108 lines
4.0 KiB
YAML
108 lines
4.0 KiB
YAML
# -----------------------------------------------------------------------------
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# MINIMAL workflow — the canonical "run a backtest" template for the skill.
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#
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# Every traced backtest runs through a workflow YAML like this one via
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# rd_run_workflow, so the `record` blocks write MLflow artifacts to disk
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# (<lake>/mlruns/<exp_id>/<run_id>). The traced experiment's ref id IS the
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# mlflow run id returned by rd_run_workflow.
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#
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# Trigger:
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# rd_run_workflow config_path=examples/workflow_minimal.yaml \
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# experiment_name=tac-rd-minimal
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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-minimal" }
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task:
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model:
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class: LGBModel
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module_path: qlib.contrib.model.gbdt
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kwargs:
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loss: mse
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learning_rate: 0.05
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num_leaves: 15
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n_estimators: 200
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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: 0.01
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reg_lambda: 0.01
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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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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 block — REQUIRED. Each entry writes one artifact family to mlruns:
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# SignalRecord pred.pkl + label.pkl
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# SigAnaRecord IC / Rank IC series + long-short group returns
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# PortAnaRecord backtest report / positions / risk
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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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