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