README.md

metadata

title: AAPL Triple-Barrier Direction Classifier
emoji: 📊
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit

AAPL Triple-Barrier Direction Classifier (educational)

Reference-backed financial-ML demo. XGBoost classifier trained on fractionally-differenced features and triple-barrier labels (López de Prado, Advances in Financial Machine Learning, Ch.3 + Ch.5).

This is an educational portfolio artifact, not a trading signal. Test-set accuracy ~38% on a 3-class label set (random = 33%, p<0.05 in 3 of 5 purged folds). Directional accuracy when the model picks a side is ~36% — worse than coin-flip. Do not trade real money on this.

Full source, technical writeup, and lessons-learned: github.com/moccaram/DataSynth.