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tac-exp-dev/queue/workflows/q15_single_seed.yaml
T
zhaoli 7124ef5d8e queue: pre-register Series 2 (Q12-Q20) targeting unproven book hypotheses
Series 1 (Q01-Q11, exp 33-43) executed and folded into book/CLAIMS.md.
Series 2 covers the remaining HYPOTHESIS rows and open questions:
  Q12 22d label + weekly recompute (untested combo)
  Q13 weekly rebalance reproduction on a 2nd OOS window
  Q14 out-of-universe validation (single-stock panel, needs lake backfill)
  Q15 5-seed vs single-seed clean A/B
  Q16 hmm family as features
  Q17 realized-moments family as features
  Q18 OptimalStopControl clean re-test
  Q19/Q20 martingale-VR + effective-names scripted studies

Each workflow pins one-variable change vs exp-26 reference and acceptance.
2026-08-20 03:51:58 +00:00

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# QUEUE-15 — 5-seed vs single-model clean A/B on the compact stochastic set.
# CLAIMS.md HYPOTHESIS: "5-seed RankIC ensemble raises performance vs single model
# on ablated set" — pre-clean-lake exp 12 idea, re-validated directionally by exp
# 22–24, never a clean A/B post-reset. Seed count is load-bearing (exp 28: 2<5).
# Change vs exp-26 reference: seeds "42,7,2026,99,123" -> single seed "2026".
# Acceptance: single-model RankIC < 0.0663, net_IR < 0.21 (ensemble beats single).
# Run: rd_run_workflow config_path=<repo>/experiments/queue/workflows/q15_single_seed.yaml \
# experiment_name=tac-rd-q15-single-seed
{%- set LAKE = TAC_LAKE_DIR %}
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
qlib_init:
provider_uri: "{{ LAKE }}"
region: us
expression_cache: null
dataset_cache: null
calendar_provider:
class: tac_qlib.data.providers.LakeCalendarProvider
kwargs: { lake_root: "{{ LAKE }}", market: US }
instrument_provider:
class: tac_qlib.data.providers.LakeInstrumentProvider
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
feature_provider:
class: tac_qlib.data.providers.LakeFeatureProvider
kwargs: { lake_root: "{{ LAKE }}", market: US }
exp_manager:
class: MLflowExpManager
module_path: qlib.workflow.expm
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-q15-single-seed" }
task:
model:
class: RankICEnsembleLGBModel
module_path: tac_qlib.contrib.model.rank_ensemble
kwargs:
loss: mse
learning_rate: 0.02
num_leaves: 31
n_estimators: 3000
num_boost_round: 3000
early_stopping_rounds: 200
min_data_in_leaf: 20
lambda_l2: 0.5
colsample_bytree: 0.8
subsample: 0.8
subsample_freq: 1
reg_alpha: 0.1
reg_lambda: 1.0
seeds: "2026"
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: TACHandler
module_path: tac_qlib.contrib.data.handler
kwargs:
instruments: "{{ UNIVERSE }}"
start_time: 2015-01-03
end_time: 2026-08-10
fit_start_time: 2016-01-04
fit_end_time: 2025-09-01
freq: day
lake_root: "{{ LAKE }}"
market: US
label: "Ref($close,-6)/Ref($close,-1)-1"
feature_fields: "{{ FEATURES }}"
infer_processors:
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
- { class: ProcessInf, kwargs: {} }
- { class: CSRankNorm, kwargs: {} }
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
- { class: Fillna, kwargs: {} }
segments:
train: [2016-01-04, 2025-09-01]
valid: [2025-09-03, 2026-01-03]
test: [2026-01-04, 2026-08-10]
record:
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config:
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
backtest:
start_time: 2026-01-04
end_time: 2026-08-10
account: 1000000
benchmark: SPY
exchange_kwargs:
codes: "{{ UNIVERSE }}"
deal_price: $close
freq: day
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5.0
risk_analysis_freq: 1d