128 lines
4.3 KiB
YAML
128 lines
4.3 KiB
YAML
# -----------------------------------------------------------------------------
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# Tune run 2: same-day signal, strongly regularized model, 3x rotating book.
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#
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# Baseline (exp 1 / run f29f5446): IC 0.071 / ICIR 0.17, Rank IC ~0.014;
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# excess return w/ cost -0.94 ann, IR -2.23. The 1-day signal was noisy
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# (Rank IC ~ 0) and the topk=2 book turned over 24 times in 27 days, paying
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# ~1.1% of the $1M account in costs.
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#
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# Changes (isolates model/backtest effects; universe + label same as baseline):
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# - model: stronger regularization (reg_alpha 0.5, reg_lambda 5.0,
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# subsample 0.7, colsample 0.6) to combat the unstable Rank IC.
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# - topk 2 -> 3, n_drop 1 -> 2: rotate out losers faster (lower cost drag,
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# higher turnover on only the worst names).
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# - benchmark AAPL -> QQQ.
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# - universe: drop leveraged/duplicate names (VXX, USO, SLV, BIL, GPIQ,
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# QQQE, KTEC) for a cleaner cross-section; keeps baseline 1-day label.
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#
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# Trigger:
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# rd_run_workflow config_path=tac-qlib/workflows/tune_run2_regularized.yaml \
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# experiment_name=tac-rd-tune
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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:///mlruns.db"
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default_exp_name: "tac-rd-tune"
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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: 250
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colsample_bytree: 0.6
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subsample: 0.7
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subsample_freq: 1
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reg_alpha: 0.5
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reg_lambda: 5.0
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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: 2026-08-06
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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: 3
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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: 2026-07-01
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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,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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