134 lines
4.7 KiB
YAML
134 lines
4.7 KiB
YAML
# -----------------------------------------------------------------------------
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# Tune run 5: longer backtest window (2026-01-01 -> 2026-08-01).
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#
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# Purpose: test the fixed universe provider (_resolve_symbols now honors the
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# comma-separated 10-name instruments) and the fixed artifact pinning
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# (mlruns/<exp_id>/<run_id>/) over a 7-month out-of-sample window instead of
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# the single month (Jul) of run 47e9e369 / tune_run4.
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#
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# Changes vs tune_run4_fix_universe_longtrain.yaml:
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# - test/backtest window 2026-07-01..08-06 -> 2026-01-01..2026-08-01
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# - train/valid moved back so they stay strictly before test (no leakage):
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# train: 2024-06-03 .. 2025-11-28 (~18 months, ~4500 rows x 10 names)
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# valid: 2025-12-01 .. 2025-12-31 (1 month, right before test)
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# test : 2026-01-01 .. 2026-08-01 (7 months)
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# - everything else held fixed: 5-day label, LGB baseline hyperparams,
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# topk 5 / n_drop 2, benchmark QQQ, universe 10 names.
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#
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# NOTE: requires the providers.py fix so the universe is actually 10 names
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# (not silently expanded to all 17 lake symbols).
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#
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# Trigger (existing experiment, exp id 4 -> artifacts under
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# $TAC_LAKE_DIR/mlruns/4/<run_id>/ ):
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# rd_run_workflow config_path=tac-qlib/workflows/tune_run5_longtest.yaml \
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# experiment_name=tac-rd-tune2
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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:
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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-tune2"
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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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num_boost_round: 1000
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early_stopping_rounds: 50
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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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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,TSLA,QQQ,IVV,SMH,TLT,IBIT,MCHI,AIQ
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start_time: 2000-01-03
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end_time: 2026-08-01
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fit_start_time: 2024-06-03
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fit_end_time: 2025-11-28
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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: [2024-06-03, 2025-11-28]
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valid: [2025-12-01, 2025-12-31]
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test: [2026-01-01, 2026-08-01]
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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: 5
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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: 2026-01-01
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end_time: 2026-08-01
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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,TSLA,QQQ,IVV,SMH,TLT,IBIT,MCHI,AIQ
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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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