From f4ed5e42fe56a02ad3961f016f11c2ad8f0f355a Mon Sep 17 00:00:00 2001 From: zhaoli Date: Thu, 20 Aug 2026 11:35:04 +0000 Subject: [PATCH] finish experiment 55 (exp/55-adaptive-short-window-retrain-test-the-4) --- code/MANIFEST.txt | 2 +- .../m2-sharpe22_1y_2021.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_1y_2023.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_1y_2024.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_1y_2025.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_1y_2026.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_2y_2021.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_2y_2023.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_2y_2024.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_2y_2025.yaml | 96 +++++++++++++++++++ .../m2-sharpe22_2y_2026.yaml | 96 +++++++++++++++++++ 11 files changed, 961 insertions(+), 1 deletion(-) create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2021.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2023.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2024.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2025.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2026.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2021.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2023.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2024.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2025.yaml create mode 100644 workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2026.yaml diff --git a/code/MANIFEST.txt b/code/MANIFEST.txt index 977a167..b69542d 100644 --- a/code/MANIFEST.txt +++ b/code/MANIFEST.txt @@ -1,5 +1,5 @@ # TradeAC custom-qlib-code snapshot (auto-generated) -# parent repo HEAD : 1b0a3be60535232933d21fa123d202011d2c416c +# parent repo HEAD : 81ee9f1a6e70f68a4bc08f28d868bbc1c71a3810 # tac-qlib/tac_qlib/contrib # tac-qlib/tac_qlib/data # per-file hashes (git hash-object): diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2021.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2021.yaml new file mode 100644 index 0000000..a0aad34 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2021.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2021 (trace exp 55) — 1y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-1y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2022-01-07" + fit_start_time: "2019-08-30" + fit_end_time: "2020-08-31" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$open,$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2019-08-30", fit_end_time: "2020-08-31" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2019-08-30", fit_end_time: "2020-08-31" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2019-08-30", "2020-08-31"] + valid: ["2020-09-01", "2020-12-31"] + test: ["2021-01-04", "2021-12-31"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2021-01-04" + end_time: "2021-12-31" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2023.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2023.yaml new file mode 100644 index 0000000..17c9977 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2023.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2023 (trace exp 55) — 1y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-1y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2024-01-07" + fit_start_time: "2021-08-31" + fit_end_time: "2022-08-31" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2022-08-31" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2022-08-31" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2021-08-31", "2022-08-31"] + valid: ["2022-09-01", "2022-12-30"] + test: ["2023-01-03", "2023-12-29"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2023-01-03" + end_time: "2023-12-29" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2024.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2024.yaml new file mode 100644 index 0000000..258c459 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2024.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2024 (trace exp 55) — 1y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-1y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2025-01-07" + fit_start_time: "2022-08-31" + fit_end_time: "2023-08-31" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2023-08-31" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2023-08-31" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2022-08-31", "2023-08-31"] + valid: ["2023-09-01", "2023-12-29"] + test: ["2024-01-02", "2024-12-31"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2024-01-02" + end_time: "2024-12-31" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2025.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2025.yaml new file mode 100644 index 0000000..d0e09c8 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2025.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2025 (trace exp 55) — 1y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-1y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2026-01-07" + fit_start_time: "2023-08-31" + fit_end_time: "2024-08-30" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2023-08-31", fit_end_time: "2024-08-30" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2023-08-31", fit_end_time: "2024-08-30" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2023-08-31", "2024-08-30"] + valid: ["2024-09-03", "2024-12-31"] + test: ["2025-01-02", "2025-12-31"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2025-01-02" + end_time: "2025-12-31" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2026.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2026.yaml new file mode 100644 index 0000000..1a68f13 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_1y_2026.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2026 (trace exp 55) — 1y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-1y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2026-08-10" + fit_start_time: "2024-09-03" + 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_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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2024-09-03", fit_end_time: "2025-09-01" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2024-09-03", fit_end_time: "2025-09-01" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2024-09-03", "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: "", 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: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2021.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2021.yaml new file mode 100644 index 0000000..8772f54 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2021.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2021 (trace exp 55) — 2y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-2y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2022-01-07" + fit_start_time: "2018-08-31" + fit_end_time: "2020-08-31" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2018-08-31", fit_end_time: "2020-08-31" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2018-08-31", fit_end_time: "2020-08-31" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2018-08-31", "2020-08-31"] + valid: ["2020-09-01", "2020-12-31"] + test: ["2021-01-04", "2021-12-31"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2021-01-04" + end_time: "2021-12-31" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2023.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2023.yaml new file mode 100644 index 0000000..0d8b02a --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2023.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2023 (trace exp 55) — 2y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-2y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2024-01-07" + fit_start_time: "2020-08-31" + fit_end_time: "2022-08-31" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2020-08-31", fit_end_time: "2022-08-31" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2020-08-31", fit_end_time: "2022-08-31" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2020-08-31", "2022-08-31"] + valid: ["2022-09-01", "2022-12-30"] + test: ["2023-01-03", "2023-12-29"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2023-01-03" + end_time: "2023-12-29" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2024.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2024.yaml new file mode 100644 index 0000000..737f440 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2024.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2024 (trace exp 55) — 2y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-2y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2025-01-07" + fit_start_time: "2021-08-31" + fit_end_time: "2023-08-31" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2023-08-31" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2021-08-31", fit_end_time: "2023-08-31" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2021-08-31", "2023-08-31"] + valid: ["2023-09-01", "2023-12-29"] + test: ["2024-01-02", "2024-12-31"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2024-01-02" + end_time: "2024-12-31" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2025.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2025.yaml new file mode 100644 index 0000000..f027334 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2025.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2025 (trace exp 55) — 2y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-2y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2026-01-07" + fit_start_time: "2022-08-31" + fit_end_time: "2024-08-30" + freq: day + lake_root: "{{ LAKE }}" + market: US + label: "Ref($close,-6)/Ref($close,-1)-1" + feature_fields: "$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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2024-08-30" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2022-08-31", fit_end_time: "2024-08-30" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2022-08-31", "2024-08-30"] + valid: ["2024-09-03", "2024-12-31"] + test: ["2025-01-02", "2025-12-31"] + + 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: "", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 } + backtest: + start_time: "2025-01-02" + end_time: "2025-12-31" + account: 1000000 + benchmark: SPY + exchange_kwargs: + codes: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d diff --git a/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2026.yaml b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2026.yaml new file mode 100644 index 0000000..636c911 --- /dev/null +++ b/workflows/bt-m2-sharpe22-adaptive/m2-sharpe22_2y_2026.yaml @@ -0,0 +1,96 @@ +# Adaptive short-window retrain: m2-sharpe22 / test 2026 (trace exp 55) — 2y training window +# Hypothesis: a short recent training window adapts to the target year's regime and transfers the edge. +{% set LAKE = TAC_LAKE_DIR %} +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-bt-m2-sharpe22-adaptive-2y" } + +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: "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" + start_time: "2015-01-03" + end_time: "2026-08-10" + fit_start_time: "2023-09-01" + 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_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,sp_sharpe_22" + infer_processors: + - { class: DropAllNaN, kwargs: { fit_start_time: "2023-09-01", fit_end_time: "2025-09-01" } } + - { class: ProcessInf, kwargs: {} } + - { class: CSRankNorm, kwargs: {} } + - { class: ZScoreNorm, kwargs: { fit_start_time: "2023-09-01", fit_end_time: "2025-09-01" } } + - { class: Fillna, kwargs: {} } + segments: + train: ["2023-09-01", "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: "", 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: "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" + deal_price: $close + freq: day + open_cost: 0.0005 + close_cost: 0.0015 + min_cost: 5.0 + risk_analysis_freq: 1d