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)

code/tac-qlib/tac_qlib/contrib/strategy/regime_gate.py

@@ -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)

code/tac-qlib/tac_qlib/contrib/strategy/top_bottom.py

@@ -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)

code/tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py

@@ -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)

queue/README.md

@@ -1,69 +0,0 @@
-# TradeAC Experiment Queue — Series 2 (Q12+)
-
-**Purpose.** The next pre-registered batch of experiments, continuing Series 1
-(Q01–Q11, exp 33–43, all executed and folded into `book/CLAIMS.md` /
-`book/EVIDENCE.md`). Each entry targets a still-unproven `HYPOTHESIS` from the
-book or an open question flagged in `CLAIMS.md`/`book/README.md`, and follows the
-Series-1 discipline: one variable changed vs the exp-26 reference, acceptance
-fixed BEFORE the run, sequential execution, trace-first, verify-then-close.
-
-**Reference / control (MUST reproduce first).** exp 26 (`21afc6af…`, mlflow exp
-25) is the campaign baseline; exp 39 (Q07, weekly rebalance) is the best
-construction. Reference config is byte-reproduced in `workflows/exp26/` on the
-`exp/26-…` branch and in this dir's `workflows/*.yaml`.
-
-| Config element | exp-26 reference value |
-|---|---|
-| Universe | 50-ETF panel (`UNIVERSE` below) |
-| Features | compact stochastic 25-field set (no ou/hmm/moments/garch) |
-| Label | `Ref($close,-6)/Ref($close,-1)-1` (5d) |
-| Model | `RankICEnsembleLGBModel`, seeds `42,7,2026,99,123`, lr 0.02, leaves 31, 3000 rounds, ES 200 |
-| Segments | train 2016-01-04..2025-09-01 / valid 2025-09-03..2026-01-03 / test 2026-01-04..2026-08-10 |
-| Strategy | TopkDropout, topk 10, n_drop 1, risk_degree 0.95 |
-| Costs | open 0.0005 / close 0.0015 / min $5, deal $close, SPY benchmark, $1M |
-
-**Reference metrics to beat (EVIDENCE#015):** net_ann +2.13%, net_IR 0.21, gross
-+7.02%, maxDD −7.69%, RankIC 0.0663, RankICIR 0.2545, L/S Sharpe 4.54. Weekly
-(Q07, EVIDENCE#028): net +12.51%, IR 1.24, maxDD −4.13%, ~1.1pp cost drag.
-
-## The queue (ordered by value × feasibility)
-
-| ID | Title / hypothesis | Change vs reference (ONE var) | Acceptance | Config | Ready? |
-|----|--------------------|-------------------------------|------------|--------|--------|
-| Q12 | **22d label + weekly recompute** — the untested combo: Q05's label edge (IC 0.097, RankIC 0.117) with Q07's cost relief | label → 22d AND strategy → weekly (two coupled, explicitly pre-registered) | net_IR > 0.5, net_ann > +5%, cost drag ≤ 2pp | `workflows/q12_label22d_weekly.yaml` | ✅ |
-| Q13 | **Weekly rebalance reproduction on a 2nd window** — Q07 was a single OOS window; reproduce on test 2025-01-02..2025-12-31 before promoting to a live round | segments only (shifted) | net_IR > 0.21, net_ann > +2.13% on the new window | `workflows/q13_weekly_second_window.yaml` | ✅ |
-| Q14 | **Out-of-universe validation** — compact stochastic set generalizes off the 50-ETF panel to a single-stock universe | universe → 30 liquid single names | RankIC > 0.03, ICIR > 0.15, net IR > 0 on stocks | `workflows/q14_out_of_universe.yaml` | ⚠️ needs stock-lake backfill (see design) |
-| Q15 | **5-seed vs single-model clean A/B** — seed-count claim (exp 12 idea, re-validated exp 22–24, never a clean A/B) | seeds → 1 (`2026`) | single-model RankIC/IR < 5-seed ref; net_IR ≥ 0.21 acceptable if ≥ single | `workflows/q15_single_seed.yaml` | ✅ |
