start experiment 72 (exp/72-scheduled-algo-retrain-on-2026-09-08-tac)

code/MANIFEST.txt

@@ -1,31 +1,25 @@
 # TradeAC custom-qlib-code snapshot (auto-generated)
-# parent repo HEAD : 507846cee16eeee11daf33c4176e8aec79b985b2
+# parent repo HEAD : 35a5e745b96cb64748e964279e7b8e52f1ad2c73
 #   tac-qlib/tac_qlib/contrib
 #   tac-qlib/tac_qlib/data
 # per-file hashes (git hash-object):
   1b6298c4a5652f2e863cbdc385a1014a570fcd59  tac-qlib/tac_qlib/contrib/__init__.py
-  b419ee55ed455a1c45423d1c9025ca5cc0a98576  tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
+  2224424d0ff193be4f55d1b791f8fce89439c5d2  tac-qlib/tac_qlib/contrib/backtest/__init__.py
+  0bf40dee440ddbded357d7bbb4efc67c62c4b084  tac-qlib/tac_qlib/contrib/backtest/tradeac_exchange.py
   c76a9f17f680e74eea766eff27f7624359749ed6  tac-qlib/tac_qlib/contrib/data/__init__.py
-  2f6c67620aa2f9e6aaaef3369361d9b3eac3d6ca  tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
-  fdd5923a70a399e8680913593ff111641947898e  tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
-  0dd25ef161c6e0f15eafc84886e7e1381deb38c3  tac-qlib/tac_qlib/contrib/data/handler.py
+  3bba0f1696e4ab4b3deebec3f31f269b2e713899  tac-qlib/tac_qlib/contrib/data/handler.py
   b151d139a0dcde87d74b21e7c4b729176ba5c39b  tac-qlib/tac_qlib/contrib/model/__init__.py
-  08dec87ccdf6bb5d2cf611ca3032a4280aaab8cf  tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
-  6fb61946ea9a83dfb560de3717f5fbf482c4c00e  tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
-  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
-  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
+  184f80da8edf944bad3c8fb4d4d3d189bf4f082b  tac-qlib/tac_qlib/contrib/strategy/__init__.py
   896ef74ae47bcd1ed388e1e5d9c8d70c28097fe9  tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py
+  9090fc6dfbd339f2f4df4b0c9b87f400ecb5c9d5  tac-qlib/tac_qlib/contrib/strategy/long_short.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
+  839abb89ad40cd516eabcfd91fe1be626b9f091f  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
-  020dcdcf288e4832c8cf2386351f78d5ceb4fe13  tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
-  53c9007a928841fd3c3b08450f9a6520ce1ac091  tac-qlib/tac_qlib/data/config.py
+  932a06725337edd98e3e4d47b429505d58b63db0  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
+  4017cccb1cec19ab669e7e310f6577d81233ea6c  tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
+  a615243ed287e3dab9e7ef20d8354a602fb01291  tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
+  1953fb2a6371525db7f7b0e1c9dfbf3492d82110  tac-qlib/tac_qlib/data/config.py
   8d0644f6f0d1efb94798ed444cc73e63b643459b  tac-qlib/tac_qlib/data/providers.py

