book: scaffold + ch00 (execution trail as spine) — evidence exp 8-31, round 3
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TradeAC Book Agent
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# -----------------------------------------------------------------------------
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# Tune run 6 (NEXT run): wider 10-name universe A/B vs run f744455056 (exp 1).
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#
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# Baseline (exp 1 / run f744455056 — this run):
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# Input : universe AAPL,MSFT,QQQ,IVV,SMH,TLT (6 names, 5 of them the same
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# tech beta); 21 features (OHLCV + TA); label 1-day next return;
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# LGB lr 0.05 / 15 leaves / 200 trees / reg 0.01,0.01;
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# train 03-01..05-31 / valid 06-01..06-30 / test 07-01..08-06.
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# Output: IC 0.048, ICIR 0.09, Rank IC 0.065, Rank ICIR 0.13 -> noise-level
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# (per-day n=6, IC swings -0.89..+0.74 with many null days).
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# Backtest had NO benchmark (benchmark null) -> the "+180% ann, IR 6.4"
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# headline is raw strategy return, not excess. Strategy +16.5% over 27
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# days, but ~half the P&L came from ONE day (2026-07-30 MSFT +14% sell,
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# +$72k realized). 30 trades/27 days, $15.3k cost (1.5% of $1M),
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# ending book 46.6% SMH + 50.8% TLT (2-name lottery).
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#
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# PRIMARY LEVER (change one thing, everything else held at baseline):
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# universe: 6 -> 10 names (AAPL,MSFT,TSLA,QQQ,IVV,SMH,TLT,IBIT,MCHI,AIQ).
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# Rationale: with 6 near-collinear names there is nothing to rank — ICIR 0.09
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# is cross-sectional noise and the topk book just re-buys tech momentum on
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# correlated bets. Widening to ~10 independent-ish betas (mega tech, semis,
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# S&P, Nasdaq, bonds, BTC, EM, robotics) gives the cross-section real breadth,
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# stabilizes IC, and makes a diversified topk book possible.
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#
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# SUPPORTING (kept minimal, flagged for attribution):
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# - topk 2 -> 4, n_drop 1 -> 2: kill the 2-name lottery, cut per-name churn.
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# - benchmark: unset -> QQQ: the baseline "excess return" was raw strategy
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# return because no benchmark was wired; QQQ is the index the tech-heavy
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# universe tracks.
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# - model: explicit num_boost_round 1000 + early_stopping_rounds 50 so round
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# count is controlled (baseline's n_estimators: 200 was swallowed into lgb
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# params and valid l2 rose monotonically -> overfit). Hyperparameters
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# otherwise identical to baseline for a clean universe A/B.
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# - label: KEPT at 1-day next return so this run isolates the universe lever;
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# a 5-day horizon is the natural NEXT experiment (see tune_run3).
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#
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# Trigger into a NEW experiment (do not pollute exp 1); evolved_from = f744455056:
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# rd_run_workflow config_path=tac-qlib/workflows/tune_run6_wider_universe_ab.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,-2)/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: 4
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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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