Fix: actual requirements.txt (previous upload accidentally put README content here)

requirements.txt

@@ -1,27 +1,7 @@
----
-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).
+gradio>=5.49,<6
+matplotlib>=3.8
+numpy>=1.26,<3
+pandas>=2.1
+scikit-learn>=1.3
+scipy>=1.11
+xgboost>=2.0