You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
26 lines
774 B
26 lines
774 B
# Stock 15-Minute Return Prediction
|
|
|
|
Experiments for predicting stock 15-minute returns using high-frequency features.
|
|
|
|
## Data
|
|
|
|
- **Features**: alpha158 computed on 1-minute data
|
|
- **Target**: 15-minute forward returns (close[t+16]/close[t+1]-1)
|
|
- **Normalization**: industry, cs_zscore, or dual
|
|
|
|
## Notebooks
|
|
|
|
| Notebook | Purpose |
|
|
|----------|---------|
|
|
| `01_data_exploration.ipynb` | Load and explore 15m data structure |
|
|
| `02_baseline_model.ipynb` | Train baseline XGBoost model |
|
|
|
|
## Methodology
|
|
|
|
1. Load 1-minute kline data via Polars lazy frames
|
|
2. Compute/retrieve alpha158 features
|
|
3. Calculate 15-minute forward returns
|
|
4. Apply normalization (industry-neutralized or cross-sectional z-score)
|
|
5. Train gradient boosting models
|
|
6. Evaluate with IC and backtest
|