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signal-classification

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ML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold optimization

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# 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
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