alpha_lab/stock_15m/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