Stefan Jansen’s Machine Learning for Algorithmic Trading is often cited as the bible for modern quants. But is it worth the hype? In this video, I provide a detailed, critical review of this essential book from a practitioner’s perspective.
My core argument is that while this book is an exemplary, hands-on playbook, its intense focus on implementation creates a significant barrier to entry. It brilliantly shows how to build sophisticated trading models but assumes the reader already has the deep technical foundation to overcome the hurdles of “data-first” quantitative finance.
In this review, I’ll cover:
Jansen’s core thesis: Modern investing is data.
A breakdown of the book’s strongest sections: Chapter 4 (“Financial Feature Engineering”) and Chapter 5 (“Portfolio Optimization”)
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My critical take: Where I agree with Jansen’s “rational vs. behavioral” alpha factor framework and where I find the book deficient (e.g., data costs, beginner-unfriendliness, and code-level hurdles).
#bookreview #algorithmictrading #machinelearning #quantitativefinance #finance #datafirst #stefanjansen #python #trading #quant
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