-| Q16 | **HMM family added as features** — settles "dropping model-specific (ou,hmm) improves signal" (exp 25 tested OU; hmm-as-feature untested) | features += `sp_hmm_p_regime1,sp_hmm_state` | no improvement: RankIC ≤ 0.0663, net_IR ≤ 0.21 | `workflows/q16_hmm_features.yaml` | ✅ |
-| Q17 | **Realized-moments family added** — settles "moment/volatility families regress" (exp 11 idea, never clean A/B) | features += `sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22` | no improvement: RankIC ≤ 0.0663, net_IR ≤ 0.21 | `workflows/q17_moments_features.yaml` | ✅ |
-| Q18 | **OptimalStopControl clean re-test** — exp 13/14 claim (TopkDropout > stop-control) never re-tested post-reset | strategy → `OptimalStopControl` (exp-13 params) | TopkDropout net_IR ≥ stop-control net_IR; document cost drag | `workflows/q18_optstop.yaml` | ✅ (module verified in venv) |
-| Q19 | **Martingale / variance-ratio study close-out** — exp 19 never closed; VR<1 at 5–20d on clean lake | ad-hoc script (no qrun) | VR stats + drift decomposition on 50-ETF panel | `designs/q19_martingale_vr.md` | ✅ script |
-| Q20 | **Effective independent names (≈4)** — eigenvalue analysis on clean-lake covariance | ad-hoc script | eigenvalue spectrum + effective-rank count | `designs/q20_effective_names.md` | ✅ script |
-
-### Deferred (methodology / infra, P3)
-- Purged / walk-forward CV (was queue's old Q12) — methodology, not an alpha lever.
-- PSI-based drift-aware retraining cadence — needs a drift-gate module + a retrain decision rule.
-- No-trade buffer band / notional-vs-qty sizing — siblings of Q12/Q13; queue only if weekly reproduces.
-- Macro/drift overlays (SPY>200d regime gate, momentum tilt) — needs new data pipeline.
-
-## Execution protocol (per queued run)
-
-1. **Validate the lake first** (`validate_lake_dataset` + `rd_status`) — clean-lake lesson: silent NaN-drops and hollow coverage invalidate a run. Q14 additionally requires backfilling the single-stock universe (bars + sp/ta features, full range, explicit `start`/`end`).
-2. **Trace before running** (`rd_trace_start` with the hypothesis as `rational`, fresh `experiment_name`, `evolved_from=auto`).
-3. **Run** `rd_run_workflow config_path=<abs path to the queue YAML> experiment_name=<fresh name>` — `wait=false`, poll `rd_exp_get_run` until `FINISHED`.
-4. **Verify against acceptance** via `rd_exp_result` (headline + backtest risk).
-5. **Finish the trace** (`rd_trace_finish` with `metrics` + `evaluation`), snapshot any changed contrib modules.
-6. **Report to the book** — PROVE/REFUTE → update `book/CLAIMS.md` + `book/EVIDENCE.md`.
-
-Sequential execution only (concurrent runs hang — chat-ideas.md ops lesson). Any
-custom strategy/module changed here must be copied into the venv site-packages
-snapshot before `rd_run_workflow` can import it (see `/app/AGENTS.md`). As of
-2026-08-20 `WeeklyRebalanceDropoutStrategy` and `OptimalStopControl` are verified
-in sync with the venv snapshot; the lake already persists the `sp_hmm_*` and
-`sp_moments` families on the 50-ETF panel.
-
-## Provenance
-
-Mined 2026-08-20 from `book/CLAIMS.md`, `book/EVIDENCE.md`, `book/README.md`,
-`book/references/chat-ideas.md`, and Series-1 `queue/` (Q01–Q11, executed exp
-33–43). Reference numbers are post-clean-lake (exp 21+).

