signal-classificationlisted

# Signal Classification Predict whether an asset's price will move up or down over a forward horizon using supervised machine learning classifiers. This skill covers the full pipeline: label creation, model training, walk-forward validation, feature importance analysis, and threshold optimization for trading applications. ## Why Tree-Based Models Dominate Trading ML XGBoost and LightGBM are the workhorses of quantitative trading ML for good reason: - **Non-linear relationships**: Financial features interact in complex, non-linear ways that trees capture naturally - **Robust to feature scale**: No need to normalize or standardize inputs — trees split on rank order - **Built-in feature importance**: Understand which features drive predictions without separate analysis - **Fast training and inference**: Train on thousands of samples in seconds, predict in microseconds - **Handle missing values**: Native support for NaN without imputation hacks - **Regularization built in**: max_depth, min_child_weight, subsample all prevent overfitting Linear models and deep learning have their place, but for tabular trading features with fewer than 100k samples, gradient-boosted trees consistently outperform alternatives. ## Classification Types ### Binary Classification The simplest and most common setup. Predict whether forward returns exceed a threshold: - **Up signal**: forward return > +1% - **Down signal**: forward return < -1% - **Neutral (excluded)**: -1% to +1% — drop these fr