History

guofu 89bd1a528e Extract RobustZScoreNorm parameters and add from_version() method - Add extract_qlib_params.py script to extract pre-fitted mean/std parameters from Qlib's proc_list.proc and save as reusable .npy files with metadata.json - Add RobustZScoreNorm.from_version() class method to load saved parameters by version name, supporting multiple parameter versions coexistence - Update dump_polars_dataset.py to use from_version() instead of loading parameters directly from proc_list.proc - Update generate_beta_embedding.py to use qshare's filter_instruments for stock universe filtering - Save parameters to data/robust_zscore_params/csiallx_feature2_ntrla_flag_pnlnorm/ with 330 features (158 alpha158_ntrl + 158 alpha158_raw + 7 market_ext_ntrl + 7 market_ext_raw) - Update README.md with documentation for parameter extraction and usage Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>		2 days ago
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src	Extract RobustZScoreNorm parameters and add from_version() method	2 days ago
01_data_check.ipynb	Initial alpha_lab structure\n\n- Notebook-centric experiment framework\n- CTA 1D and Stock 15m tasks\n- Minimal common utilities\n- Manual experiment tracking	3 weeks ago
02_label_analysis.ipynb	Initial alpha_lab structure\n\n- Notebook-centric experiment framework\n- CTA 1D and Stock 15m tasks\n- Minimal common utilities\n- Manual experiment tracking	3 weeks ago
03_baseline_xgb.ipynb	Initial alpha_lab structure\n\n- Notebook-centric experiment framework\n- CTA 1D and Stock 15m tasks\n- Minimal common utilities\n- Manual experiment tracking	3 weeks ago
03_baseline_xgb_executed.ipynb	Add configuration files and alpha158_beta pipeline	4 days ago
04_blend_comparison.ipynb	Initial alpha_lab structure\n\n- Notebook-centric experiment framework\n- CTA 1D and Stock 15m tasks\n- Minimal common utilities\n- Manual experiment tracking	3 weeks ago
README.md	Initial alpha_lab structure\n\n- Notebook-centric experiment framework\n- CTA 1D and Stock 15m tasks\n- Minimal common utilities\n- Manual experiment tracking	3 weeks ago
__init__.py	Add CTA 1D Parquet loader and data requirements	2 weeks ago
config.yaml	Add configuration files and alpha158_beta pipeline	4 days ago
config_parquet.yaml	Add CTA 1D Parquet loader and data requirements	2 weeks ago

README.md

CTA 1-Day Return Prediction

Experiments for predicting CTA (Commodity Trading Advisor) futures 1-day returns.

Data

Features: alpha158, hffactor
Labels: Return indicators (o2c_twap1min, o2o_twap1min, etc.)
Normalization: dual (blend of zscore, cs_zscore, rolling_20, rolling_60)

Notebooks

Notebook	Purpose
`01_data_check.ipynb`	Load and validate CTA data
`02_label_analysis.ipynb`	Explore label distributions and blending
`03_baseline_xgb.ipynb`	Train baseline XGBoost model
`04_blend_comparison.ipynb`	Compare different normalization blends

Blend Configurations

The label blending combines 4 normalization methods:

zscore: Fit-time mean/std normalization
cs_zscore: Cross-sectional z-score per datetime
rolling_20: 20-day rolling window normalization
rolling_60: 60-day rolling window normalization

Predefined weights (from qshare.config.research.cta.labels):

equal: [0.25, 0.25, 0.25, 0.25]
zscore_heavy: [0.5, 0.2, 0.15, 0.15]
rolling_heavy: [0.1, 0.1, 0.3, 0.5]
cs_heavy: [0.2, 0.5, 0.15, 0.15]
short_term: [0.1, 0.1, 0.4, 0.4]
long_term: [0.4, 0.2, 0.2, 0.2]

Default: [0.2, 0.1, 0.3, 0.4]