Feature Engineering in Quantitative Trading

With the popularization of AI, feature engineering is becoming increasingly important in quantitative research. Its core encompasses multi-dimensional data cleaning, feature extraction and selection, construction and optimization, and other steps. This presentation will analyze how to extract key features from massive data through a logical framework analysis and practical case sharing (such as the LASSO attribution model in FOF fund investment), and combine AI and statistical modeling methods to achieve feature optimization, improve trading strategy performance, and provide a more efficient support path for quantitative research and intelligent investment.