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# exp/10 sp5d-moment-features
Variant C: generic-only 19 + 16 new moment/volatility families (skew, kurt,
DSV+ratios, max_up/down, rv_ac1, rv_cv_22, sig lag-5). 35 sp_* fields, ou/hmm excluded.
Run a3f7d1d40c3d4b839314fcf5b40f9b08 (tac-rd-moments / exp 12) — FINISHED.
## Result: NEGATIVE (regression vs generic-only baseline)
| Metric | generic-only 19 (run 7b1e797) | +moments 35 (run a3f7d1d) |
|---|---|---|
| Rank IC | 0.0635 | 0.0466 |
| Rank ICIR | 0.276 | 0.183 |
| L-S Sharpe | 2.55 | 1.44 |
| net excess (cost) | +3.1% IR 0.28 | -16.2% IR -1.57 |
| MDD | -7.3% | -11.1% |
Same failure mode as ou/hmm in exp 9: adding cross-sectional moment features
to the 50-name panel degrades the rank signal. Generic-only 19 remains the
best configuration. No further moment-family variants planned.
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# -----------------------------------------------------------------------------
# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
# distinct experiment name so the ablation runs are isolated.
#
# Run:
# rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
# experiment_name=tac-rd-rank-ablate
# -----------------------------------------------------------------------------
{%- 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_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,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-rank-ablate"
task:
model:
class: RankICLGBModel
module_path: tac_qlib.contrib.model.rank_gbdt
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
seed: 42
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: 2015-01-03
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: [2015-01-03, 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: 2
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
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# -----------------------------------------------------------------------------
# VARIANT C (generic + moments): keeps the winning generic-only 19-field set
# (jump,har,trend,hurst,signature) and adds the NEW generic moment families the
# engine now exposes:
# - realized skewness / kurtosis (sp_rskew_5, sp_rskew_22, sp_rkurt_5, sp_rkurt_22)
# - downside semi-variance + ratios (sp_dsv_1/5/22, sp_dsv_ratio_1/5/22)
# - signed max moves (sp_max_up, sp_max_down)
# - RV autocorr / vol-of-vol (sp_rv_ac1, sp_rv_cv_22)
# - longer-lag signature terms (sp_sig_level2_*_5)
# Drops the model-specific ou/hmm families (they scored high in importance but
# hurt the rank dimension in the all-24 run). Same panel/model as baseline.
#
# Run:
# rd_run_workflow config_path=experiments/workflows/ablate_generic_moments.yaml \
# experiment_name=tac-rd-moments
# -----------------------------------------------------------------------------
{%- 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_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" %}
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-moments"
task:
model:
class: RankICLGBModel
module_path: tac_qlib.contrib.model.rank_gbdt
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
seed: 42
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: 2015-01-03
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: [2015-01-03, 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: 2
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
@@ -0,0 +1,134 @@
# -----------------------------------------------------------------------------
# ABLATION B (generic-only): same panel/model as the baseline, but feature
# fields restricted to the model-free / generic stochastic-process families
# (jump,har,trend,hurst,signature). Drops the model-specific ou (OU/AR-1
# half-life) and hmm (2-state regime) families to test whether the generic
# families alone dominate the rank dimension.
#
# Run:
# rd_run_workflow config_path=tac-qlib/workflows/ablate_generic_only_sp_fields.yaml \
# experiment_name=tac-rd-rank-ablate
# -----------------------------------------------------------------------------
{%- 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-rank-ablate"
task:
model:
class: RankICLGBModel
module_path: tac_qlib.contrib.model.rank_gbdt
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
seed: 42
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: 2015-01-03
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: [2015-01-03, 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: 2
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