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Advanced ML Trading System
Implementation of methods from Lopez de Prado's Advances in Financial Machine Learning. CUSUM filters for event detection, triple barrier labeling, meta-labeling for signal confidence, purged K-fold cross-validation, and Kelly criterion for position sizing. Built in Python with XGBoost.
Full case study coming soon.
The interactive write-up for this project is in progress. Check back shortly.
Key Highlights
▸Triple barrier labeling and meta-labeling
▸Sequential bootstrap and purged K-fold CV
▸Kelly criterion dynamic bet sizing
▸Deflated Sharpe ratio metrics
Technologies
Quantitative FinanceMLPythonXGBoost