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

@@ -0,0 +1 @@
+from .tradeac_exchange import TradeACExchange

code/tac-qlib/tac_qlib/contrib/backtest/tradeac_exchange.py

@@ -0,0 +1,432 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""
+TradeACExchange
+
+A short/borrow enabled Exchange implementation built on top of qlib.backtest.exchange.Exchange.
+This exchange adds simple, configurable margin logic (initial/maintenance), borrowing support for
+shorts, a borrow fee, and a lightweight SMA (Special Memorandum Account) concept to emulate
+behaviors similar to brokers such as IBKR and Alpaca for backtesting purposes.
+
+Notes / limitations
+- This implementation is intentionally lightweight and conservative: it implements the
+  key behaviors needed for strategy/backtest experiments (allowing short selling, computing
+  margin requirements, performing margin-call checks, and tracking SMA-like excess equity).
+- It makes some simplifying assumptions compared to real brokers (no per-product house margins,
+  simplified SMA bookkeeping, borrow availability modeled only by a per-symbol boolean/limit).
+- The Position class in qlib.backtest.position was not changed. To support shorts we update the
+  position.position dict directly when necessary. This keeps integration simple but bypasses some
+  internal Position helpers. Use with care.
+
+API additions
+- allow_short: enable short selling (bool)
+- initial_margin_long/short: fraction required to open a position
+- maintenance_margin_long/short: fraction required to keep a position
+- borrow_fee_rate: periodic borrow fee applied on short value (applied at trade time as additional cost)
+- borrowable: dict mapping stock_id -> bool or float (max borrowable shares). Symbols missing from
+  the dict follow `borrow_default` (default True = unlimited; set False for a strict whitelist)
+- get_sma(position): returns SMA-like excess equity available as "buying power credit"
+- check_margin_call(position): returns True if position is below maintenance requirement
+
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, Optional, Tuple
+
+import numpy as np
+
+from qlib.backtest.decision import Order
+from qlib.backtest.exchange import Exchange
+from qlib.backtest.position import BasePosition
+
+
+class TradeACExchange(Exchange):
+    """An exchange that supports short selling / borrowing and basic margin rules.
+
+    The implementation aims to be compatible with the Exchange API used by Account and
+    Position classes in qlib.backtest. It overrides only the minimum methods required to
+    enable short/borrow behavior and margin calculations.
+    """
+
+    def __init__(
+        self,
+        *args: Any,
+        allow_short: bool = True,
+        initial_margin_long: float = 0.5,
+        initial_margin_short: float = 0.5,
+        maintenance_margin_long: float = 0.25,
+        maintenance_margin_short: float = 0.3,
+        borrow_fee_rate: float = 0.0,
+        borrowable: Optional[Dict[str, float]] = None,
+        borrow_default: bool = True,
+        sma_enabled: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        """Create TradeACExchange.
+
+        Parameters mirror Exchange with additional tradeac-specific options.
+        """
+        super().__init__(*args, **kwargs)
+        self.allow_short = allow_short
+        self.initial_margin_long = initial_margin_long
+        self.initial_margin_short = initial_margin_short
+        self.maintenance_margin_long = maintenance_margin_long
+        self.maintenance_margin_short = maintenance_margin_short
+        self.borrow_fee_rate = borrow_fee_rate
+        # borrowable can be a dict with per-symbol max borrowable amount, or None (unlimited)
+        self.borrowable = borrowable or {}
+        # borrow_default: policy for symbols absent from `borrowable`.
+        #   True  -> unlisted symbols are unlimited-borrowable (legacy behavior)
+        #   False -> unlisted symbols are NOT borrowable; only listed ones can be shorted
+        self.borrow_default = bool(borrow_default)
+        # sma_enabled: whether to expose lightweight SMA calculation
+        self.sma_enabled = sma_enabled
+
+    # --------------------------- Helper calculations ---------------------------
+    def _initial_margin_requirement(self, position: BasePosition) -> float:
+        """Compute the initial margin requirement (money) for the given position.
+
+        We treat longs and shorts separately and sum their required initial margins.
+        """
+        im_req = 0.0
+        for sid in position.get_stock_list():
+            amt = position.get_stock_amount(sid)
+            price = position.get_stock_price(sid)
+            val = amt * price
+            if val > 0:
+                im_req += abs(val) * self.initial_margin_long
+            elif val < 0:
+                im_req += abs(val) * self.initial_margin_short
+        return im_req
+
+    def _maintenance_margin_requirement(self, position: BasePosition) -> float:
+        """Compute the maintenance margin requirement (money) for the given position."""
+        mm_req = 0.0
+        for sid in position.get_stock_list():
+            amt = position.get_stock_amount(sid)
+            price = position.get_stock_price(sid)
+            val = amt * price
+            if val > 0:
+                mm_req += abs(val) * self.maintenance_margin_long
+            elif val < 0:
+                mm_req += abs(val) * self.maintenance_margin_short
+        return mm_req
+
+    def get_equity(self, position: BasePosition) -> float:
+        """Return account equity (position value + cash)."""
+        return position.calculate_value()
+
+    def get_sma(self, position: BasePosition) -> float:
+        """Return a simplified SMA: excess equity above initial margin requirement.
+
+        Note: This is a synthetic/Simplified SMA used for strategy/backtest logic. Real-broker
+        SMA accounting (e.g. credits/debits across days) can be more complex.
+        """
+        if not self.sma_enabled:
+            return 0.0
+        equity = self.get_equity(position)
+        im_req = self._initial_margin_requirement(position)
+        return max(0.0, equity - im_req)
+
+    def check_margin_call(self, position: BasePosition) -> bool:
+        """Return True when the account is under maintenance margin (margin call).
+
+        Margin call condition here is simple: equity < maintenance requirement.
+        """
+        equity = self.get_equity(position)
+        mm_req = self._maintenance_margin_requirement(position)
+        return equity < mm_req
+
+    def get_buying_power(self, position: BasePosition) -> float:
+        """Estimate buying power for new long positions assuming opening margin requirement.
+
+        Simplified: the maximum notional long value = equity / initial_margin_long.
+        """
+        equity = self.get_equity(position)
+        if self.initial_margin_long <= 0:
+            return 0.0
+        return equity / self.initial_margin_long
+
+    # --------------------------- Order / execution overrides ---------------------------
+    def _borrow_headroom(self, stock_id: str, current_short: float) -> float:
+        """Remaining borrowable shares for `stock_id` given an already-open short of `current_short` shares.
+
+        borrowable values: bool (True=unlimited, False=not borrowable) or numeric max shares.
+        Missing symbols follow `borrow_default` (True = unlimited when allow_short is enabled).
+        """
+        if not self.allow_short:
+            return 0.0
+        v = self.borrowable.get(stock_id, self.borrow_default)
+        if isinstance(v, bool):
+            return float("inf") if v else 0.0
+        try:
+            limit = float(v)
+        except (TypeError, ValueError):
+            return float("inf")
+        return max(0.0, limit - max(current_short, 0.0))
+
+    def _calc_trade_info_by_order(
+        self,
+        order: Order,
+        position: Optional[BasePosition],
+        dealt_order_amount: Dict[str, float],
+    ) -> Tuple[float, float, float]:
+        """Override to allow (optionally) short selling and to apply borrow fees.
+
+        The original Exchange implementation forbids selling more than you own. Here we allow
+        sell orders to create/expand short positions when allow_short is True. We still rely on
+        most base logic (price discovery, impact, cost calculation) by calling super(), but we
+        adjust the sell-side clipping behavior before delegating to the base implementation.
+        """
+        # When selling and shorts are allowed, temporarily relax the clipping logic in the base
+        # implementation by monkey-patching current position check. Simpler: replicate minimal
+        # parts of logic from Exchange._calc_trade_info_by_order with the key change.
+
+        # Get basic trade price & volume info using Exchange helpers
+        trade_price = float(self.get_deal_price(order.stock_id, order.start_time, order.end_time, direction=order.direction))
+        total_trade_val = float(self.get_volume(order.stock_id, order.start_time, order.end_time) or 0.0) * trade_price
+
+        order.factor = self.get_factor(order.stock_id, order.start_time, order.end_time)
+        order.deal_amount = order.amount  # attempt full
+
+        # volume clipping (same as base)
+        self._clip_amount_by_volume(order, dealt_order_amount)
+
+        # approximate adjusted cost ratio based on liquidity
+        if not total_trade_val or np.isnan(total_trade_val) or total_trade_val <= 0:
+            adj_cost_ratio = self.impact_cost
+        else:
+            trade_val_tmp = order.deal_amount * trade_price
+            adj_cost_ratio = self.impact_cost * (trade_val_tmp / total_trade_val) ** 2
+
+        # Differentiate buy / sell
+        if order.direction == Order.SELL:
+            cost_ratio = self.close_cost + adj_cost_ratio
+            current_amount = (
+                position.get_stock_amount(order.stock_id) if (position is not None and position.check_stock(order.stock_id)) else 0.0
+            )
+            long_held = max(current_amount, 0.0)
+            short_open = max(-current_amount, 0.0)
+
+            if position is not None:
+                if not self.allow_short:
+                    # clip by current holdings only
+                    if not np.isclose(order.deal_amount, current_amount):
+                        order.deal_amount = self.round_amount_by_trade_unit(
+                            min(long_held, order.deal_amount), order.factor
+                        )
+                else:
+                    # allow selling beyond holdings up to the remaining borrow limit;
+                    # later when updating the position we create/expand a short if necessary.
+                    max_sell = long_held + self._borrow_headroom(order.stock_id, short_open)
+                    if order.deal_amount > max_sell and not np.isclose(order.deal_amount, max_sell):
+                        order.deal_amount = self.round_amount_by_trade_unit(max_sell, order.factor)
+
+        elif order.direction == Order.BUY:
+            cost_ratio = self.open_cost + adj_cost_ratio
+            if position is not None:
+                cash = position.get_cash()
+                trade_val = order.deal_amount * trade_price
+                if cash < max(trade_val * cost_ratio, self.min_cost):
+                    order.deal_amount = 0
+                    self.logger.debug(f"Order clipped due to cost higher than cash: {order}")
+                elif cash < trade_val + max(trade_val * cost_ratio, self.min_cost):
+                    max_buy_amount = self._get_buy_amount_by_cash_limit(trade_price, cash, cost_ratio)
+                    order.deal_amount = self.round_amount_by_trade_unit(min(max_buy_amount, order.deal_amount), order.factor)
+                    self.logger.debug(f"Order clipped due to cash limitation: {order}")
+                else:
+                    order.deal_amount = self.round_amount_by_trade_unit(order.deal_amount, order.factor)
+            else:
+                order.deal_amount = self.round_amount_by_trade_unit(order.deal_amount, order.factor)
+        else:
+            raise NotImplementedError("order direction {} error".format(order.direction))
+
+        # compute final trade_val & trade_cost
+        trade_val = order.deal_amount * trade_price
+        # base trade_cost
+        trade_cost = max(trade_val * cost_ratio, self.min_cost)
+        # apply borrow fee only on the net-new short portion of the sell
+        if order.direction == Order.SELL and self.allow_short:
+            new_short = max(0.0, order.deal_amount - long_held)
+            trade_cost += new_short * trade_price * self.borrow_fee_rate
+
+        if trade_val <= 1e-5:
+            trade_cost = 0
+
+        return trade_price, trade_val, trade_cost
+
+    def deal_order(
+        self,
+        order: Order,
+        trade_account: Optional[Any] = None,
+        position: Optional[BasePosition] = None,
+        dealt_order_amount: Dict[str, float] = None,
+    ) -> Tuple[float, float, float]:
+        """Deal order and handle short position bookkeeping.
+
+        This method mirrors Exchange.deal_order but when a position is provided and shorts are
+        allowed it will update the Position.position dict directly to support negative amounts.
+        """
+        if dealt_order_amount is None:
+            dealt_order_amount = {}
+
+        if not self.check_order(order):
+            order.deal_amount = 0.0
+            self.logger.debug(f"Order failed due to trading limitation: {order}")
+            return 0.0, 0.0, np.nan
+
+        if trade_account is not None and position is not None:
+            raise ValueError("trade_account and position can only choose one")
+
+        pos = position or (trade_account.current_position if trade_account is not None else None)
+        trade_price, trade_val, trade_cost = self._calc_trade_info_by_order(order, pos, dealt_order_amount)
+
+        if trade_val > 1e-5:
+            if trade_account is not None:
+                cp = trade_account.current_position
+                if not cp.skip_update():
+                    held = cp.check_stock(order.stock_id)
+                    # Account-level bookkeeping (turnover/cost/returns). Mirrors
+                    # Account._update_state_from_order except for fresh short sales,
+                    # where no prior price exists to compute order profit from.
+                    if order.direction == Order.SELL and not held:
+                        trade_account.accum_info.add_turnover(trade_val)
+                        trade_account.accum_info.add_cost(trade_cost)
+                        trade_account.accum_info.add_return_value(0.0)
+                    if order.direction == Order.SELL:
+                        # sell: update account state first (stock entry may be deleted)
+                        if held:
+                            trade_account._update_state_from_order(order, trade_val, trade_cost, trade_price)
+                        self._position_sell(cp, order, trade_val, trade_cost, trade_price)
+                    else:
+                        # buy: update position first (entry may be created), then account state
+                        # A buy that covers a short to exactly flat deletes the entry inside
+                        # _position_buy; re-seed a transient zero-amount stub so the
+                        # account's order-profit lookup still finds the trade price,
+                        # then drop it (_update_state_from_order never mutates entries).
+                        sid = order.stock_id
+                        had_entry = isinstance(cp.position.get(sid), dict)
+                        self._position_buy(cp, order, trade_val, trade_cost, trade_price)
+                        covered_to_flat = had_entry and not isinstance(cp.position.get(sid), dict)
+                        if covered_to_flat:
+                            cp.position[sid] = {"amount": 0.0, "price": trade_price, "weight": 0}
+                        trade_account._update_state_from_order(order, trade_val, trade_cost, trade_price)
+                        if covered_to_flat:
+                            cp.position.pop(sid, None)
+            elif position is not None:
+                if order.direction == Order.BUY:
+                    self._position_buy(position, order, trade_val, trade_cost, trade_price)
+                else:
+                    self._position_sell(position, order, trade_val, trade_cost, trade_price)
+        return trade_val, trade_cost, trade_price
+
+    # --------------------------- Position mutation helpers ---------------------------
+    def _position_buy(self, position: BasePosition, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
+        """Handle buy order bookkeeping against a BasePosition while supporting shorts.
+
+        Rules implemented (simplified):
+        - If there is an existing short (amount < 0), the buy will first cover the short.
+        - If covering closes the short completely, the remaining buy becomes a long
+        - Cash updates mimic Position._buy_stock/_sell_stock (cash decreases by trade_val+cost for buys)
+        """
+        trade_amount = trade_val / trade_price
+        sid = order.stock_id
+        current_amount = position.get_stock_amount(sid) if position.check_stock(sid) else 0.0
+
+        # covering existing short
+        if current_amount < -1e-12:
+            # amount is negative -> we are short. Buying reduces the short.
+            new_amount = current_amount + trade_amount
+            if abs(new_amount) <= 1e-8:
+                # short fully covered exactly -> remove entry
+                if sid in position.position:
+                    del position.position[sid]
+            elif new_amount > 0:
+                # short fully covered with leftover buy amount -> leftover becomes a long position
+                position.position[sid] = {"amount": new_amount, "price": trade_price, "weight": 0}
+            else:
+                # partially cover
+                position.position[sid]["amount"] = new_amount
+                position.position[sid]["price"] = trade_price
+        else:
+            # normal or increasing long
+            if sid not in position.position or not isinstance(position.position[sid], dict):
+                # initialize stock
+                position.position[sid] = {"amount": trade_amount, "price": trade_price, "weight": 0}
+            else:
+                position.position[sid]["amount"] = position.position[sid].get("amount", 0.0) + trade_amount
+                position.position[sid]["price"] = trade_price
+
+        # cash effect same as Position._buy_stock
+        position.position["cash"] -= trade_val + cost
+
+    def _position_sell(self, position: BasePosition, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
+        """Handle sell order bookkeeping against a BasePosition while supporting shorts.
+
+        Rules implemented (simplified):
+        - If holding enough long shares, sell will reduce/close the long position normally.
+        - If not holding enough long shares and shorts are allowed, the remaining sold amount will create/expand a short position.
+        - Cash update for sells follows Position._sell_stock logic (cash increases by trade_val - cost)
+        """
+        trade_amount = trade_val / trade_price
+        sid = order.stock_id
+        current_amount = position.get_stock_amount(sid) if position.check_stock(sid) else 0.0
+
+        if current_amount > 1e-12:
+            # we have long shares; sell from them first
+            if trade_amount >= current_amount - 1e-8:
+                # selling all or more than holdings
+                # remove long position
+                if sid in position.position:
+                    del position.position[sid]
+                # remaining sold amount becomes short if allowed
+                remain = trade_amount - current_amount
+                if remain > 1e-8:
+                    if not self.allow_short:
+                        # should not happen due to clipping earlier, but guard anyway
+                        raise ValueError(f"Attempt to short {sid} while shorting disabled")
+                    # create short entry
+                    position.position[sid] = {"amount": -remain, "price": trade_price, "weight": 0}
+            else:
+                # partial sell
+                position.position[sid]["amount"] = current_amount - trade_amount
+                position.position[sid]["price"] = trade_price
+        else:
+            # currently flat or already short
+            if not self.allow_short:
+                raise ValueError(f"Attempt to short {sid} while shorting disabled")
+            # expand short
+            new_amount = current_amount - trade_amount
+            if sid not in position.position or not isinstance(position.position[sid], dict):
+                position.position[sid] = {"amount": new_amount, "price": trade_price, "weight": 0}
+            else:
+                position.position[sid]["amount"] = new_amount
+                position.position[sid]["price"] = trade_price
+
+        # cash effect same as Position._sell_stock
+        new_cash = trade_val - cost
+        if getattr(position, "_settle_type", None) == position.ST_CASH:
+            position.position["cash_delay"] = position.position.get("cash_delay", 0.0) + new_cash
+        else:
+            position.position["cash"] = position.position.get("cash", 0.0) + new_cash
+
+    # --------------------------- Borrow availability helpers ---------------------------
+    def is_borrowable(self, stock_id: str, amount: float) -> bool:
+        """Check whether the requested amount is borrowable for the given stock.
+
+        If a borrowable dict is provided, it may contain either booleans or numeric limits (maximum borrowable shares).
+        Symbols absent from the dict follow `borrow_default`.
+        """
+        if not self.allow_short:
+            return False
+        if stock_id not in self.borrowable:
+            return self.borrow_default
+        v = self.borrowable[stock_id]
+        if isinstance(v, bool):
+            return v
+        try:
+            limit = float(v)
+            return amount <= limit
+        except Exception:
+            return True
+

