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alpha_lab/stock_15m
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guofu 966c17d7a9
Consolidate Python code under src/ directories 		3 weeks ago
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src Consolidate Python code under src/ directories 3 weeks ago
01_data_exploration.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_baseline_model.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 Consolidate Python code under src/ directories 3 weeks ago

README.md

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