# 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]