Bitcoin Trading with XGBoost: What 70,000 Hours of Data Actually Prove
Quick answer: XGBoost on hourly BTC/USDT data can produce positive net-of-cost returns — one 2026 study (arXiv:2606.00060) reports ~65% annualised return with Sharpe above 1 using a cost-aware execution filter — but the edge is concentrated in execution discipline, not model architecture, and it does not survive strict statistical significance tests against buy-and-hold. Translation: the model is a tool, not a ATM.
Most "AI predicts Bitcoin" posts stop at model.fit(X, y) and a pretty curve. This one is different. It is about what ~70,000 hourly observations and a 27-fold walk-forward protocol actually show — and where the hype collapses.
Why trees, not a neural net, for BTC
XGBoost remains the sane default for tabular market data because:
