Hands-On Machine Learning for Algorithmic Trading

Domain expertise helps unlock value in data

We emphasized that data is a necessary driver of successful ML applications, but that domain expertise is also crucial to inform strategic direction, feature engineering and data selection, and model design.

In any domain, practitioners have theories about the drivers of key outcomes and relationships among them. Finance stands out by the amount of relevant quantitative research, both theoretical and empirical. Marcos López de Prado and others (see GitHub for references https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Trading) criticize most empirical results given pervasive data mining that may invalidate the findings. Nonetheless, a robust understanding of how financial markets ...