In this work, the application of the Triple-Barrier Method and Meta-Labeling techniques are explored using XGBoost to develop a sentiment-based trading signal for the S&P 500 stock market index. The results indicate that sentiment data possess predictive power; however, substantial work remains before a fully implementable strategy can be realized.

Advanced Financial Machine Learning Framework - Production-grade quant trading tools based on Lopez de Prado

End-to-end ML system for prediction market trading — 521K markets, 78 features, 7 model architectures, walk-forward validation, live VPS A/B across 7 configs. Honest research-stop on alpha decay (NO-GO verdict). AFML methodology: Purged K-Fold, Deflated Sharpe Ratio, meta-labeling, focal loss.

Systematic signal refinement framework using point-in-time data, triple-barrier labeling, calibrated ML models, and probability-aware portfolio construction.

Implement robust backtesting using Marcos Lopez de Prado’s methods to reduce overfitting and improve live trading strategy reliability.