130 lines
4.3 KiB
YAML
130 lines
4.3 KiB
YAML
# -----------------------------------------------------------------------------
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# LightGBM on the TradeAC lake -- qrun workflow (train -> signal -> backtest).
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#
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# Run it like a stock qlib project:
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#
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# cd tac-qlib
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# qrun workflows/workflow_lgb_taclake.yaml \
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# --experiment_name tac-lake-lgb --uri_folder mlruns
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#
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# Or with a custom lake root:
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#
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# TAC_LAKE_DIR=/path/to/lake qrun workflows/workflow_lgb_taclake.yaml \
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# --experiment_name tac-lake-lgb
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#
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# The lake providers (calendar/instrument/feature) are wired in `qlib_init`; the
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# expression engine and backtest Exchange stay upstream qlib. The TACHandler reads
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# OHLCV + ta-lib features straight from the parquet lake.
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#
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# Segment split (the lake holds 1d bars since 2026-02-09):
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# train 2026-03-01..2026-05-31 / valid 2026-06-01..2026-06-30 / test 2026-07-01..2026-08-06
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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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# --- lake-backed providers (see tac_qlib.data.providers) -----------------
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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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# sqlite backend avoids mlflow's filesystem-backend maintenance-mode opt-out
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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:///mlruns.db"
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default_exp_name: "tac-lake-demo"
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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: all
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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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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
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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: 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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# any symbol the lake holds works; the lake has no index quotes yet
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benchmark: AAPL
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exchange_kwargs:
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codes: all
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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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