start experiment 61 (exp/61-scheduled-algo-retrain-on-2026-08-24-tac)

code/MANIFEST.txt

@@ -1,34 +1,34 @@
 # TradeAC custom-qlib-code snapshot (auto-generated)
-# parent repo HEAD : f1bd6d99c323c5939e4baf03e108e93b0f6caade
+# parent repo HEAD : ee5aa8e73c5286860d3ec1dfa6001bf0f8b6690d
 #   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
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   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
+  a5c6952e9c8bab2c83fd8030416999726aea73d9  tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
-  2ef18965f77e8955580334d2edc09bd381204355  tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
+  39f148bef96fcce8f548d74c6d338eccdc4145e3  tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
-  3bba0f1696e4ab4b3deebec3f31f269b2e713899  tac-qlib/tac_qlib/contrib/data/handler.py
+  fc3d01d530d4e84b3386614b6ac94324e811920e  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
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-  009ebd83c5156ca3d7039a112e0d277dd416ca86  tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
+  858f5fcbe285642569c214d5803edbae1e5dfa1e  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
+  7de125f7f263c554b3337138c760d44394b4824a  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
+  6893c2097f0de110909f3f5076ac1c32ac5b742b  tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
-  6e38a7fa8584b80410ccc88e5feff228a7ece38b  tac-qlib/tac_qlib/contrib/strategy/__pycache__/long_short.cpython-312.pyc
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   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
   92e6e90eb0cd0a25142034560f27adb6b705b1a8  tac-qlib/tac_qlib/data/__init__.py
-  a0e969bd6504bb8d9220f4641cc01e960c3120e4  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
+  fa1090eb6f17b1b7835038e35fac86334294d165  tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
-  2f8c537d11135155539276bee342d087aad8743e  tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
+  48629a1f51bd4058036ce78d79ed49454982c051  tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
-  53cf7828c425f6b5032b206b93a238607111a6ed  tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
+  ad7c9b726c9c52773fa82031fcd681337be97fa8  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/data/handler.py

@@ -15,9 +15,6 @@ 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
@@ -147,174 +144,6 @@ 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.
 
@@ -417,12 +246,10 @@ class TACHandler(DataHandlerLP):
         return get_common_feature_fields(lake_root, market, timeframe_for_freq(freq))
 
 
-__all__ = ["TACHandler", "DropAllNaN", "BenchResidual", "BenchBetaResidual", "get_common_feature_fields"]
+__all__ = ["TACHandler", "DropAllNaN", "get_common_feature_fields"]
 
 
-# Make `DropAllNaN`/`BenchResidual`/`BenchBetaResidual` resolvable by bare name from processor
+# Make `DropAllNaN` resolvable by bare name from processor configs (e.g. the default
-# configs (e.g. the default ``infer_processors`` and workflow yamls that reference them without a
+# ``infer_processors`` and workflow yamls that reference it without a ``module_path``),
-# ``module_path``), mirroring how qlib registers its own processors in ``qlib.data.dataset.processor``.
+# mirroring how qlib registers its own processors in ``qlib.data.dataset.processor``.
 processor_module.DropAllNaN = DropAllNaN
-processor_module.BenchResidual = BenchResidual
-processor_module.BenchBetaResidual = BenchBetaResidual

