147 lines
5.7 KiB
YAML
147 lines
5.7 KiB
YAML
# -----------------------------------------------------------------------------
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# Tune run 3 (NEXT run): 5-day label + clean 10-name universe.
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#
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# Baseline (exp 1 / run f29f5446):
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# IC 0.071, ICIR 0.17, Rank IC 0.014, Rank ICIR 0.03 -> ranking ~ coin flip
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# valid l2 best at round 0 and never improved (early-stopped ~50 rounds, overfit)
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# backtest: strategy +4.9% ann (raw) vs equal-weight universe +89.2% ann
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# (benchmark was unset -> qlib used equal-weight), excess w/ cost -94.0% ann,
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# IR -2.23, max DD -18.9%. topk=2, 24 trades/27 days, $11.1k cost (1.1% of $1M),
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# ending book ~97.5% in AAPL+IBIT (two names, both ~49%).
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#
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# PRIMARY LEVER (change one thing, everything else held at baseline):
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# label: 1-day next return -> 5-day forward return
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# "Ref($close,-6)/Ref($close,-1)-1".
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# Rationale: Rank ICIR 0.03 is the binding constraint - a topk book's return
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# is bounded by ranking quality, and no backtest tuning fixes a non-existent
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# ranking. The retained TA features (rsi_14, macd_hist, ema_20, volume,
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# stoch, aroon) are momentum/mean-reversion proxies that predict multi-day
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# drift, not overnight noise; and the avg holding in the baseline book was
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# several days, so a 1-day label mismatches the holding horizon.
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#
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# SUPPORTING (kept minimal, flagged for attribution):
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# - universe 17 -> 10: drop leveraged/vol/cash names (VXX, USO, SLV, BIL)
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# and near-duplicate index baskets (GPIQ, QQQE, KTEC). 17 names were really
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# ~8 independent betas (QQQ/QQQE/IVV/SMH/AIQ overlap heavily).
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# - topk 2 -> 5, n_drop 1 -> 2: stop the 2-name lottery, cut per-name turnover.
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# - benchmark: unset -> QQQ (a real index ETF the universe tracks; the
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# "excess return" vs equal-weight of a 17-name universe is misleading).
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# - model: explicitly num_boost_round 1000 + early_stopping_rounds 50 so the
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# round count is actually controlled (baseline's n_estimators: 200 was a
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# no-op, swallowed into lgb params; rounds were the 1000 default).
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# Hyperparameters otherwise identical to baseline (lr 0.05, num_leaves 15,
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# reg 0.01/0.01) for a clean label A/B.
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#
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# Trigger into a NEW experiment (do not pollute exp 1):
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# rd_run_workflow config_path=tac-qlib/workflows/tune_run3_label5d_clean_universe.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:///{{ LAKE }}/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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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: 2026-08-06
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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:
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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: 2026-07-01
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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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