Compare commits
1
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
|
f9d1fe66f6 |
@@ -0,0 +1,20 @@
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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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+20
-17
@@ -1,19 +1,22 @@
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# -----------------------------------------------------------------------------
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# EXP 14 - Strategy A (baseline): reference model + TopkDropout.
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#
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# Model = RankICEnsembleLGBModel (5-seed RankIC-early-stopped LGB), the class
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# wired by the tac-rd-rank-ensemble-isolated reference (run 0cea66d9...).
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# Strategy = TopkDropout topk=10 n_drop=2 risk_degree=0.95 (the reference's own
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# recorded backtest strategy), so this run reproduces the reference baseline on
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# the same 50-ETF SP-5d panel.
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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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#
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# Run:
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# rd_run_workflow config_path=experiments/workflows/exp14-optstop-v2/a_topk_baseline.yaml \
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# experiment_name=tac-rd-optstop-v2
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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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# -----------------------------------------------------------------------------
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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_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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{%- 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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qlib_init:
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provider_uri: "{{ LAKE }}"
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@@ -43,12 +46,12 @@ qlib_init:
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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-optstop-v2"
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default_exp_name: "tac-rd-moments"
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task:
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model:
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class: RankICEnsembleLGBModel
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module_path: tac_qlib.contrib.model.rank_ensemble
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class: RankICLGBModel
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module_path: tac_qlib.contrib.model.rank_gbdt
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kwargs:
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loss: mse
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learning_rate: 0.02
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@@ -63,7 +66,7 @@ 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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seeds: "42,7,2026,99,123"
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seed: 42
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dataset:
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class: DatasetH
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@@ -75,8 +78,8 @@ 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: 2026-08-14
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fit_start_time: 2016-01-04
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end_time: 2026-08-10
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fit_start_time: 2015-01-03
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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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@@ -95,7 +98,7 @@ task:
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- class: Fillna
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kwargs: {}
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segments:
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train: [2016-01-04, 2025-09-01]
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train: [2015-01-03, 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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@@ -1,141 +0,0 @@
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# -----------------------------------------------------------------------------
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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/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_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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region: us
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expression_cache: null
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dataset_cache: null
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||||
|
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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:
|
||||
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-rank-ensemble-isolated"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
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||||
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: "42,7,2026,99,123"
|
||||
|
||||
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: 2026-08-14
|
||||
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: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- 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
|
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- 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: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
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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
|
||||
@@ -1,149 +0,0 @@
|
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# -----------------------------------------------------------------------------
|
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# EXP 14 - Strategy B (enhanced): reference model + OptimalStopControlV2.
|
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#
|
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# Model = RankICEnsembleLGBModel (5-seed RankIC-early-stopped LGB), identical to
|
||||
# Strategy A. Strategy = OptimalStopControlV2 (tac_qlib.contrib.strategy.
|
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# optimal_stop_v2) with the controls that address OptimalStopControl's documented
|
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# weaknesses:
|
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# - turnover / cost control: rebalance_band=0.05 (skip small rebalances),
|
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# cooldown_days=3 (no whipsaw re-entries), max_turnover=0.30 (cap daily
|
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# traded notional, priority exits > opens > rebalances)
|
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# - robust thresholds (no valid-window overfit): entry 0.85 / exit 0.70 /
|
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# max_hold 10 / min_hold 2 / sl -0.08
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# Sizing = equal-weight control (risk_degree fraction of total value split
|
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# across targets) - the "proper allocation" that replaces cash-heuristic sizing.
|
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#
|
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# Run:
|
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# rd_run_workflow config_path=experiments/workflows/exp14-optstop-v2/b_optstop_v2.yaml \
|
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# experiment_name=tac-rd-optstop-v2
|
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# -----------------------------------------------------------------------------
|
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{%- 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 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" %}
|
||||
|
||||
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:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-optstop-v2"
|
||||
|
||||
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: "42,7,2026,99,123"
|
||||
|
||||
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: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- 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: OptimalStopControlV2
|
||||
module_path: tac_qlib.contrib.strategy.optimal_stop_v2
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
entry_pct: 0.85
|
||||
exit_pct: 0.70
|
||||
max_hold_days: 10
|
||||
min_hold_days: 2
|
||||
sl: -0.08
|
||||
risk_degree: 0.95
|
||||
notional: 20000
|
||||
rebalance_band: 0.05
|
||||
cooldown_days: 3
|
||||
max_turnover: 0.30
|
||||
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
|
||||
Reference in New Issue
Block a user