Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
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5483fd8d02 | ||
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zhaoli
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f684e8265b |
+1
-1
@@ -1,5 +1,5 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : cdfd3105aabf69c817cd9acee24072096124dcc2
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# parent repo HEAD : f684e8265b3861cc221bb849959b16e0bdd2cb46
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# tac-qlib/tac_qlib/contrib
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# tac-qlib/tac_qlib/contrib
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# tac-qlib/tac_qlib/data
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# tac-qlib/tac_qlib/data
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# per-file hashes (git hash-object):
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# per-file hashes (git hash-object):
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@@ -1,144 +0,0 @@
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# QUEUE-ly
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# Fractional-Kelly sizing (Q06): same selection, sizing ∝ score magnitude, capped at 0.5× equal-weight notional.
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# Acceptance: net_IR > 0.21 AND net_ann > +2.13% with total_cost not higher than reference.
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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 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" %}
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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-q06-kelly"
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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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kwargs:
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loss: mse
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learning_rate: 0.02
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num_leaves: 31
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n_estimators: 3000
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num_boost_round: 3000
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early_stopping_rounds: 200
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min_data_in_leaf: 20
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lambda_l2: 0.5
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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: 0.1
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reg_lambda: 1.0
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seeds: "42,7,2026,99,123"
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parallel: 5
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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: "{{ 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: 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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market: US
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label: "Ref($close,-6)/Ref($close,-1)-1"
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feature_fields: "{{ FEATURES }}"
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infer_processors:
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- class: DropAllNaN
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kwargs: {}
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- class: ProcessInf
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kwargs: {}
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- class: CSRankNorm
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kwargs: {}
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- class: ZScoreNorm
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kwargs: {}
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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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valid: [2025-09-03, 2026-01-03]
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test: [2026-01-04, 2026-08-10]
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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: FractionalKellyDropoutStrategy
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module_path: tac_qlib.contrib.strategy.kelly_dropout
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kwargs:
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signal: "<PRED>"
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topk: 10
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n_drop: 1
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only_tradable: true
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risk_degree: 0.95
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cap_frac: 0.5
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backtest:
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start_time: 2026-01-04
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end_time: 2026-08-10
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account: 1000000
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benchmark: SPY
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exchange_kwargs:
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codes: "{{ UNIVERSE }}"
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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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@@ -1,133 +0,0 @@
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# -----------------------------------------------------------------------------
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# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
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# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
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# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
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# distinct experiment name so the ablation runs are isolated.
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#
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# Run:
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# rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
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# experiment_name=tac-rd-rank-ablate
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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_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" %}
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|
||||||
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|
||||||
qlib_init:
|
|
||||||
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:
|
|
||||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
|
||||||
kwargs:
|
|
||||||
lake_root: "{{ LAKE }}"
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|
||||||
market: US
|
|
||||||
instrument_provider:
|
|
||||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
|
||||||
kwargs:
|
|
||||||
lake_root: "{{ LAKE }}"
|
|
||||||
market: US
|
|
||||||
markets: {}
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|
||||||
feature_provider:
|
|
||||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
|
||||||
kwargs:
|
|
||||||
lake_root: "{{ LAKE }}"
|
|
||||||
market: US
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|
||||||
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|
||||||
exp_manager:
|
|
||||||
class: MLflowExpManager
|
|
||||||
module_path: qlib.workflow.expm
|
|
||||||
kwargs:
|
|
||||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
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|
||||||
default_exp_name: "tac-rd-rank-ablate"
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|
||||||
|
|
||||||
task:
|
|
||||||
model:
|
|
||||||
class: RankICLGBModel
|
|
||||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
|
||||||
kwargs:
|
|
||||||
loss: mse
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|
||||||
learning_rate: 0.02
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|
||||||
num_leaves: 31
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|
||||||
n_estimators: 3000
|
|
||||||
num_boost_round: 3000
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|
||||||
early_stopping_rounds: 200
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|
||||||
min_data_in_leaf: 20
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|
||||||
lambda_l2: 0.5
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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: 0.1
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|
||||||
reg_lambda: 1.0
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|
||||||
seed: 42
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|
||||||
|
|
||||||
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
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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 }}"
|
|
||||||
market: US
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|
||||||
label: "Ref($close,-6)/Ref($close,-1)-1"
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|
||||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
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|
||||||
infer_processors:
|
|
||||||
- class: DropAllNaN
|
|
||||||
kwargs: {}
|
|
||||||
- class: ProcessInf
|
|
||||||
kwargs: {}
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|
||||||
- class: CSRankNorm
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|
||||||
kwargs: {}
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|
||||||
- class: ZScoreNorm
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|
||||||
kwargs: {}
|
|
||||||
- class: Fillna
|
|
||||||
kwargs: {}
|
|
||||||
segments:
|
|
||||||
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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|
||||||
|
|
||||||
record:
|
|
||||||
- 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:
