---
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.

![Gradio interface](app_screenshot.png)

Full source, technical writeup, and lessons-learned:
[github.com/moccaram/DataSynth](https://github.com/moccaram/DataSynth).