queue/designs/q19_martingale_vr.md

@@ -1,26 +0,0 @@
-# QUEUE-19 — Martingale / variance-ratio study close-out (no qrun)
-
-**Status:** QUEUED · **Priority:** P2 · **Effort:** ad-hoc script under `book/data/`
-
-## Hypothesis (settle)
-Assets are submartingales long-horizon / mean-reverting short-horizon
-(`VR < 1` at 5–20d). CLAIMS.md marks this HYPOTHESIS (chat-derived martingale
-study; exp 19 was opened but never closed). It is a market-structure claim, not a
-trading claim — settle it with a clean-lake script, then close exp 19 or open a
-scripted EVIDENCE entry.
-
-## Method (persist everything under `book/data/evidence/q19-vr/`)
-1. Load the 50-ETF panel 1d bars from the lake for 2015-01-01..2026-08-19.
-2. Compute the Lo–MacKinlay variance ratio at horizons 5 / 10 / 20d per symbol,
-   with heteroskedasticity-robust z-stats.
-3. Report: per-horizon VR distribution, fraction of symbols with VR < 1 and the
-   z-significance, pooled drift vs daily variance (submartingale check).
-4. Cross-check the pooled `sp_trend_slope_5` regression beta claim (β ≈ −0.53,
-   t ≈ −24) on the clean lake.
-5. Write `VR_stats.csv` + a one-page summary into the evidence dir.
-
-## Acceptance
-- VR < 1 at 5–20d for a material fraction of the panel with |z| > 2 → supports
-  the mean-reversion HYPOTHESIS; else mark REFUTED or REFERENCED.
-- The result updates CLAIMS.md's "Assets are submartingales…" row and closes the
-  exp-19 open thread.

queue/designs/q20_effective_names.md

@@ -1,22 +0,0 @@
-# QUEUE-20 — Effective independent names in the 50-ETF book (no qrun)
-
-**Status:** QUEUED · **Priority:** P2 · **Effort:** ad-hoc script under `book/data/`
-
-## Hypothesis (settle)
-The 50-ETF book has only ~4 effective independent names (CLAIMS.md HYPOTHESIS,
-chat-derived eigenvalue analysis, pre-reset). This is a concentration/diversification
-claim with direct sizing relevance; verify it on the clean lake.
-
-## Method (persist everything under `book/data/evidence/q20-effective-names/`)
-1. Load the 50-ETF panel 1d returns from the lake for the test window 2026-01-04..2026-08-10.
-2. Standardize returns; compute the correlation matrix and its eigendecomposition.
-3. Count eigenvalues above the Marchenko–Pastur bound (N=50, T≈150) and report the
-   cumulative-variance share of the top k components.
-4. Effective-rank measures: participation ratio `(Σλ)² / Σλ²` and cumulative 80%
-   variance count.
-5. Write `eigenanalysis.csv` + a one-page summary.
-
-## Acceptance
-- If effective rank ≈ 4 (top-4 explain ~80%+ variance), the concentration claim is
-  PROVEN and feeds chapter 08 sizing guidance (why topk 10→20 adds no breadth).
-- If effective rank is much larger, mark the claim REFUTED.

queue/workflows/q01_m2_sharpe22_repro.yaml

@@ -1,35 +1,52 @@
-# QUEUE-18 — OptimalStopControl clean re-test vs TopkDropout (exp 13/14 claim).
-# CLAIMS.md HYPOTHESIS: "TopkDropout beats stochastic-control OptimalStopControl on
-# the ensemble signal" — exp 13/14 were pre-clean-lake; never re-tested post-reset.
-# Same compact signal as the exp-26 reference; ONLY the strategy changes to
-# OptimalStopControl with exp-13 params (entry 0.85 / exit 0.7 / hold 10 / sl -0.08).
-# PREREQUISITE: tac_qlib/contrib/strategy/optimal_stop.py must be synced to the venv
-# site-packages snapshot before running (see /app/AGENTS.md).
-# Acceptance: TopkDropout net_IR >= stop-control net_IR; document cost drag of both.
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q18_optstop.yaml \
-#        experiment_name=tac-rd-q18-optstop
+# -----------------------------------------------------------------------------
+# QUEUE-01 — M2 reproduction: risk-adjusted 22d Sharpe drift (sp_sharpe_22).
+#
+# 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.
+#
+# Change vs exp-26 reference (EVIDENCE#015, run 21afc6af...): ONE feature added,
+# feature_fields = compact set + sp_sharpe_22. Everything else byte-identical.
+#
+# 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 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" %}
+{%- 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 }
+        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:///mlruns.db", default_exp_name: "tac-rd-q18-optstop" }
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q01-m2-sharpe22-repro"
 
 task:
     model:
@@ -50,6 +67,7 @@ task:
             reg_alpha: 0.1
             reg_lambda: 1.0
             seeds: "42,7,2026,99,123"
+            parallel: 5
 
     dataset:
         class: DatasetH
@@ -70,27 +88,43 @@ task:
                     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: {} }
+                        - 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: 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: OptimalStopControl
-                      module_path: tac_qlib.contrib.strategy.optimal_stop
-                      kwargs: { signal: "<PRED>", topk: 10, entry_pct: 0.85, exit_pct: 0.7, max_hold_days: 10, min_hold_days: 2, sl: -0.08 }
+                      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

