XGBoost for Trading: Why Gradient Boosting Beats Deep Learning on Price Data

XGBoost Bitcoin trading model using seven engineered inputs

Our companion article explains why the LSTM branch did not survive. This one explains why XGBoost for trading remains the production backbone on BitcoinAI.Pro: not because gradient boosting is universally superior, but because it fits the size, structure and audit requirements of the BTCUSD D1 problem we measured. The current Frontier Model uses a frozen … Read more

Walk-Forward Analysis for Bitcoin Trading: A Complete Worked Example

Bitcoin walk-forward analysis comparing the frozen Frontier model with rolling refits

Most explanations of walk-forward analysis for Bitcoin trading stop at a diagram: train here, test there, move the window and repeat. The useful part is harder: deciding the rules before seeing the result, preserving chronology, and accepting what happens when a model you already like fails to reproduce itself. BitcoinAI.Pro has now run more than … Read more