Feature Engineering

Feature engineering is the process of transforming raw data into meaningful inputs for quantitative models. It involves selecting, cleaning, normalizing, and combining variables to improve predictive power. Examples include creating momentum indicators from prices, profitability ratios from financial statements, or volatility measures from returns. Good feature engineering often matters more than complex algorithms. Well-designed features capture economic intuition and reduce noise. Quant workflows emphasize stability, interpretability, and robustness when engineering features. Overly intricate transformations increase the risk of overfitting. Feature engineering bridges domain knowledge and machine learning, converting financial intuition into systematic signals.