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26 lines
774 B
26 lines
774 B
3 weeks ago
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# Stock 15-Minute Return Prediction
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Experiments for predicting stock 15-minute returns using high-frequency features.
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## Data
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- **Features**: alpha158 computed on 1-minute data
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- **Target**: 15-minute forward returns (close[t+16]/close[t+1]-1)
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- **Normalization**: industry, cs_zscore, or dual
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## Notebooks
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| Notebook | Purpose |
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|----------|---------|
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| `01_data_exploration.ipynb` | Load and explore 15m data structure |
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| `02_baseline_model.ipynb` | Train baseline XGBoost model |
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## Methodology
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1. Load 1-minute kline data via Polars lazy frames
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2. Compute/retrieve alpha158 features
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3. Calculate 15-minute forward returns
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4. Apply normalization (industry-neutralized or cross-sectional z-score)
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5. Train gradient boosting models
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6. Evaluate with IC and backtest
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