|
|
||||||
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
|
|
||||||
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
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|
||||||
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
|
|
||||||
@@ -1,134 +0,0 @@
|
|||||||
# -----------------------------------------------------------------------------
|
|
||||||
# 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
|
|
||||||
@@ -1,141 +0,0 @@
|
|||||||
# -----------------------------------------------------------------------------
|
|
||||||
# ISOLATION: multi-seed RankIC ensemble, ablate-B generic-only feature set.
|
|
||||||
#
|
|
||||||
# Isolates the ensemble effect on the SP-5d rank signal. Same panel, segments,
|
|
||||||
# history (full backfilled 2016+) and feature set as the exp-9 ablate-B winner
|
|
||||||
# (generic-only sp_* families: jump,har,trend,hurst,signature), but replaces the
|
|
||||||
# single RankICLGBModel with a 5-seed RankICEnsembleLGBModel (42,7,2026,99,123)
|
|
||||||
# that averages per-day predictions.
|
|
||||||
#
|
|
||||||
# Differs from exp-15 (tac-rd-rank-ensemble, mlflow exp 15) ONLY by dropping the
|
|
||||||
# TA subset (rsi_14,roc_10,macd_hist,willr_14,atr_14) and the inter-asset xr_*
|
|
||||||
# features, so any change vs exp-15 is attributable to the feature set alone,
|
|
||||||
# and any change vs exp-9 is attributable to the ensemble + full history alone.
|
|
||||||
#
|
|
||||||
# Run:
|
|
||||||
# rd_run_workflow config_path=experiments/workflows/exp12_isolation_ensemble.yaml \
|
|
||||||
# experiment_name=tac-rd-rank-ensemble-isolated
|
|
||||||
# -----------------------------------------------------------------------------
|
|
||||||
{%- 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:///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
|
|
||||||
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: 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
|
|
||||||
@@ -1,136 +0,0 @@
|
|||||||
# -----------------------------------------------------------------------------
|
|
||||||
# EXP 15 - Strategy A (baseline): parallel reference model + TopkDropout.
|
|
||||||
#
|
|
||||||
# Model = RankICEnsembleLGBModel with PARALLEL=5 (thread-pool seed training,
|
|
||||||
# rank_ensemble.py) - the speedup means the 5-seed 3000-round ensemble trains in
|
|
||||||
# ~1/5th the wall time of the serial reference. Strategy = TopkDropout topk=10
|
|
||||||
# n_drop=2 risk_degree=0.95 (the reference's recorded strategy).
|
|
||||||
#
|
|
||||||
# Run:
|
|
||||||
# rd_run_workflow config_path=experiments/workflows/exp15-kelly-size/a_topk_baseline.yaml \
|
|
||||||
# experiment_name=tac-rd-kelly-size
|
|
||||||
# -----------------------------------------------------------------------------
|
|
||||||
{%- 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-kelly-size"
|
|
||||||
|
|
||||||
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"
|
|
||||||
parallel: 5
|
|
||||||
|
|
||||||
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: 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
|
|
||||||
@@ -1,144 +0,0 @@
|
|||||||
# -----------------------------------------------------------------------------
|
|
||||||
# EXP 15 - Strategy B: parallel reference model + KellyWeightStrategy.
|
|
||||||
#
|
|
||||||
# Model = RankICEnsembleLGBModel PARALLEL=5 (same as A). Strategy =
|
|
||||||
# KellyWeightStrategy (tac_qlib.contrib.strategy.kelly_weight): applies the
|
|
||||||
# Kelly criterion to position SIZING -
|
|
||||||
# f* = kelly_fraction * mu_i / var_i (Gaussian Kelly, mu_i > 0)
|
|
||||||
# with per-name mu/var estimated from the rolling signal history (no
|
|
||||||
# lookahead), floored/capped and normalized to the risk_degree leverage budget.
|
|
||||||
# This is the piece the reference's equal-weight TopkDropout never tunes: it
|
|
||||||
# weights names by edge/risk instead of equal-weight top-k.
|
|
||||||
#
|
|
||||||
# Run:
|
|
||||||
# rd_run_workflow config_path=experiments/workflows/exp15-kelly-size/b_kelly_weight.yaml \
|
|
||||||
# experiment_name=tac-rd-kelly-size
|
|
||||||
# -----------------------------------------------------------------------------
|
|
||||||
{%- 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-kelly-size"
|
|
||||||
|
|
||||||
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"
|
|
||||||
parallel: 5
|
|
||||||
|
|
||||||
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: KellyWeightStrategy
|
|
||||||
module_path: tac_qlib.contrib.strategy.kelly_weight
|
|
||||||
kwargs:
|
|
||||||
signal: "<PRED>"
|
|
||||||
topk: 10
|
|
||||||
lookback: 20
|
|
||||||
min_obs: 10
|
|
||||||
kelly_fraction: 0.5
|
|
||||||
max_weight: 0.15
|
|
||||||
min_weight: 0.0
|
|
||||||
risk_degree: 0.95
|
|
||||||
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