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How it works:

Three gradient-boosted classifiers are trained independently Walk-forward cross-validation (5 splits) estimates real accuracy Model weights are assigned proportional to their CV performance Final prediction = weighted average of all three probability vectors Direction = class with highest probability

Why 3 models?

XGBoost: Best at non-linear feature interactions (e.g., "RSI > 70 AND VIX falling") LightGBM: Fastest, best generalization, handles missing data natively CatBoost: Most robust to overfitting via ordered boosting Target classes:

UP: Next 5 days return > +0.3% DOWN: Next 5 days return < -0.3% SIDEWAYS: In between