start experiment 76 (exp/76-scheduled-algo-retrain-on-2026-09-16-tac)

code/MANIFEST.txt

@@ -1,34 +1,25 @@
 # TradeAC custom-qlib-code snapshot (auto-generated)
-# parent repo HEAD : ce2e0c1a7cf2277108b3c6e469e225579f9cf259
+# parent repo HEAD : f9c8fbe9459f8f9738c502f96ebc34ec1b347367
 #   tac-qlib/tac_qlib/contrib
 #   tac-qlib/tac_qlib/data
 # per-file hashes (git hash-object):
   1b6298c4a5652f2e863cbdc385a1014a570fcd59  tac-qlib/tac_qlib/contrib/__init__.py
-  6c57851807631dfa1a525f87538a1b0a495fd7b2  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
-  1acd2cb845eac1bcee54450004a4af36484544ed  tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
-  2ef18965f77e8955580334d2edc09bd381204355  tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
   3bba0f1696e4ab4b3deebec3f31f269b2e713899  tac-qlib/tac_qlib/contrib/data/handler.py
   b151d139a0dcde87d74b21e7c4b729176ba5c39b  tac-qlib/tac_qlib/contrib/model/__init__.py
-  c975d2b978f2cc08a388a5d921938704a3dd592d  tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
-  009ebd83c5156ca3d7039a112e0d277dd416ca86  tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
-  1716b680b5623394229f7600ad4c81ad07fa6a2b  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
   184f80da8edf944bad3c8fb4d4d3d189bf4f082b  tac-qlib/tac_qlib/contrib/strategy/__init__.py
-  9c9f7743970b1a3827bb72768bb6e8be03040759  tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
-  6e38a7fa8584b80410ccc88e5feff228a7ece38b  tac-qlib/tac_qlib/contrib/strategy/__pycache__/long_short.cpython-312.pyc
-  f983d5c2472cd16ef9f14a240674ec0a7f41e81c  tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
   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
-  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
-  a0e969bd6504bb8d9220f4641cc01e960c3120e4  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
-  2f8c537d11135155539276bee342d087aad8743e  tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
-  53cf7828c425f6b5032b206b93a238607111a6ed  tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
+  6963ba16f49411450562d381fd6ba89e4a7b3e28  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
+  fb3c8faf2701e3e2e5abf9a8958f2684783fbd74  tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
+  67cc6fa7b0fe0a87b1882dd736123ffead6a5163  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/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)