145 lines
5.4 KiB
YAML
145 lines
5.4 KiB
YAML
# -----------------------------------------------------------------------------
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# Tune run 4 (NEXT run): fix the universe bug + extend the train window.
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#
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# Previous (exp 3 / run 1e170e7f): IC -0.025 / ICIR -0.071 / RankIC -0.022 /
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# RankICIR -0.073 (noise), Long-Short -27% ann; excess +15.8% ann w/ cost
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# (IR 1.35) vs QQQ; $1M -> $971.7k (-2.8%); 65 trades/27d, $12.1k cost.
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# l2.train 0.35 vs l2.valid 0.95 -> gross overfit (valid best at round 0,
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# early-stopped at 16 trees).
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#
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# CRITICAL BUG in that run: the 10-name universe was silently IGNORED.
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# TACHandler passes `instruments` as a comma-separated STRING; qlib wraps it
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# as {"market": "<comma string>", "filter_pipe": []}; LakeInstrumentProvider
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# ._resolve_symbols() only handles list/tuple/ndarray and falls through to
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# load_symbols() = the ENTIRE 17-symbol lake. So the model trained/traded on
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# VXX, USO, SLV, BIL, GPIQ, QQQE, KTEC too - exactly the leveraged/hedge
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# names the "clean 10-name universe" hypothesis meant to drop. The universe
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# A/B is UNTESTED.
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# Fix (providers.py:100 _resolve_symbols): split comma-separated strings.
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#
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# PRIMARY LEVER (this run, ONE hypothesis):
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# universe = the intended 10-name dedup pool (AAPL,MSFT,TSLA,QQQ,IVV,SMH,
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# TLT,IBIT,MCHI,AIQ), now actually enforced, + train window 3 months -> 2
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# years. The 3-month window (~1000 rows for a 21-feature GBDT) is the hard
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# ceiling on signal; features span 2000-2026 so more data is free.
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# Everything else held at run-1e170e7f for a clean A/B: 5-day label,
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# LGB baseline hyperparams, topk 5 / n_drop 2, benchmark QQQ.
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#
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# SUPPORTING (flagged, NOT changed this run to keep attribution clean):
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# - if valid loss still rises monotonically after 2y of data, next step is
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# regularization (reg_alpha/lambda 0.01 -> ~0.5, num_leaves 15 -> 10,
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# lr 0.05 -> 0.02) rather than label/topk changes.
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#
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# Trigger into a NEW experiment (do not pollute exp 1/3):
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# rd_run_workflow config_path=tac-qlib/workflows/tune_run4_fix_universe_longtrain.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: 2026-08-06
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fit_start_time: 2024-06-03
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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: [2024-06-03, 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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