Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Quant 4.0 has three key differentiating components. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories'' Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules.

Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc.

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