queue/workflows/q04_label10d.yaml

@@ -0,0 +1,141 @@
+# -----------------------------------------------------------------------------
+# QUEUE-04 — Non-overlapping 10-day label horizon.
+#
+# 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).
+#
+# 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 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:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-q04-label10d"
+
+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: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-11)/Ref($close,-1)-1"
+                    feature_fields: "{{ FEATURES }}"
+                    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: 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

queue/workflows/q13_weekly_second_window.yaml

@@ -1,106 +0,0 @@
-# QUEUE-13 — Weekly rebalance reproduction on a second OOS window.
-# Q07 (exp 39) proved weekly recompute on test 2026-01-04..2026-08-10 (net +12.51%,
-# IR 1.24) but that is a single OOS window. Before promoting the weekly construction
-# to a live round, reproduce it on a disjoint window: test 2025-01-02..2025-12-31
-# with train/valid shifted to end 2024.
-# Change vs exp-26 reference: segments shifted only (train ends 2024-08, test = 2025);
-# strategy is the SAME weekly recompute as exp 39. Label stays 5d.
-# Acceptance: net_IR > 0.21 AND net_ann > +2.13% on the 2025 window.
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q13_weekly_second_window.yaml \
-#        experiment_name=tac-rd-q13-weekly-second-window
-{%- 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-q13-weekly-second-window" }
-
-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: 2025-12-31
-                    fit_start_time: 2016-01-04
-                    fit_end_time: 2024-08-30
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "{{ FEATURES }}"
-                    infer_processors:
-                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
-                        - { class: ProcessInf, kwargs: {} }
-                        - { class: CSRankNorm, kwargs: {} }
-                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
-                        - { class: Fillna, kwargs: {} }
-            segments:
-                train: [2016-01-04, 2024-08-30]
-                valid: [2024-09-03, 2024-12-31]
-                test: [2025-01-02, 2025-12-31]
-
-    record:
-        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
-        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: WeeklyRebalanceDropoutStrategy
-                      module_path: tac_qlib.contrib.strategy.weekly_rebalance
-                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
-                  backtest:
-                      start_time: 2025-01-02
-                      end_time: 2025-12-31
-                      account: 1000000
-                      benchmark: SPY
-                      exchange_kwargs:
-                          codes: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d

queue/workflows/q14_out_of_universe.yaml

@@ -1,107 +0,0 @@
-# QUEUE-14 — Out-of-universe validation: compact stochastic set on single-stock names.
-# The 50-ETF panel results (compact feature set, RankIC 0.0663) are panel-specific;
-# book/CLAIMS.md marks "generalizes to other universes" HYPOTHESIS - TODO(evidence-needed).
-# Change vs exp-26 reference: universe -> 30 liquid US single-stock names.
-# PREREQUISITE: backfill lake bars + sp/ta features for these symbols (full range,
-# explicit start/end) — the stock panel currently has only ~180d of data (2025-12-01+).
-# Backfill: get_lake_bars symbols=... start=2000-01-03 then
-#   get_lake_sp symbol=<s> start=2000-01-03 end=<today> fit_end=<train-end> persist=true
-# Acceptance: RankIC > 0.03, ICIR > 0.15, net IR > 0 on the stock universe.
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q14_out_of_universe.yaml \
-#        experiment_name=tac-rd-q14-out-of-universe
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "AAPL,MSFT,NVDA,AMZN,GOOGL,META,TSLA,AVGO,AMD,JPM,UNH,PG,JNJ,MA,V,WMT,DIS,HD,KO,PEP,BAC,XOM,MCD,ABBV,COST,CRM,NFLX,ORCL,IBM,T" %}
-{%- 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-q14-out-of-universe" }
-
-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

workflows/ablate_baseline_all_sp_fields.yaml

@@ -0,0 +1,133 @@
+# -----------------------------------------------------------------------------
+# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
+# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
+# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
+# distinct experiment name so the ablation runs are isolated.
+#
+# Run:
+#   rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
+#       experiment_name=tac-rd-rank-ablate
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+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-rank-ablate"
+
+task:
+    model:
+        class: RankICLGBModel
+        module_path: tac_qlib.contrib.model.rank_gbdt
+        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
+            seed: 42
+
+    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: 2015-01-03
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2015-01-03, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          n_drop: 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

