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 zhaoli
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32477c7bb8 |
@@ -1,20 +0,0 @@
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# exp/10 sp5d-moment-features
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Variant C: generic-only 19 + 16 new moment/volatility families (skew, kurt,
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DSV+ratios, max_up/down, rv_ac1, rv_cv_22, sig lag-5). 35 sp_* fields, ou/hmm excluded.
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Run a3f7d1d40c3d4b839314fcf5b40f9b08 (tac-rd-moments / exp 12) — FINISHED.
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## Result: NEGATIVE (regression vs generic-only baseline)
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| Metric | generic-only 19 (run 7b1e797) | +moments 35 (run a3f7d1d) |
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|---|---|---|
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| Rank IC | 0.0635 | 0.0466 |
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| Rank ICIR | 0.276 | 0.183 |
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| L-S Sharpe | 2.55 | 1.44 |
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| net excess (cost) | +3.1% IR 0.28 | -16.2% IR -1.57 |
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| MDD | -7.3% | -11.1% |
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Same failure mode as ou/hmm in exp 9: adding cross-sectional moment features
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to the 50-name panel degrades the rank signal. Generic-only 19 remains the
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best configuration. No further moment-family variants planned.
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@@ -1,22 +1,24 @@
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# -----------------------------------------------------------------------------
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# VARIANT C (generic + moments): keeps the winning generic-only 19-field set
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# (jump,har,trend,hurst,signature) and adds the NEW generic moment families the
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# engine now exposes:
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# - realized skewness / kurtosis (sp_rskew_5, sp_rskew_22, sp_rkurt_5, sp_rkurt_22)
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# - downside semi-variance + ratios (sp_dsv_1/5/22, sp_dsv_ratio_1/5/22)
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# - signed max moves (sp_max_up, sp_max_down)
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# - RV autocorr / vol-of-vol (sp_rv_ac1, sp_rv_cv_22)
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# - longer-lag signature terms (sp_sig_level2_*_5)
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# Drops the model-specific ou/hmm families (they scored high in importance but
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# hurt the rank dimension in the all-24 run). Same panel/model as baseline.
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# ISOLATION: multi-seed RankIC ensemble, ablate-B generic-only feature set.
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#
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# Isolates the ensemble effect on the SP-5d rank signal. Same panel, segments,
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# history (full backfilled 2016+) and feature set as the exp-9 ablate-B winner
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# (generic-only sp_* families: jump,har,trend,hurst,signature), but replaces the
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# single RankICLGBModel with a 5-seed RankICEnsembleLGBModel (42,7,2026,99,123)
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# that averages per-day predictions.
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#
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# Differs from exp-15 (tac-rd-rank-ensemble, mlflow exp 15) ONLY by dropping the
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# TA subset (rsi_14,roc_10,macd_hist,willr_14,atr_14) and the inter-asset xr_*
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# features, so any change vs exp-15 is attributable to the feature set alone,
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# and any change vs exp-9 is attributable to the ensemble + full history alone.
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#
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# Run:
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# rd_run_workflow config_path=experiments/workflows/ablate_generic_moments.yaml \
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# experiment_name=tac-rd-moments
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# rd_run_workflow config_path=experiments/workflows/exp12_isolation_ensemble.yaml \
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# experiment_name=tac-rd-rank-ensemble-isolated
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# -----------------------------------------------------------------------------
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{%- set LAKE = TAC_LAKE_DIR %}
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{%- 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" %}
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{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_max_up,sp_max_down,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_rv_ac1,sp_rv_cv_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,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22" %}
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{%- set SP_FIELDS = "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" %}
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qlib_init:
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provider_uri: "{{ LAKE }}"
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@@ -45,13 +47,13 @@ qlib_init:
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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-moments"
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uri: "sqlite:///mlruns.db"
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default_exp_name: "tac-rd-rank-ensemble-isolated"
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task:
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model:
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class: RankICLGBModel
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module_path: tac_qlib.contrib.model.rank_gbdt
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class: RankICEnsembleLGBModel
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module_path: tac_qlib.contrib.model.rank_ensemble
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kwargs:
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loss: mse
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learning_rate: 0.02
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@@ -66,7 +68,7 @@ task:
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subsample_freq: 1
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reg_alpha: 0.1
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reg_lambda: 1.0
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seed: 42
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seeds: "42,7,2026,99,123"
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dataset:
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class: DatasetH
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@@ -78,8 +80,8 @@ task:
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kwargs:
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instruments: "{{ UNIVERSE }}"
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start_time: 2015-01-03
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end_time: 2026-08-10
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fit_start_time: 2015-01-03
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end_time: 2026-08-14
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fit_start_time: 2016-01-04
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fit_end_time: 2025-09-01
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freq: day
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lake_root: "{{ LAKE }}"
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@@ -98,7 +100,7 @@ task:
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- class: Fillna
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kwargs: {}
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segments:
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train: [2015-01-03, 2025-09-01]
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train: [2016-01-04, 2025-09-01]
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valid: [2025-09-03, 2026-01-03]
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test: [2026-01-04, 2026-08-10]
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