code/tac-qlib/tac_qlib/contrib/data/handler.py

@@ -15,6 +15,9 @@ import os
 from inspect import getfullargspec
 from typing import List, Optional, Tuple, Union
 
+import numpy as np
+import pandas as pd
+
 from qlib.data.dataset import processor as processor_module
 from qlib.data.dataset.handler import DataHandlerLP
 from qlib.utils import get_callable_kwargs
@@ -22,6 +25,7 @@ from qlib.utils import get_callable_kwargs
 from ...data.config import (
     LakeConfig,
     timeframe_for_freq,
+    FEATURE_FAMILIES,
     NON_FEATURE_COLUMNS,
 )
 
@@ -92,7 +96,7 @@ def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> Li
 
     common: set = set()
     # family tier: features/market=*/timeframe=*/family=*/symbol=*.parquet
-    for fam in ("ta", "sp"):
+    for fam in FEATURE_FAMILIES:
         fam_dir = feat_dir / f"family={fam}"
         if fam_dir.is_dir():
             common |= _family_common(fam_dir)
@@ -143,6 +147,174 @@ class DropAllNaN(processor_module.Processor):
         return df
 
 
+class BenchResidual(processor_module.Processor):
+    """Subtract a benchmark instrument's forward return from the label, per datetime.
+
+    Turns the training target from an absolute-return rank into a *residual* rank:
+    ``r_i - r_bench`` is ranked cross-sectionally by the downstream ``CSRankNorm`` /
+    ``CSZScoreNorm`` processors instead of ``r_i`` alone. Must be inserted BEFORE any
+    per-date normalization so the ranking itself is computed on residual returns
+    (ordering flips exactly where the benchmark trends).
+
+    Stateless: ``fit`` is a no-op and the benchmark forward return is recomputed from
+    the lake parquet on first ``__call__``. Rows whose benchmark value is missing are
+    left untouched. Accepts ``fit_start_time``/``fit_end_time`` (ignored) so
+    ``check_transform_proc`` can inject the fit window uniformly.
+
+    NOTE: under any cross-sectional normalization downstream (``CSRankNorm`` /
+    ``CSZScoreNorm``) this processor is a mathematical no-op: subtracting the same
+    per-date constant preserves ranks, and z-scoring absorbs constant shifts. Use
+    ``BenchBetaResidual`` for a target that actually reorders.
+    """
+
+    def __init__(
+        self,
+        benchmark="SPY",
+        fields_group="label",
+        lake_root=None,
+        market="US",
+        timeframe=None,
+        freq="day",
+        fit_start_time=None,
+        fit_end_time=None,
+    ):
+        self.benchmark = benchmark
+        self.fields_group = fields_group
+        self.lake_root = lake_root
+        self.market = market
+        self.timeframe = timeframe or timeframe_for_freq(freq)
+        self.fit_start_time = fit_start_time
+        self.fit_end_time = fit_end_time
+        self._bench_label = None
+
+    def _load_bench_label(self):
+        if self._bench_label is not None:
+            return self._bench_label
+        cfg = LakeConfig(self.lake_root, self.market)
+        p = cfg.bar_path(self.timeframe, self.benchmark)
+        if not p.exists():
+            raise FileNotFoundError(f"BenchResidual: benchmark bar file not found: {p}")
+        df = pd.read_parquet(p)
+        s = pd.Series(df["c"].astype(float).values, index=pd.to_datetime(df["t"])).sort_index()
+        s.index = s.index.normalize()
+        # mirror Ref($close,-6)/Ref($close,-1)-1 on the benchmark's own calendar
+        bench_label = s.shift(-6) / s.shift(-1) - 1
+        self._bench_label = bench_label[~bench_label.index.duplicated(keep="last")]
+        return self._bench_label
+
+    def fit(self, df=None):
+        return self
+
+    def __call__(self, df):
+        bl = self._load_bench_label()
+        cols = processor_module.get_group_columns(df, self.fields_group)
+        dt = df.index.get_level_values("datetime")
+        aligned = bl.reindex(pd.DatetimeIndex(dt.unique())).reindex(dt)
+        mask = aligned.notna().values
+        out = df.copy()
+        for c in cols:
+            vals = df[c].values
+            res = vals.copy()
+            res[mask] = np.asarray(vals[mask], dtype=float) - aligned[mask].values
+            out[c] = res
+        return out
+
+
+class BenchBetaResidual(processor_module.Processor):
+    """Residualize the label against a beta-scaled benchmark move: ``r_i - b_i * r_bench``.
+
+    Unlike a plain constant subtraction (see ``BenchResidual``), the name-specific rolling
+    beta ``b_i`` makes this survive cross-sectional normalization: in up-weeks high-beta
+    names lose rank, in down-weeks they gain — exactly the relative structure an absolute-
+    return ranking hides.
+
+    Beta is estimated from *past* data only (rolling ``window`` trading days of daily close
+    returns of each instrument vs the benchmark, both read up to and including ``t``), so
+    no lookahead enters the target. The benchmark leg uses the same horizon as the label
+    expression (``Ref($close,-6)/Ref($close,-1)-1`` by default via ``horizon``/``base``,
+    matching the yaml's 6-day label). Rows with missing beta or benchmark values keep
+    their raw label.
+
+    Requires ``$close`` to be present in the feature group (it always is for TACHandler).
+    Stateless; accepts ``fit_start_time``/``fit_end_time`` (ignored) for uniform kwargs
+    injection. Must be inserted BEFORE any per-date normalization processor.
+    """
+
+    def __init__(
+        self,
+        benchmark="SPY",
+        fields_group="label",
+        lake_root=None,
+        market="US",
+        timeframe=None,
+        freq="day",
+        window=63,
+        horizon=6,
+        base=1,
+        feature_field="$close",
+        fit_start_time=None,
+        fit_end_time=None,
+    ):
+        self.benchmark = benchmark
+        self.fields_group = fields_group
+        self.lake_root = lake_root
+        self.market = market
+        self.timeframe = timeframe or timeframe_for_freq(freq)
+        self.window = int(window)
+        self.horizon = int(horizon)
+        self.base = int(base)
+        self.feature_field = feature_field
+        self.fit_start_time = fit_start_time
+        self.fit_end_time = fit_end_time
+        self._bench = None
+
+    def _load_bench_close(self):
+        if self._bench is not None:
+            return self._bench
+        cfg = LakeConfig(self.lake_root, self.market)
+        p = cfg.bar_path(self.timeframe, self.benchmark)
+        if not p.exists():
+            raise FileNotFoundError(f"BenchBetaResidual: benchmark bar file not found: {p}")
+        df = pd.read_parquet(p)
+        s = pd.Series(df["c"].astype(float).values, index=pd.to_datetime(df["t"])).sort_index()
+        s.index = s.index.normalize()
+        self._bench = s[~s.index.duplicated(keep="last")]
+        return self._bench
+
+    def fit(self, df=None):
+        return self
+
+    def __call__(self, df):
+        bench = self._load_bench_close()
+
+        # benchmark forward return over the same horizon as the label expression
+        fwd = bench.shift(-(self.base + self.horizon - 1)) / bench.shift(-self.base) - 1
+
+        px_col = ("feature", self.feature_field)
+        if px_col not in df.columns:
+            raise KeyError(f"BenchBetaResidual: {self.feature_field} not found in features")
+        px = df[px_col].unstack("instrument").sort_index()
+        rets = px / px.shift(1) - 1
+        bret = bench.reindex(px.index).pct_change()
+
+        # rolling beta per instrument using data <= t (no lookahead)
+        cov = rets.rolling(self.window, min_periods=max(10, self.window // 2)).cov(bret)
+        var = bret.rolling(self.window, min_periods=max(10, self.window // 2)).var()
+        beta = cov.div(var, axis=0)
+
+        contrib = beta.mul(fwd.reindex(px.index), axis=0)
+        cols = list(processor_module.get_group_columns(df, self.fields_group))
+        out = df.copy()
+        for c in cols:
+            lab = df[c].unstack("instrument").reindex(px.index)
+            resid = lab - contrib.where(contrib.notna() & lab.notna(), 0.0)
+            new_vals = resid.stack()
+            new_vals.index.names = df.index.names
+            # residual where available, raw label otherwise (e.g. beta warm-up rows)
+            out[c] = new_vals.reindex(out.index).fillna(df[c])
+        return out
+
+
 class TACHandler(DataHandlerLP):
     """DataHandlerLP backed by the TradeAC parquet lake.
 
@@ -245,10 +417,12 @@ class TACHandler(DataHandlerLP):
         return get_common_feature_fields(lake_root, market, timeframe_for_freq(freq))
 
 
-__all__ = ["TACHandler", "DropAllNaN", "get_common_feature_fields"]
+__all__ = ["TACHandler", "DropAllNaN", "BenchResidual", "BenchBetaResidual", "get_common_feature_fields"]
 
 
-# Make `DropAllNaN` resolvable by bare name from processor configs (e.g. the default
-# ``infer_processors`` and workflow yamls that reference it without a ``module_path``),
-# mirroring how qlib registers its own processors in ``qlib.data.dataset.processor``.
+# Make `DropAllNaN`/`BenchResidual`/`BenchBetaResidual` resolvable by bare name from processor
+# configs (e.g. the default ``infer_processors`` and workflow yamls that reference them without a
+# ``module_path``), mirroring how qlib registers its own processors in ``qlib.data.dataset.processor``.
 processor_module.DropAllNaN = DropAllNaN
+processor_module.BenchResidual = BenchResidual
+processor_module.BenchBetaResidual = BenchBetaResidual

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

@@ -1,13 +1,4 @@
-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
+from .long_short import LongShortTopkStrategy  # noqa: F401
 
-__all__ = [
-    "OptimalStopControl",
-    "FractionalKellyDropoutStrategy",
-    "WeeklyRebalanceDropoutStrategy",
-    "TopBottomDropoutStrategy",
-    "RegimeGateDropoutStrategy",
-]
+__all__ = ["OptimalStopControl", "LongShortTopkStrategy"]

