114 lines
5.3 KiB
YAML
114 lines
5.3 KiB
YAML
# -----------------------------------------------------------------------------
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# Improved RankIC workflow: 300+ stock universe, proven RankICLGBModel params,
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# extended 12-month validation, full SP feature set (40 features).
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#
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# Changes from repro run:
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# 1. Single RankICLGBModel (not ensemble) — proven config from skill
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# 2. num_leaves=15 (not 31) — the verified value
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# 3. Universe expanded from 50 ETFs to 300+ single stocks + ETFs
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# 4. Validation extended to 12 months (2025-01 to 2026-01)
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# 5. Full 40 SP features (no leakage confirmed)
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# 6. Early stopping still at 200 (proven)
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#
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# Run:
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# rd_run_workflow config_path=tac-qlib/workflows/workflow_lgb_300sp_rankic.yaml \
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# experiment_name=tac-rd-300sp-rankic
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# -----------------------------------------------------------------------------
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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-300sp-rankic" }
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task:
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model:
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# Single RankICLGBModel — proven config from tac-qlib-custom skill.
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# Per-day query groups + feval=rankic + metric='None' so early-stopping
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# tracks mean per-day Spearman instead of l2.
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class: RankICLGBModel
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module_path: tac_qlib.contrib.model.rank_gbdt
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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: 15
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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_l1: 0.0
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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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seed: 2026
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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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# Expanded universe: all lake symbols (instruments: "all" = every symbol with bars in the lake)
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instruments: "all"
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start_time: "2015-01-03"
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end_time: "2026-08-14"
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fit_start_time: "2016-01-04"
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fit_end_time: "2025-01-01"
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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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# Full 40 SP features + 6 OHLCV = 46 features
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feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_logp,sp_hurst_exponent,sp_ou_half_life,sp_ou_revert,sp_ou_zscore,sp_hmm_state,sp_hmm_p_regime1,sp_jump_flag,sp_jump_ratio,sp_jump_tail,sp_max_move,sp_max_up,sp_max_down,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5"
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infer_processors:
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- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-01-01" } }
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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: "2025-01-01" } }
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- { class: Fillna, kwargs: {} }
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segments:
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train: ["2016-01-04", "2024-12-31"]
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valid: ["2025-01-02", "2026-01-02"]
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test: ["2026-01-04", "2026-08-14"]
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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: 2, 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: "2026-08-14"
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: ""
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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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