143 lines
5.6 KiB
YAML
143 lines
5.6 KiB
YAML
# -----------------------------------------------------------------------------
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# CANONICAL: LightGBM with RankIC early-stopping on the 50-ETF SP-5d panel.
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#
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# Uses the tac-qlib contrib stack so no reinvention is needed:
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# - model: RankICLGBModel (tac_qlib.contrib.model.rank_gbdt) — early-stops
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# on per-day cross-sectional RankIC, not l2. The measured lever:
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# RankIC 0.047 -> 0.075 on the SP-5d signal, and with the tuned
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# budget the first config that beat SPY net of costs.
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# - handler: TACHandler (tac_qlib.contrib.data.handler) — lake features
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# - records: SignalRecord + SigAnaRecord + PortAnaRecord (TopkDropout)
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#
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# Feature columns are the 24 sp_* columns computed by the Rust get_lake_sp tool
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# (7 stochastic-process families: ou,hmm,jump,har,trend,hurst,signature). Any
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# other column present in the lake features parquet can be listed instead.
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#
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# Run:
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# rd_run_workflow config_path=tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml \
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# experiment_name=tac-rd-rankic
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# -----------------------------------------------------------------------------
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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 SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,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:
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lake_root: "{{ LAKE }}"
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market: US
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instrument_provider:
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class: tac_qlib.data.providers.LakeInstrumentProvider
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kwargs:
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lake_root: "{{ LAKE }}"
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market: US
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markets: {}
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feature_provider:
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class: tac_qlib.data.providers.LakeFeatureProvider
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kwargs:
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lake_root: "{{ LAKE }}"
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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:
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uri: "sqlite:///{{ LAKE }}/mlruns.db"
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default_exp_name: "tac-rd-rankic"
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task:
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model:
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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: 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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seed: 42
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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: 2015-01-03
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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: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
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infer_processors:
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- class: DropAllNaN
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kwargs: {}
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- class: ProcessInf
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kwargs: {}
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- class: CSRankNorm
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kwargs: {}
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- class: ZScoreNorm
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kwargs: {}
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- class: Fillna
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kwargs: {}
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segments:
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train: [2015-01-03, 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
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module_path: qlib.workflow.record_temp
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kwargs: {}
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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ana_long_short: true
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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:
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signal: "<PRED>"
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topk: 10
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n_drop: 2
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only_tradable: true
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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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