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

@@ -0,0 +1,361 @@
+"""Long-short Top-K strategy for cross-sectional signals.
+
+Each day the strategy ranks the cross-section by prediction score and rebalances
+to an equal-weight two-sided book: the ``topk`` highest-ranked names go long and
+the ``k_short`` lowest-ranked names go short. Net-new shorts are opened by
+selling beyond current holdings, which requires a short-aware exchange such as
+``tac_qlib.contrib.backtest.tradeac_exchange.TradeACExchange`` with
+``allow_short=True`` (borrow limits, margin requirements and borrow fees are
+enforced there, not here).
+
+Sizing deploys ``equity * risk_degree`` as gross notional split evenly across
+all long and short legs, so the book is approximately market neutral.
+``allow_short=False`` disables the short side entirely (long-only ``topk``).
+
+Short eligibility can be restricted further, with static or dynamic gates:
+``short_whitelist`` limits shorts to an explicit symbol set; ``short_vol_top_pct``
+requires a candidate's trailing realized volatility to rank in the top fraction
+of that day's cross-section; ``short_max_mom`` (falling-knife filter) only
+allows shorting names whose own trailing momentum is at/below a threshold;
+``short_regime_sma`` disables shorts entirely while the benchmark trades above
+its moving average (risk-on). Borrow availability itself is enforced by the
+exchange (``borrowable`` whitelist / per-symbol caps via ``TradeACExchange``).
+
+Wired into qrun workflows like any ``BaseStrategy`` (see ``PortAnaRecord``
+config). Mirrors the API usage of qlib's ``TopkDropoutStrategy``: ``Order`` /
+``OrderDir`` from ``qlib.backtest.decision``, ``trade_calendar`` /
+``trade_exchange`` / ``trade_position`` injected by the backtest executor.
+"""
+
+from __future__ import annotations
+
+import copy
+from typing import Dict, List, Optional
+
+import pandas as pd
+
+from qlib.backtest import Order
+from qlib.backtest.decision import OrderDir, TradeDecisionWO
+from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
+from qlib.log import get_module_logger
+
+__all__ = ["LongShortTopkStrategy"]
+
+
+class LongShortTopkStrategy(BaseSignalStrategy):
+    """Equal-weight long-short Top-K strategy over a cross-sectional signal.
+
+    Parameters
+    ----------
+    topk : number of long legs (highest-ranked names).
+    k_short : number of short legs (lowest-ranked names).
+    hold_thresh : minimum holding days before a leg may be closed/reduced.
+    only_tradable : only select candidates tradable on the trade date.
+    rebalance_tol : skip rebalances smaller than this fraction of a leg's
+        target notional (turnover control).
+    allow_short : enable/disable the short side. With ``False`` the bottom-ranked
+        legs are dropped and the book is long-only ``topk``; pair with
+        ``allow_short=False`` on the exchange for a fully borrow-free run.
+        Legacy alias ``enable_short`` is accepted.
+    short_whitelist : optional list of symbols eligible for shorting; candidates
+        outside the list are skipped (``None`` = all names eligible).
+    short_vol_window : trailing window (trading days) for realized-vol estimation.
+    short_vol_top_pct : if set, a short candidate's trailing realized volatility
+        must rank at or above this percentile of that day's cross-section
+        (e.g. ``0.5`` = only the more volatile half may be shorted). Candidates
+        without measurable vol are never shorted.
+    short_mom_window : trailing window (trading days) for the candidate momentum
+        used by the falling-knife gate.
+    short_max_mom : if set, a candidate's trailing ``short_mom_window``-day return
+        must be <= this value to be shortable (e.g. ``0.0`` = only short names
+        that are actually falling). Candidates without measurable momentum are
+        never shorted.
+    short_regime_symbol : benchmark symbol for the regime gate (default SPY).
+    short_regime_sma : if set, shorts are only allowed on days where the regime
+        symbol's last close (strictly before the execution bar) is BELOW its
+        ``short_regime_sma``-day moving average — i.e. shorts are disabled in
+        risk-on regimes and enabled in drawdowns.
+    """
+
+    def __init__(
+        self,
+        *,
+        signal=None,
+        topk: int = 4,
+        k_short: int = 2,
+        hold_thresh: int = 1,
+        only_tradable: bool = True,
+        rebalance_tol: float = 0.05,
+        allow_short: Optional[bool] = None,
+        enable_short: Optional[bool] = None,
+        short_whitelist: Optional[List[str]] = None,
+        short_vol_window: int = 20,
+        short_vol_top_pct: Optional[float] = None,
+        short_mom_window: int = 20,
+        short_max_mom: Optional[float] = None,
+        short_regime_symbol: str = "SPY",
+        short_regime_sma: Optional[int] = None,
+        risk_degree: float = 0.95,
+        trade_exchange=None,
+        level_infra=None,
+        common_infra=None,
+        **kwargs,
+    ):
+        super().__init__(
+            signal=signal,
+            risk_degree=risk_degree,
+            trade_exchange=trade_exchange,
+            level_infra=level_infra,
+            common_infra=common_infra,
+            **kwargs,
+        )
+        if allow_short is None:
+            allow_short = True if enable_short is None else bool(enable_short)
+        self.allow_short = bool(allow_short)
+        self.topk = topk
+        self.k_short = k_short
+        self.hold_thresh = hold_thresh
+        self.only_tradable = only_tradable
+        self.rebalance_tol = rebalance_tol
+        self.short_whitelist = set(short_whitelist) if short_whitelist is not None else None
+        if not 0 < float(short_vol_window) <= 1000:
+            raise ValueError(f"short_vol_window must be in (0, 1000], got {short_vol_window}")
+        self.short_vol_window = int(short_vol_window)
+        if short_vol_top_pct is not None and not 0.0 < float(short_vol_top_pct) <= 1.0:
+            raise ValueError(f"short_vol_top_pct must be in (0, 1], got {short_vol_top_pct}")
+        self.short_vol_top_pct = None if short_vol_top_pct is None else float(short_vol_top_pct)
+        if not 0 < float(short_mom_window) <= 1000:
+            raise ValueError(f"short_mom_window must be in (0, 1000], got {short_mom_window}")
+        self.short_mom_window = int(short_mom_window)
+        self.short_max_mom = None if short_max_mom is None else float(short_max_mom)
+        self.short_regime_symbol = str(short_regime_symbol)
+        if short_regime_sma is not None and not 1 < int(short_regime_sma) <= 1000:
+            raise ValueError(f"short_regime_sma must be in (1, 1000], got {short_regime_sma}")
+        self.short_regime_sma = None if short_regime_sma is None else int(short_regime_sma)
+        # per-day caches (keyed by trade date)
+        self._vol_cache_key: Optional[str] = None
+        self._vol_cache_val: Dict[str, Dict[str, float]] = {}
+        self._regime_cache: Dict[str, bool] = {}
+
+    # ------------------------------------------------------------------ utils
+    def _is_tradable(self, code, start, end) -> bool:
+        if not self.only_tradable:
+            return True
+        try:
+            return self.trade_exchange.is_stock_tradable(stock_id=code, start_time=start, end_time=end)
+        except TypeError:
+            return True
+
+    def _mark_price(self, code, start, end) -> Optional[float]:
+        try:
+            px = self.trade_exchange.get_deal_price(
+                stock_id=code, start_time=start, end_time=end, direction=OrderDir.BUY
+            )
+        except (KeyError, ValueError):
+            return None
+        if px is None or px != px or px <= 0:
+            return None
+        return float(px)
+
+    def _day_stats(self, codes: List[str], trade_start) -> Dict[str, Dict[str, float]]:
+        """Per-day cross-sectional stats used by the dynamic short gates.
+
+        For each code, returns ``{"vol_rank": r}`` (percentile of trailing
+        realized vol over ``short_vol_window`` bars across that day's
+        cross-section) when the vol gate is on, and ``{"mom": m}`` (trailing
+        ``short_mom_window``-bar return) when the falling-knife gate is on.
+        All series end on the last bar strictly BEFORE the execution bar (no
+        lookahead). Codes without measurable data are simply absent — such
+        candidates are never shorted (fail-closed).
+        """
+        if self.short_vol_top_pct is None and self.short_max_mom is None:
+            return {}
+        key = str(pd.Timestamp(trade_start))
+        if self._vol_cache_key == key:
+            return self._vol_cache_val
+        out: Dict[str, Dict[str, float]] = {}
+        try:
+            from qlib.data import D
+
+            end = pd.Timestamp(trade_start)
+            buf = max(self.short_vol_window, self.short_mom_window) * 3 + 30
+            df = D.features(
+                sorted(codes),
+                ["$close"],
+                start_time=end - pd.Timedelta(days=buf),
+                end_time=end - pd.Timedelta(days=1),
+            )
+            close = df["$close"].unstack(level="instrument") if isinstance(df.index, pd.MultiIndex) else df["$close"]
+            if self.short_vol_top_pct is not None:
+                vol = close.pct_change().rolling(self.short_vol_window).std().iloc[-1]
+                for code, rank in vol.rank(pct=True).dropna().items():
+                    out.setdefault(str(code), {})["vol_rank"] = float(rank)
+            if self.short_max_mom is not None:
+                w = min(self.short_mom_window, len(close) - 1)
+                mom = close.iloc[-1] / close.iloc[-(w + 1)] - 1
+                for code, m in mom.items():
+                    if m == m:
+                        out.setdefault(str(code), {})["mom"] = float(m)
+        except Exception as e:  # noqa: BLE001 - degrade to fail-closed (no shorts)
+            get_module_logger(self.__class__.__name__).warning(
+                f"short gates unavailable ({type(e).__name__}: {e}); no shorts this step"
+            )
+        self._vol_cache_key, self._vol_cache_val = key, out
+        return out
+
+    def _regime_ok(self, trade_start) -> bool:
+        """True when shorting is allowed by the benchmark-regime gate.
+
+        With ``short_regime_sma`` set, shorts are permitted only while the
+        regime symbol's last close strictly before the execution bar sits below
+        its moving average (risk-off). Data failure fails closed (no shorts).
+        """
+        if self.short_regime_sma is None:
+            return True
+        key = str(pd.Timestamp(trade_start))
+        cached = self._regime_cache.get(key)
+        if cached is not None:
+            return cached
+        ok = False
+        try:
+            from qlib.data import D
+
+            end = pd.Timestamp(trade_start)
+            df = D.features(
+                [self.short_regime_symbol],
+                ["$close"],
+                start_time=end - pd.Timedelta(days=int(self.short_regime_sma * 3 + 30)),
+                end_time=end - pd.Timedelta(days=1),
+            )
+            s = df["$close"]
+            if isinstance(s.index, pd.MultiIndex):
+                s = s.droplevel("instrument")
+            sma = s.rolling(self.short_regime_sma).mean().iloc[-1]
+            px = s.iloc[-1]
+            ok = bool(px < sma)
+        except Exception as e:  # noqa: BLE001 - degrade to fail-closed (no shorts)
+            get_module_logger(self.__class__.__name__).warning(
+                f"regime gate unavailable ({type(e).__name__}: {e}); no shorts this step"
+            )
+        self._regime_cache[key] = ok
+        return ok
+
+    # ------------------------------------------------------------- decision
+    def generate_trade_decision(self, execute_result=None):
+        trade_step = self.trade_calendar.get_trade_step()
+        trade_start, trade_end = self.trade_calendar.get_step_time(trade_step)
+        pred_start, pred_end = self.trade_calendar.get_step_time(trade_step, shift=1)
+        pred_score = self.signal.get_signal(start_time=pred_start, end_time=pred_end)
+        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)
+        pred_score = pred_score.dropna()
+        if pred_score.empty:
+            return TradeDecisionWO([], self)
+
+        time_per_step = self.trade_calendar.get_freq()
+        current_temp = copy.deepcopy(self.trade_position)
+
+        # ---- signed current holdings ---------------------------------------
+        cur_amount: Dict[str, float] = {}
+        for code in current_temp.get_stock_list():
+            amt = float(current_temp.get_stock_amount(code))
+            if abs(amt) > 1e-6:
+                cur_amount[code] = amt
+
+        # ---- targets: top-k long, bottom-k_short short ----------------------
+        ranked = list(pred_score.sort_values(ascending=False).index)
+        longs: List[str] = []
+        for code in ranked:
+            if len(longs) >= self.topk:
+                break
+            if self._is_tradable(code, trade_start, trade_end):
+                longs.append(code)
+        shorts: List[str] = []
+        if self.allow_short and self._regime_ok(trade_start):
+            stats = self._day_stats(list(ranked), trade_start)
+            for code in reversed(ranked):
+                if len(shorts) >= self.k_short:
+                    break
+                if code in longs:
+                    continue
+                if not self._is_tradable(code, trade_start, trade_end):
+                    continue
+                if self.short_whitelist is not None and code not in self.short_whitelist:
+                    continue
+                st = stats.get(code)
+                if self.short_vol_top_pct is not None:
+                    rank = None if st is None else st.get("vol_rank")
+                    if rank is None or rank < self.short_vol_top_pct:
+                        continue
+                if self.short_max_mom is not None:
+                    mom = None if st is None else st.get("mom")
+                    if mom is None or mom > self.short_max_mom:
+                        continue
+                shorts.append(code)
+
+        # ---- marks & equity --------------------------------------------------
+        marks: Dict[str, float] = {}
+        for code in set(cur_amount) | set(longs) | set(shorts):
+            px = self._mark_price(code, trade_start, trade_end)
+            if px is not None:
+                marks[code] = px
+
+        equity = current_temp.get_cash()
+        for code, amt in cur_amount.items():
+            if code in marks:
+                equity += amt * marks[code]
+        if equity <= 0:
+            return TradeDecisionWO([], self)
+
+        n_legs = len([c for c in longs if c in marks]) + len([c for c in shorts if c in marks])
+        if n_legs == 0:
+            return TradeDecisionWO([], self)
+        per_leg = equity * self.risk_degree / n_legs
+
+        target_signed: Dict[str, float] = {}
+        for code in longs:
+            if code in marks:
+                target_signed[code] = per_leg / marks[code]
+        for code in shorts:
+            if code in marks:
+                target_signed[code] = -(per_leg / marks[code])
+
+        # ---- order generation -------------------------------------------------
+        sell_orders: List[Order] = []
+        buy_orders: List[Order] = []
+
+        def submit(code: str, amount: float, direction: int) -> None:
+            factor = self.trade_exchange.get_factor(stock_id=code, start_time=trade_start, end_time=trade_end)
+            amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
+            if amount <= 1e-6:
+                return
+            o = Order(stock_id=code, amount=amount, start_time=trade_start, end_time=trade_end, direction=direction)
+            if self.trade_exchange.check_order(o):
+                (buy_orders if direction == Order.BUY else sell_orders).append(o)
+
+        # close holdings that are no longer targeted (frees cash / unwinds shorts)
+        for code, amt in cur_amount.items():
+            if code in target_signed:
+                continue
+            if marks.get(code) is None:
+                continue
+            if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
+                continue
+            submit(code, abs(amt), Order.SELL if amt > 0 else Order.BUY)
+
+        # rebalance targeted legs toward their signed target quantity
+        for code, tgt in target_signed.items():
+            cur = cur_amount.get(code, 0.0)
+            delta = tgt - cur
+            if abs(delta * marks[code]) < max(self.rebalance_tol * per_leg, 1.0):
+                continue
+            if delta > 0:
+                submit(code, delta, Order.BUY)
+            else:
+                if cur > 0 and current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
+                    continue
+                submit(code, -delta, Order.SELL)
+
+        return TradeDecisionWO(sell_orders + buy_orders, 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