workflows/ablate_generic_only_sp_fields.yaml

@@ -0,0 +1,134 @@
+# -----------------------------------------------------------------------------
+# ABLATION B (generic-only): same panel/model as the baseline, but feature
+# fields restricted to the model-free / generic stochastic-process families
+# (jump,har,trend,hurst,signature). Drops the model-specific ou (OU/AR-1
+# half-life) and hmm (2-state regime) families to test whether the generic
+# families alone dominate the rank dimension.
+#
+# Run:
+#   rd_run_workflow config_path=tac-qlib/workflows/ablate_generic_only_sp_fields.yaml \
+#       experiment_name=tac-rd-rank-ablate
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_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-rank-ablate"
+
+task:
+    model:
+        class: RankICLGBModel
+        module_path: tac_qlib.contrib.model.rank_gbdt
+        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
+            seed: 42
+
+    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: 2015-01-03
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2015-01-03, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: TopkDropoutStrategy
+                      module_path: qlib.contrib.strategy
+                      kwargs:
+                          signal: "<PRED>"
+                          topk: 10
+                          n_drop: 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

workflows/exp12_isolation_ensemble.yaml

@@ -0,0 +1,141 @@
+# -----------------------------------------------------------------------------
+# ISOLATION: multi-seed RankIC ensemble, ablate-B generic-only feature set.
+#
+# Isolates the ensemble effect on the SP-5d rank signal. Same panel, segments,
+# history (full backfilled 2016+) and feature set as the exp-9 ablate-B winner
+# (generic-only sp_* families: jump,har,trend,hurst,signature), but replaces the
+# single RankICLGBModel with a 5-seed RankICEnsembleLGBModel (42,7,2026,99,123)
+# that averages per-day predictions.
+#
+# Differs from exp-15 (tac-rd-rank-ensemble, mlflow exp 15) ONLY by dropping the
+# TA subset (rsi_14,roc_10,macd_hist,willr_14,atr_14) and the inter-asset xr_*
+# features, so any change vs exp-15 is attributable to the feature set alone,
+# and any change vs exp-9 is attributable to the ensemble + full history alone.
+#
+# Run:
+#   rd_run_workflow config_path=experiments/workflows/exp12_isolation_ensemble.yaml \
+#       experiment_name=tac-rd-rank-ensemble-isolated
+# -----------------------------------------------------------------------------
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_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-rank-ensemble-isolated"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-14
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: {}
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: {}
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              ana_long_short: true
+              ann_scaler: 252
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: 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

workflows/exp22/workflow.yaml

@@ -0,0 +1,97 @@
+# 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 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 }
+    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-exp16-db-ta-sp" }
+
+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: 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

workflows/exp23/workflow.yaml

@@ -0,0 +1,97 @@
+# 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 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 }
+    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: 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

workflows/exp24/workflow.yaml

@@ -1,12 +1,4 @@
-# QUEUE-12 — Long-horizon label (22d) + weekly recompute construction.
-# Untested combination from book/CLAIMS.md open questions: Q05 (exp 37) proved the
-# 22d label has the strongest signal (IC 0.097, RankIC 0.117) but daily turnover
-# killed the book (net -4.60%); Q07 (exp 39) proved weekly recompute is the cost
-# lever (net +12.51%). Hypothesis: pairing them monetizes the label edge.
-# Change vs exp-26 reference: label 5d -> 22d AND strategy -> WeeklyRebalanceDropoutStrategy.
-# Acceptance: net_IR > 0.5, net_ann > +5%, cost drag <= 2pp.
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q12_label22d_weekly.yaml \
-#        experiment_name=tac-rd-q12-label22d-weekly
+# 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 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" %}
@@ -28,7 +20,7 @@ qlib_init:
     exp_manager:
         class: MLflowExpManager
         module_path: qlib.workflow.expm
-        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-q12-label22d-weekly" }
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp22-stochastic-general" }
 
