114 lines
4.3 KiB
YAML
114 lines
4.3 KiB
YAML
# -----------------------------------------------------------------------------
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# RankIC early-stop workflow — minimal example wiring the custom model.
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#
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# model_rank_gbdt.py must be importable: copy it (or symlink) into
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# tac_qlib/contrib/model/ and sync to /opt/venv site-packages (see SKILL.md
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# "Installed package copy" gotcha). Then run:
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#
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# rd_run_workflow config_path=examples/workflow_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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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-rankic" }
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task:
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model:
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# Custom model — see examples/model_rank_gbdt.py (RankICLGBModel):
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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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instruments: AAPL,MSFT,QQQ,IVV,SMH,TLT
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start_time: 2026-03-01
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end_time: 2026-08-06
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fit_start_time: 2026-03-01
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fit_end_time: 2026-05-31
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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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infer_processors:
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- { class: DropAllNaN, kwargs: {} }
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- { class: ProcessInf, kwargs: {} }
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- { class: CSRankNorm, kwargs: {} }
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- { class: ZScoreNorm, kwargs: {} }
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- { class: Fillna, kwargs: {} }
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segments:
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train: [2026-03-01, 2026-05-31]
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valid: [2026-06-01, 2026-06-30]
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test: [2026-07-01, 2026-08-06]
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record:
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- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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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:
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signal: "<PRED>"
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topk: 2
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n_drop: 1
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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: 2026-07-01
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end_time: 2026-08-06
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account: 1000000
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benchmark: QQQ
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exchange_kwargs:
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codes: AAPL,MSFT,QQQ,IVV,SMH,TLT
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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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