Feature Engineering for Finance

Feature engineering is the process of selecting, transforming, and creating input variables for machine learning models to improve their predictive accuracy. In finance, this involves converting raw data like price, volume, and order flow into meaningful indicators.

For example, instead of just using price, a feature might be the rolling volatility, the relative strength index, or the order book imbalance. Good features capture the essential information that drives market dynamics while filtering out the noise.

In crypto, this can include on-chain metrics, social media sentiment, and exchange-specific data. The quality of these features is often more important than the complexity of the model itself.

Effective feature engineering is what allows a model to "understand" the market's underlying drivers. It is a critical step in the quantitative trading pipeline.