@@ -37,11 +37,20 @@ class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
     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).
+    rebalance_every_n_weeks : rebalance every N ISO weeks instead of every week
+        (default 1 = weekly; 2 = biweekly).  Ignored when
+        ``rebalance_every_n_days`` is set.
+    rebalance_every_n_days : rebalance every N trading days (daily when N=1).
+        When set, overrides the weekly gating logic entirely.
     """
 
-    def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT, **kwargs):
+    def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT,
+                 rebalance_every_n_weeks: int = 1,
+                 rebalance_every_n_days: int = 0, **kwargs):
         super().__init__(topk=topk, n_drop=n_drop, **kwargs)
         self.hold_band_pct = hold_band_pct
+        self.rebalance_every_n_weeks = rebalance_every_n_weeks
+        self.rebalance_every_n_days = rebalance_every_n_days
 
     @staticmethod
     def _iso_week(ts) -> tuple:
@@ -53,12 +62,24 @@ class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
         trade_step = self.trade_calendar.get_trade_step()
         trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
 
+        if self.rebalance_every_n_days > 0:
+            # daily gating: count trading steps since last rebalance
+            step_num = trade_step
+            if hasattr(self, "_last_rebal_step"):
+                if (step_num - self._last_rebal_step) < self.rebalance_every_n_days:
+                    return TradeDecisionWO([], self)
+            self._last_rebal_step = step_num
+        else:
             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)
+
+            if self.rebalance_every_n_weeks > 1:
+                week_num = cur_week[1]
+                if prev_week is not None and (week_num % self.rebalance_every_n_weeks) != 1:
                     return TradeDecisionWO([], self)
 
         pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)

code/tac-qlib/tac_qlib/data/config.py

@@ -61,6 +61,11 @@ UNKNOWN_FIELD_NAMES = ("factor", "change", "trade_unit", "suspend_flag")
 #: columns in the parquet files that are not features
 NON_FEATURE_COLUMNS = ("t", "date", "market", "timeframe", "symbol")
 
