Compare commits
2
Commits
| Author | SHA1 | Date | |
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 zhaoli
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7460d5fe01 | ||
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zhaoli
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337f99222d |
+1
-9
@@ -1,23 +1,15 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : e660b4f2dd7c521615c54a142eec9266837f2e64
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# parent repo HEAD : 337f99222df3629f58eca0404caae2b1ccb6a994
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# tac-qlib/tac_qlib/contrib
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# tac-qlib/tac_qlib/data
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# per-file hashes (git hash-object):
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1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
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b5e28b7c0a10ab553eaa6303013a880731742378 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
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c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
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715ab312125a446af751b928fe41cf41b99ffcd0 tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
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a82a6a236db93097d48748e541c2cf9caa4e48b5 tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
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0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
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b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
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483221cf1d5bad779d3cf9505269b7fb0b3f165d tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
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1286dec07a861f25691b24f71ecc22d8eb3537df tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
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5185764bd55dbc63d16582bfcb6adc11b7e6d342 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
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d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
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ccfe7d554989aa7f3e5a2128ae663e51b2207149 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
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4afcf9058231111c412925f4c4b84e81d656db87 tac-qlib/tac_qlib/contrib/strategy/__init__.py
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13a8e4a41ce9afde97a55756d666d3ff5cbbec0e tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
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0bb5804a79611c66bc25988226ca0f1cdbe1a7eb tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
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79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
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92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
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3cbc0686e6f863d306d93c589a607a5bdb7201f2 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
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# M2 isolation run: base compact set + risk-adjusted drift sp_sharpe_22.
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# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
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# except feature_fields. 5-seed ensemble.
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{%- set LAKE = TAC_LAKE_DIR %}
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{%- 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" %}
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{%- 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" %}
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qlib_init:
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provider_uri: "{{ LAKE }}"
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region: us
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expression_cache: null
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dataset_cache: null
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calendar_provider:
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class: tac_qlib.data.providers.LakeCalendarProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US }
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instrument_provider:
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class: tac_qlib.data.providers.LakeInstrumentProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
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feature_provider:
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class: tac_qlib.data.providers.LakeFeatureProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US }
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exp_manager:
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class: MLflowExpManager
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module_path: qlib.workflow.expm
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kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp30-m2-sharpe" }
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task:
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model:
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class: RankICEnsembleLGBModel
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module_path: tac_qlib.contrib.model.rank_ensemble
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kwargs:
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loss: mse
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learning_rate: 0.02
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num_leaves: 31
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n_estimators: 3000
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num_boost_round: 3000
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early_stopping_rounds: 200
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min_data_in_leaf: 20
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lambda_l2: 0.5
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colsample_bytree: 0.8
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subsample: 0.8
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subsample_freq: 1
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reg_alpha: 0.1
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reg_lambda: 1.0
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seeds: "42,7,2026,99,123"
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: TACHandler
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module_path: tac_qlib.contrib.data.handler
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kwargs:
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instruments: "{{ UNIVERSE }}"
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start_time: 2015-01-03
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end_time: 2026-08-10
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fit_start_time: 2016-01-04
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fit_end_time: 2025-09-01
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freq: day
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lake_root: "{{ LAKE }}"
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market: US
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label: "Ref($close,-6)/Ref($close,-1)-1"
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feature_fields: "{{ FEATURES }}"
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infer_processors:
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- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
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- { class: ProcessInf, kwargs: {} }
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- { class: CSRankNorm, kwargs: {} }
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- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
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- { class: Fillna, kwargs: {} }
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segments:
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train: [2016-01-04, 2025-09-01]
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valid: [2025-09-03, 2026-01-03]
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test: [2026-01-04, 2026-08-10]
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record:
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- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
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- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
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- class: PortAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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config:
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy
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kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
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backtest:
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start_time: 2026-01-04
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end_time: 2026-08-10
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: "{{ UNIVERSE }}"
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deal_price: $close
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freq: day
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open_cost: 0.0005
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close_cost: 0.0015
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min_cost: 5.0
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risk_analysis_freq: 1d
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@@ -1,99 +0,0 @@
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# M3 isolation run: base compact set + GARCH(1,1) vol-regime trio.
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# Exact copy of exp26 (reference: expId=25 run=21afc6afdb674a399b59dd76c97628ce)
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# except feature_fields. 5-seed ensemble.
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{%- set LAKE = TAC_LAKE_DIR %}
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{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
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{%- set FEATURES = "$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" %}
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qlib_init:
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provider_uri: "{{ LAKE }}"
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region: us
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expression_cache: null
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dataset_cache: null
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calendar_provider:
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class: tac_qlib.data.providers.LakeCalendarProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US }
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instrument_provider:
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class: tac_qlib.data.providers.LakeInstrumentProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
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feature_provider:
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class: tac_qlib.data.providers.LakeFeatureProvider
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kwargs: { lake_root: "{{ LAKE }}", market: US }
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exp_manager:
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class: MLflowExpManager
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module_path: qlib.workflow.expm
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kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-exp31-m3-garch" }
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task:
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model:
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class: RankICEnsembleLGBModel
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module_path: tac_qlib.contrib.model.rank_ensemble
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kwargs:
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loss: mse
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learning_rate: 0.02
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num_leaves: 31
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n_estimators: 3000
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num_boost_round: 3000
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early_stopping_rounds: 200
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min_data_in_leaf: 20
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lambda_l2: 0.5
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colsample_bytree: 0.8
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subsample: 0.8
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subsample_freq: 1
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reg_alpha: 0.1
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reg_lambda: 1.0
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seeds: "42,7,2026,99,123"
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: TACHandler
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module_path: tac_qlib.contrib.data.handler
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kwargs:
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instruments: "{{ UNIVERSE }}"
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start_time: 2015-01-03
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end_time: 2026-08-10
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fit_start_time: 2016-01-04
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fit_end_time: 2025-09-01
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freq: day
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lake_root: "{{ LAKE }}"
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market: US
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label: "Ref($close,-6)/Ref($close,-1)-1"
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feature_fields: "{{ FEATURES }}"
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infer_processors:
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- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
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- { class: ProcessInf, kwargs: {} }
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- { class: CSRankNorm, kwargs: {} }
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- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
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- { class: Fillna, kwargs: {} }
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segments:
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train: [2016-01-04, 2025-09-01]
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valid: [2025-09-03, 2026-01-03]
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test: [2026-01-04, 2026-08-10]
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record:
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- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
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- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
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- class: PortAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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config:
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy
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kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
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backtest:
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start_time: 2026-01-04
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end_time: 2026-08-10
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: "{{ UNIVERSE }}"
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deal_price: $close
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freq: day
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open_cost: 0.0005
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close_cost: 0.0015
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min_cost: 5.0
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risk_analysis_freq: 1d
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