queue/workflows/q01_m2_sharpe22_repro.yaml

@@ -1,140 +0,0 @@
-# -----------------------------------------------------------------------------
-# QUEUE-01 — M2 reproduction: risk-adjusted 22d Sharpe drift (sp_sharpe_22).
-#
-# Hypothesis (book ch.01/ch.07, EVIDENCE#018 -> exp 30): adding the
-# risk-adjusted 22d Sharpe drift feature (sp_sharpe_22) to the compact
-# stochastic reference IMPROVES net portfolio performance (exp 30: net +6.53%
-# IR 0.62 vs reference +2.13% IR 0.21) while rank metrics dip (RankIC 0.0576 vs
-# 0.0663). exp 30 is a SINGLE clean-lake run, unreproduced -> HYPOTHESIS.
-#
-# Change vs exp-26 reference (EVIDENCE#015, run 21afc6af...): ONE feature added,
-# feature_fields = compact set + sp_sharpe_22. Everything else byte-identical.
-#
-# Acceptance: net_ann_return > +2.13% AND net_IR > 0.21 (else HYPOTHESIS -> REFUTED).
-# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q01_m2_sharpe22_repro.yaml \
-#        experiment_name=tac-rd-q01-m2-sharpe22-repro
-# -----------------------------------------------------------------------------
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22" %}
-
-qlib_init:
-    provider_uri: "{{ LAKE }}"
-    region: us
-    expression_cache: null
-    dataset_cache: null
-
-    calendar_provider:
-        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs:
-            lake_root: "{{ LAKE }}"
-            market: US
-    instrument_provider:
-        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs:
-            lake_root: "{{ LAKE }}"
-            market: US
-            markets: {}
-    feature_provider:
-        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs:
-            lake_root: "{{ LAKE }}"
-            market: US
-
-    exp_manager:
-        class: MLflowExpManager
-        module_path: qlib.workflow.expm
-        kwargs:
-            uri: "sqlite:///{{ LAKE }}/mlruns.db"
-            default_exp_name: "tac-rd-q01-m2-sharpe22-repro"
-
-task:
-    model:
-        class: RankICEnsembleLGBModel
-        module_path: tac_qlib.contrib.model.rank_ensemble
-        kwargs:
-            loss: mse
-            learning_rate: 0.02
-            num_leaves: 31
-            n_estimators: 3000
-            num_boost_round: 3000
-            early_stopping_rounds: 200
-            min_data_in_leaf: 20
-            lambda_l2: 0.5
-            colsample_bytree: 0.8
-            subsample: 0.8
-            subsample_freq: 1
-            reg_alpha: 0.1
-            reg_lambda: 1.0
-            seeds: "42,7,2026,99,123"
-            parallel: 5
-
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: TACHandler
-                module_path: tac_qlib.contrib.data.handler
-                kwargs:
-                    instruments: "{{ UNIVERSE }}"
-                    start_time: 2015-01-03
-                    end_time: 2026-08-10
-                    fit_start_time: 2016-01-04
-                    fit_end_time: 2025-09-01
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "{{ FEATURES }}"
-                    infer_processors:
-                        - class: DropAllNaN
-                          kwargs: {}
-                        - class: ProcessInf
-                          kwargs: {}
-                        - class: CSRankNorm
-                          kwargs: {}
-                        - class: ZScoreNorm
-                          kwargs: {}
-                        - class: Fillna
-                          kwargs: {}
-            segments:
-                train: [2016-01-04, 2025-09-01]
-                valid: [2025-09-03, 2026-01-03]
-                test: [2026-01-04, 2026-08-10]
-
-    record:
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: {}
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              ana_long_short: true
-              ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: TopkDropoutStrategy
-                      module_path: qlib.contrib.strategy
-                      kwargs:
-                          signal: "<PRED>"
-                          topk: 10
-                          n_drop: 1
-                          only_tradable: true
-                          risk_degree: 0.95
-                  backtest:
-                      start_time: 2026-01-04
-                      end_time: 2026-08-10
-                      account: 1000000
-                      benchmark: SPY
-                      exchange_kwargs:
-                          codes: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d

workflows/exp30/workflow.yaml

@@ -1,99 +0,0 @@
-# M2 isolation run: base compact set + risk-adjusted drift sp_sharpe_22.
-# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
-# except feature_fields. 5-seed ensemble.
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sharpe_22" %}
-
-qlib_init:
-    provider_uri: "{{ LAKE }}"
-    region: us
-    expression_cache: null
-    dataset_cache: null
-    calendar_provider:
-        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    instrument_provider:
-        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
-    feature_provider:
-        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    exp_manager:
-        class: MLflowExpManager
-        module_path: qlib.workflow.expm
-        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp30-m2-sharpe" }
-
-task:
-    model:
-        class: RankICEnsembleLGBModel
-        module_path: tac_qlib.contrib.model.rank_ensemble
-        kwargs:
-            loss: mse
-            learning_rate: 0.02
-            num_leaves: 31
-            n_estimators: 3000
-            num_boost_round: 3000
-            early_stopping_rounds: 200
-            min_data_in_leaf: 20
-            lambda_l2: 0.5
-            colsample_bytree: 0.8
-            subsample: 0.8
-            subsample_freq: 1
-            reg_alpha: 0.1
-            reg_lambda: 1.0
-            seeds: "42,7,2026,99,123"
-
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: TACHandler
-                module_path: tac_qlib.contrib.data.handler
-                kwargs:
-                    instruments: "{{ UNIVERSE }}"
-                    start_time: 2015-01-03
-                    end_time: 2026-08-10
-                    fit_start_time: 2016-01-04
-                    fit_end_time: 2025-09-01
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "{{ FEATURES }}"
-                    infer_processors:
-                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
-                        - { class: ProcessInf, kwargs: {} }
-                        - { class: CSRankNorm, kwargs: {} }
-                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
-                        - { class: Fillna, kwargs: {} }
-            segments:
-                train: [2016-01-04, 2025-09-01]
-                valid: [2025-09-03, 2026-01-03]
-                test: [2026-01-04, 2026-08-10]
-
-    record:
-        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
-        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: TopkDropoutStrategy
-                      module_path: qlib.contrib.strategy
-                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
-                  backtest:
-                      start_time: 2026-01-04
-                      end_time: 2026-08-10
-                      account: 1000000
-                      benchmark: SPY
-                      exchange_kwargs:
-                          codes: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d