+#: Feature-family partitions merged by ``LakeConfig.load_features`` and scanned
+#: by the handler's field discovery. ``macro`` holds broadcast market-state
+#: columns (see skills/tac-qlib-custom/examples/persist_macro_broadcast.py).
+FEATURE_FAMILIES = ("ta", "sp", "macro")
+
 
 def timeframe_for_freq(freq: str) -> str:
     """Map a qlib frequency (e.g. ``day``, ``1min``) to a lake timeframe (e.g. ``1d``)."""
@@ -113,12 +118,12 @@ class LakeConfig:
         return self.features_dir(timeframe) / f"symbol={str(symbol).upper()}.parquet"
 
     def load_features(self, timeframe: str, symbol: str) -> pd.DataFrame:
-        """All feature columns for a symbol, merging the `family=ta` and
-        `family=sp` partitions by timestamp. Returns an empty frame when no
+        """All feature columns for a symbol, merging the `family=ta|sp|macro`
+        partitions by timestamp. Returns an empty frame when no
         feature files exist (legacy flat layout falls back transparently)."""
         sym = str(symbol).upper()
         frames = []
-        for family in ("ta", "sp"):
+        for family in FEATURE_FAMILIES:
             p = self.features_dir(timeframe) / f"family={family}" / f"symbol={sym}.parquet"
             if p.exists():
                 frames.append(pd.read_parquet(p))

evidence/q08-risklimit/RESULT.md

@@ -1,35 +0,0 @@
-# Q08 — Risk-limit A/B re-validation (trace 40)
-
-**Status:** DONE (verdict: REFUTED as an IR edge; safety-net value retained)
-
-## Input
-- Reference signal: exp-26 pred, run `21afc6afdb674a399b59dd76c97628ce` (mlflow exp 25)
-- Window: 2026-01-04 → 2026-08-10, Topk10 n_drop1, SPY benchmark, $1M, 5/15bp/$5
-- Tool: `rd_risk_calibrate` (A/B + sensitivity grid). Full JSON: `risk_calibration.json`
-
-## Candidate spec (round-3 live spec)
-`{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12, "concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}`
-
-## Results (net, with cost)
-| Config | IR | Ann. return | Max DD |
-|---|---|---|---|
-| baseline (no limits) | 1.5804 | +27.50% | −6.91% |
-| **candidate (5M floor + caps)** | **1.5121** | +2.20% | **−0.65%** |
-| liquidity $10M | 1.5457 | +2.25% | −0.64% |
-
-## Findings
-- **Floor binds, not a no-op**: $5M liquidity floor dropped 8 symbols —
-  `DBA, DBC, ESPO, FDN, REM, TAN, UNG, XAR`.
-- **No IR edge from the gate**: candidate IR (1.512) is BELOW baseline (1.580).
-  The exp-18 direction (floor IR 0.81→0.98) does NOT reproduce on the clean-lake
-  reference signal.
-- **Drawdown cut is pure defunding**: size_cap 0.12 × concentration 0.95 fold
-  the effective risk_degree to ~0.0095 → ~$9.5k deployed of $1M (~100x less).
-  Sensitivity grid shows both caps are no-ops (conc 20–50% identical,
-  size_cap 5–20% identical); only the liquidity floor moves returns, marginally.
-- **Conclusion**: keep the live spec as a safety net; there is no risk-limit
-  gate IR edge to harvest when the signal is the bottleneck (exp-20 pattern).
-
-## Artifacts on this branch
-- `evidence/q08-risklimit/risk_calibration.json` — full calibration dump
-- `queue/designs/q08_risk_limit_ab.md` — the pre-registered design doc

evidence/q08-risklimit/risk_calibration.json

@@ -1,401 +0,0 @@
-{
-  "rows": [
-    {
-      "label": "baseline (no limits)",
-      "mean": 0.001155,
-      "std": 0.011279,
-      "annualized_return": 0.274989,
-      "information_ratio": 1.580427,
-      "max_drawdown": -0.069145
-    },
-    {
-      "label": "liquidity $10,000,000",
-      "mean": 9.4e-05,
-      "std": 0.000942,
-      "annualized_return": 0.022464,
-      "information_ratio": 1.545736,
-      "max_drawdown": -0.006389
-    },
-    {
-      "label": "conc 20%",
-      "mean": 0.000115,
-      "std": 0.001168,
-      "annualized_return": 0.027285,
-      "information_ratio": 1.513718,
-      "max_drawdown": -0.008104
-    },
-    {
-      "label": "conc 30%",
-      "mean": 0.000115,
-      "std": 0.001168,
-      "annualized_return": 0.027285,
-      "information_ratio": 1.513718,
-      "max_drawdown": -0.008104
-    },
-    {
-      "label": "conc 40%",
-      "mean": 0.000115,
-      "std": 0.001168,
-      "annualized_return": 0.027285,
-      "information_ratio": 1.513718,
-      "max_drawdown": -0.008104
-    },
-    {
-      "label": "conc 50%",
-      "mean": 0.000115,
-      "std": 0.001168,
-      "annualized_return": 0.027285,
-      "information_ratio": 1.513718,
-      "max_drawdown": -0.008104
-    },
-    {
-      "label": "candidate {\"liquidity_floor_adv\": 5000000.0, \"size_cap_pct\": 0.12, \"concentration_cap_pct\": 0.95, \"drawdown_pause_pct\": 0.1}",
-      "mean": 9.2e-05,
-      "std": 0.000943,
-      "annualized_return": 0.021991,
-      "information_ratio": 1.512051,
-      "max_drawdown": -0.00653
-    },
-    {
-      "label": "size_cap 5%",
-      "mean": 9.2e-05,
-      "std": 0.000943,
-      "annualized_return": 0.021991,
-      "information_ratio": 1.512051,
-      "max_drawdown": -0.00653
-    },
-    {
-      "label": "size_cap 10%",
-      "mean": 9.2e-05,
-      "std": 0.000943,
-      "annualized_return": 0.021991,
-      "information_ratio": 1.512051,
-      "max_drawdown": -0.00653
-    },
-    {
-      "label": "size_cap 15%",
-      "mean": 9.2e-05,
-      "std": 0.000943,
-      "annualized_return": 0.021991,
-      "information_ratio": 1.512051,
-      "max_drawdown": -0.00653
-    },
-    {
-      "label": "size_cap 20%",
-      "mean": 9.2e-05,
-      "std": 0.000943,
-      "annualized_return": 0.021991,
-      "information_ratio": 1.512051,
-      "max_drawdown": -0.00653
-    },
-    {
-      "label": "liquidity $5,000,000",
-      "mean": 9.2e-05,
-      "std": 0.000943,
-      "annualized_return": 0.021991,
-      "information_ratio": 1.512051,
-      "max_drawdown": -0.00653
-    },
-    {
-      "label": "liquidity $1,000,000",
-      "mean": 9.1e-05,
-      "std": 0.000929,
-      "annualized_return": 0.021625,
-      "information_ratio": 1.508748,
-      "max_drawdown": -0.006376
-    },
-    {
-      "label": "liquidity $2,500,000",
-      "mean": 7.1e-05,
-      "std": 0.000918,
-      "annualized_return": 0.017,
-      "information_ratio": 1.199721,
-      "max_drawdown": -0.007158
-    }
-  ],
-  "runs": {
-    "baseline": {
-      "risk": {
-        "mean": 0.0011554172081987572,
-        "std": 0.01127853762493476,
-        "annualized_return": 0.27498929555130425,
-        "information_ratio": 1.5804272791471323,
-        "max_drawdown": -0.06914515336341577
-      },
-      "applied": {}
-    },
-    "candidate": {
-      "risk": {
-        "mean": 9.239707947451976e-05,
-        "std": 0.0009427144352738658,
-        "annualized_return": 0.0219905049149357,
-        "information_ratio": 1.5120514373488407,
-        "max_drawdown": -0.006530482262119444
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "size_cap 5%": {
-      "risk": {
-        "mean": 9.239707947451976e-05,
-        "std": 0.0009427144352738658,
-        "annualized_return": 0.0219905049149357,
-        "information_ratio": 1.5120514373488407,
-        "max_drawdown": -0.006530482262119444
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "size_cap 10%": {
-      "risk": {
-        "mean": 9.239707947451976e-05,
-        "std": 0.0009427144352738658,
-        "annualized_return": 0.0219905049149357,
-        "information_ratio": 1.5120514373488407,
-        "max_drawdown": -0.006530482262119444
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "size_cap 15%": {
-      "risk": {
-        "mean": 9.239707947451976e-05,
-        "std": 0.0009427144352738658,
-        "annualized_return": 0.0219905049149357,
-        "information_ratio": 1.5120514373488407,
-        "max_drawdown": -0.006530482262119444
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "size_cap 20%": {
-      "risk": {
-        "mean": 9.239707947451976e-05,
-        "std": 0.0009427144352738658,
-        "annualized_return": 0.0219905049149357,
-        "information_ratio": 1.5120514373488407,
-        "max_drawdown": -0.006530482262119444
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "conc 20%": {
-      "risk": {
-        "mean": 0.00011464156491316718,
-        "std": 0.0011683839517000441,
-        "annualized_return": 0.027284692449333788,
-        "information_ratio": 1.5137180903503433,
-        "max_drawdown": -0.008103887185240407
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "conc 30%": {
-      "risk": {
-        "mean": 0.00011464156491316718,
-        "std": 0.0011683839517000441,
-        "annualized_return": 0.027284692449333788,
-        "information_ratio": 1.5137180903503433,
-        "max_drawdown": -0.008103887185240407
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "conc 40%": {
-      "risk": {
-        "mean": 0.00011464156491316718,
-        "std": 0.0011683839517000441,
-        "annualized_return": 0.027284692449333788,
-        "information_ratio": 1.5137180903503433,
-        "max_drawdown": -0.008103887185240407
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "conc 50%": {
-      "risk": {
-        "mean": 0.00011464156491316718,
-        "std": 0.0011683839517000441,
-        "annualized_return": 0.027284692449333788,
-        "information_ratio": 1.5137180903503433,
-        "max_drawdown": -0.008103887185240407
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "liquidity $1,000,000": {
-      "risk": {
-        "mean": 9.086210454881382e-05,
-        "std": 0.0009290831160004576,
-        "annualized_return": 0.021625180882617688,
-        "information_ratio": 1.508747982736451,
-        "max_drawdown": -0.006376134679664126
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "ESPO"
-        ]
-      }
-    },
-    "liquidity $2,500,000": {
-      "risk": {
-        "mean": 7.142665167642606e-05,
-        "std": 0.0009184775632266332,
-        "annualized_return": 0.016999543098989402,
-        "information_ratio": 1.1997208834083914,
-        "max_drawdown": -0.0071582979845040825
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "REM",
-          "XAR"
-        ]
-      }
-    },
-    "liquidity $5,000,000": {
-      "risk": {
-        "mean": 9.239707947451976e-05,
-        "std": 0.0009427144352738658,
-        "annualized_return": 0.0219905049149357,
-        "information_ratio": 1.5120514373488407,
-        "max_drawdown": -0.006530482262119444
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "REM",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    },
-    "liquidity $10,000,000": {
-      "risk": {
-        "mean": 9.438545151345752e-05,
-        "std": 0.0009420158170657147,
-        "annualized_return": 0.02246373746020289,
-        "information_ratio": 1.5457360696934006,
-        "max_drawdown": -0.006388809561209335
-      },
-      "applied": {
-        "dropped_liquidity": [
-          "DBA",
-          "DBC",
-          "ESPO",
-          "FDN",
-          "ICLN",
-          "ITA",
-          "MDY",
-          "REM",
-          "SHY",
-          "TAN",
-          "UNG",
-          "XAR"
-        ]
-      }
-    }
-  },
-  "candidate": {
-    "liquidity_floor_adv": 5000000.0,
-    "size_cap_pct": 0.12,
-    "concentration_cap_pct": 0.95,
-    "drawdown_pause_pct": 0.1
-  }
-}

