alpha_lab/cta_1d

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]