workflows/exp31/workflow.yaml

@@ -1,99 +0,0 @@
-# M3 isolation run: base compact set + GARCH(1,1) vol-regime trio.
-# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
-# except feature_fields. 5-seed ensemble.
-{%- set LAKE = TAC_LAKE_DIR %}
-{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
-{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_garch_cond_var,sp_garch_persistence,sp_garch_std_resid" %}
-
-qlib_init:
-    provider_uri: "{{ LAKE }}"
-    region: us
-    expression_cache: null
-    dataset_cache: null
-    calendar_provider:
-        class: tac_qlib.data.providers.LakeCalendarProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    instrument_provider:
-        class: tac_qlib.data.providers.LakeInstrumentProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
-    feature_provider:
-        class: tac_qlib.data.providers.LakeFeatureProvider
-        kwargs: { lake_root: "{{ LAKE }}", market: US }
-    exp_manager:
-        class: MLflowExpManager
-        module_path: qlib.workflow.expm
-        kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp31-m3-garch" }
-
-task:
-    model:
-        class: RankICEnsembleLGBModel
-        module_path: tac_qlib.contrib.model.rank_ensemble
-        kwargs:
-            loss: mse
-            learning_rate: 0.02
-            num_leaves: 31
-            n_estimators: 3000
-            num_boost_round: 3000
-            early_stopping_rounds: 200
-            min_data_in_leaf: 20
-            lambda_l2: 0.5
-            colsample_bytree: 0.8
-            subsample: 0.8
-            subsample_freq: 1
-            reg_alpha: 0.1
-            reg_lambda: 1.0
-            seeds: "42,7,2026,99,123"
-
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: TACHandler
-                module_path: tac_qlib.contrib.data.handler
-                kwargs:
-                    instruments: "{{ UNIVERSE }}"
-                    start_time: 2015-01-03
-                    end_time: 2026-08-10
-                    fit_start_time: 2016-01-04
-                    fit_end_time: 2025-09-01
-                    freq: day
-                    lake_root: "{{ LAKE }}"
-                    market: US
-                    label: "Ref($close,-6)/Ref($close,-1)-1"
-                    feature_fields: "{{ FEATURES }}"
-                    infer_processors:
-                        - { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
-                        - { class: ProcessInf, kwargs: {} }
-                        - { class: CSRankNorm, kwargs: {} }
-                        - { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
-                        - { class: Fillna, kwargs: {} }
-            segments:
-                train: [2016-01-04, 2025-09-01]
-                valid: [2025-09-03, 2026-01-03]
-                test: [2026-01-04, 2026-08-10]
-
-    record:
-        - { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
-        - { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-              config:
-                  strategy:
-                      class: TopkDropoutStrategy
-                      module_path: qlib.contrib.strategy
-                      kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
-                  backtest:
-                      start_time: 2026-01-04
-                      end_time: 2026-08-10
-                      account: 1000000
-                      benchmark: SPY
-                      exchange_kwargs:
-                          codes: "{{ UNIVERSE }}"
-                          deal_price: $close
-                          freq: day
-                          open_cost: 0.0005
-                          close_cost: 0.0015
-                          min_cost: 5.0
-              risk_analysis_freq: 1d