finish experiment 55 (exp/55-adaptive-short-window-retrain-test-the-4)
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# Adaptive short-window retrain: m2-sharpe22 / test 2021 (trace exp 55) — 1y training window
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# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge.
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{% set LAKE = TAC_LAKE_DIR %}
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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-bt-m2-sharpe22-adaptive-1y" }
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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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parallel: 5
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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: "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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start_time: "2015-01-03"
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end_time: "2022-01-07"
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fit_start_time: "2019-08-30"
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fit_end_time: "2020-08-31"
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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: "$open,$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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infer_processors:
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- { class: DropAllNaN, kwargs: { fit_start_time: "2019-08-30", fit_end_time: "2020-08-31" } }
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- { class: ProcessInf, kwargs: {} }
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- { class: CSRankNorm, kwargs: {} }
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- { class: ZScoreNorm, kwargs: { fit_start_time: "2019-08-30", fit_end_time: "2020-08-31" } }
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- { class: Fillna, kwargs: {} }
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segments:
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train: ["2019-08-30", "2020-08-31"]
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valid: ["2020-09-01", "2020-12-31"]
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test: ["2021-01-04", "2021-12-31"]
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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: "2021-01-04"
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end_time: "2021-12-31"
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: "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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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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