queue/designs/q08_risk_limit_ab.md

@@ -1,34 +0,0 @@
-# QUEUE-08 — Risk-limit A/B re-validation: $5M liquidity floor on the exp-26 reference
-
-**Status:** QUEUED · **Priority:** P1 · **Effort:** tool-only (no new code)
-
-## Hypothesis (prove)
-The $5M liquidity floor improves net IR and cuts drawdown on the **post-reset**
-reference signal (pre-reset exp 18, EVIDENCE#008: net IR 0.81→0.98, cumDD
-7.93%→5.44%), while size/concentration caps hurt by cutting deployed capital.
-Needs re-validation on the exp-26 lineage because exp 18 is pre-clean-lake and
-not comparable (EVIDENCE#009/010). Source: `book/CLAIMS.md` open question +
-`book/README.md` `TODO(evidence-needed: reconciliation of exp 18 risk-limit spec
-on the post-reset reference signal)`.
-
-## Change vs exp-26 reference (ONE variable)
-- Reference: the saved exp-26 prediction (run `21afc6af…`, mlflow exp 25).
-- A/B via `rd_risk_calibrate` (runs limit-vs-no-limit A/B + sensitivity grid
-  over size_cap_pct, concentration_cap_pct, liquidity_floor_adv) and/or
-  `rd_backtest` with `risk_limits` on the SAME saved `pred.pkl`:
-  - baseline: no limits (this must reproduce the exp-26 net +2.13% / IR 0.21);
-  - candidate: `{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12,
-    "concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}` (round-3 spec).
-- Pick the spec (B2 calibration) that keeps live ≈ backtest.
-
-## Acceptance
-- Candidate spec: `net_IR > 0.21` AND `net_max_drawdown < 7.69%` vs no-limit on
-  the same pred. Size/concentration caps expected to REDUCE deployed capital
-  (record the direction as confirmation of exp 18).
-- If the floor is a no-op (gates don't bind at this signal) → report that gates
-  are no-ops when the signal is the bottleneck (exp 20 pattern) as a PROVEN
-  clean-lake result.
-
-## Execution prerequisites
-- None (uses saved pred + `rd_risk_calibrate`/`rd_backtest`). Trace the A/B as
-  an experiment; record the spec chosen for the next live round.

workflows/ablate_baseline_all_sp_fields.yaml

@@ -1,133 +0,0 @@
-# -----------------------------------------------------------------------------
-# 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

@@ -1,134 +0,0 @@
-# -----------------------------------------------------------------------------
-# 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

@@ -1,141 +0,0 @@
-# -----------------------------------------------------------------------------
-# 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/exp18-risk-limit/a_baseline.yaml

@@ -1,141 +0,0 @@
-# -----------------------------------------------------------------------------
-# EXP 18 - Risk-limit control: reference model + TopkDropout baseline (A).
-#
-# Signal/model identical to the reference (tac-rd-rank-ensemble-isolated,
-# run 0cea66d9...): RankICEnsembleLGBModel (parallel, 5 seeds) on the 50-ETF
-# SP-5d panel, test 2026-01-04..2026-08-10. This workflow reproduces the
-# unconstrained TopkDropout baseline net-of-cost so the risk-limited variant
-# (same pred, liquidity/size/concentration caps) can be compared 1:1.
-#
-# The risk_limits spec itself is applied via rd_backtest / rd_strategy_targets
-# (tool-level param, not a YAML key); this run records the unconstrained
-# baseline that the limit A/B is measured against.
-#
-# Run:
-#   rd_run_workflow config_path=experiments/workflows/exp18-risk-limit/a_baseline.yaml \
-#       experiment_name=tac-rd-risk-limit
-# -----------------------------------------------------------------------------
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
-
-qlib_init:
-    provider_uri: "{{ LAKE }}"
-    region: us
-    expression_cache: null
-    dataset_cache: null
-
-    calendar_provider:
-        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs:
-            lake_root: "{{ LAKE }}"
-            market: US
-    instrument_provider:
-        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs:
-            lake_root: "{{ LAKE }}"
-            market: US
-            markets: {}
-    feature_provider:
-        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs:
-            lake_root: "{{ LAKE }}"
-            market: US
-
-    exp_manager:
-        class: MLflowExpManager
-        module_path: qlib.workflow.expm
-        kwargs:
-            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-risk-limit"
-
-task:
-    model:
-        class: RankICEnsembleLGBModel
-        module_path: tac_qlib.contrib.model.rank_ensemble
-        kwargs:
-            loss: mse
-            learning_rate: 0.02
-            num_leaves: 31
-            n_estimators: 3000
-            num_boost_round: 3000
-            early_stopping_rounds: 200
-            min_data_in_leaf: 20
-            lambda_l2: 0.5
-            colsample_bytree: 0.8
-            subsample: 0.8
-            subsample_freq: 1
-            reg_alpha: 0.1
-            reg_lambda: 1.0
-            seeds: "42,7,2026,99,123"
-            parallel: 5
-
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: TACHandler
-                module_path: tac_qlib.contrib.data.handler
-                kwargs:
-                    instruments: "{{ UNIVERSE }}"
-                    start_time: 2015-01-03
-                    end_time: 2026-08-14
-                    fit_start_time: 2016-01-04
-                    fit_end_time: 2025-09-01
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
-                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
-            segments:
-                train: [2016-01-04, 2025-09-01]
-                valid: [2025-09-03, 2026-01-03]
-                test: [2026-01-04, 2026-08-10]
-
-    record:
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: {}
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              ana_long_short: true
-              ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: 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