 task:
     model:
@@ -66,7 +58,7 @@ task:
                     freq: day
                     lake_root: "{{ LAKE }}"
                     market: US
-                    label: "Ref($close,-23)/Ref($close,-1)-1"
+                    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" } }
@@ -87,9 +79,9 @@ task:
           kwargs:
               config:
                   strategy:
-                      class: WeeklyRebalanceDropoutStrategy
-                      module_path: tac_qlib.contrib.strategy.weekly_rebalance
-                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
+                      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
@@ -102,4 +94,4 @@ task:
                           open_cost: 0.0005
                           close_cost: 0.0015
                           min_cost: 5.0
-              risk_analysis_freq: 1d
+              risk_analysis_freq: 1d

workflows/exp26/workflow.yaml

@@ -1,15 +1,8 @@
-# QUEUE-17 — Realized-moments family added to the compact set.
-# CLAIMS.md HYPOTHESIS: "Adding moment/volatility families regresses the signal"
-# (idea: pre-clean-lake exp 11). M1 momentum bundle (exp 29) and M3 GARCH (exp 31)
-# were refuted post-reset; the realized-moments family (sp_rskew/sp_rkurt/sp_dsv)
-# has NOT been clean A/B'd. This run adds the moments columns to the compact set.
-# Change vs exp-26 reference: features += sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22.
-# Acceptance (prune-hypothesis): no improvement — RankIC <= 0.0663, net_IR <= 0.21.
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q17_moments_features.yaml \
-#        experiment_name=tac-rd-q17-moments-features
+# 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,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22" %}
+{%- 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 }}"
@@ -28,7 +21,7 @@ qlib_init:
     exp_manager:
         class: MLflowExpManager
         module_path: qlib.workflow.expm
-        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-q17-moments-features" }
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp22-stochastic-general" }
 
 task:
     model:
@@ -102,4 +95,4 @@ task:
                           open_cost: 0.0005
                           close_cost: 0.0015
                           min_cost: 5.0
-              risk_analysis_freq: 1d
+              risk_analysis_freq: 1d

workflows/exp30/workflow.yaml

@@ -1,14 +1,9 @@
-# QUEUE-15 — 5-seed vs single-model clean A/B on the compact stochastic set.
-# CLAIMS.md HYPOTHESIS: "5-seed RankIC ensemble raises performance vs single model
-# on ablated set" — pre-clean-lake exp 12 idea, re-validated directionally by exp
-# 22–24, never a clean A/B post-reset. Seed count is load-bearing (exp 28: 2<5).
-# Change vs exp-26 reference: seeds "42,7,2026,99,123" -> single seed "2026".
-# Acceptance: single-model RankIC < 0.0663, net_IR < 0.21 (ensemble beats single).
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q15_single_seed.yaml \
-#        experiment_name=tac-rd-q15-single-seed
+# 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" %}
+{%- 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 }}"
@@ -27,7 +22,7 @@ qlib_init:
     exp_manager:
         class: MLflowExpManager
         module_path: qlib.workflow.expm
-        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-q15-single-seed" }
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp30-m2-sharpe" }
 
 task:
     model:
@@ -47,7 +42,7 @@ task:
             subsample_freq: 1
             reg_alpha: 0.1
             reg_lambda: 1.0
-            seeds: "2026"
+            seeds: "42,7,2026,99,123"
 
     dataset:
         class: DatasetH

workflows/exp31/workflow.yaml

@@ -1,15 +1,9 @@
-# QUEUE-16 — HMM family added as model features to the compact set.
-# CLAIMS.md HYPOTHESIS: "Dropping model-specific feature families (ou, hmm)
-# improves the rank signal" — exp 25 cleanly tested OU (adding it hurts: IC 0.0511->0.0343);
-# hmm-as-features has NOT been clean A/B'd post-reset (exp 42 tested hmm as an entry
-# GATE overlay, refuted). This run adds the hmm family columns to the compact set.
-# Change vs exp-26 reference: features += sp_hmm_p_regime1, sp_hmm_state.
-# Acceptance (prune-hypothesis): no improvement — RankIC <= 0.0663, net_IR <= 0.21.
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q16_hmm_features.yaml \
-#        experiment_name=tac-rd-q16-hmm-features
+# 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_hmm_p_regime1,sp_hmm_state" %}
+{%- 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 }}"
@@ -28,7 +22,7 @@ qlib_init:
     exp_manager:
         class: MLflowExpManager
         module_path: qlib.workflow.expm
-        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-q16-hmm-features" }
+        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp31-m3-garch" }
 
 task:
     model: