106 lines
5.0 KiB
YAML
106 lines
5.0 KiB
YAML
# QUEUE-13 — Weekly rebalance reproduction on a second OOS window.
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# Q07 (exp 39) proved weekly recompute on test 2026-01-04..2026-08-10 (net +12.51%,
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# IR 1.24) but that is a single OOS window. Before promoting the weekly construction
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# to a live round, reproduce it on a disjoint window: test 2025-01-02..2025-12-31
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# with train/valid shifted to end 2024.
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# Change vs exp-26 reference: segments shifted only (train ends 2024-08, test = 2025);
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# strategy is the SAME weekly recompute as exp 39. Label stays 5d.
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# Acceptance: net_IR > 0.21 AND net_ann > +2.13% on the 2025 window.
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# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q13_weekly_second_window.yaml \
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# experiment_name=tac-rd-q13-weekly-second-window
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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" %}
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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-q13-weekly-second-window" }
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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: 2025-12-31
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fit_start_time: 2016-01-04
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fit_end_time: 2024-08-30
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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: "2024-08-30" } }
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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: "2024-08-30" } }
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- { class: Fillna, kwargs: {} }
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segments:
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train: [2016-01-04, 2024-08-30]
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valid: [2024-09-03, 2024-12-31]
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test: [2025-01-02, 2025-12-31]
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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: WeeklyRebalanceDropoutStrategy
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module_path: tac_qlib.contrib.strategy.weekly_rebalance
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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: 2025-01-02
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end_time: 2025-12-31
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: "{{ 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 |