The Quant’s Playbook: A Critical Review of Jansen’s “Machine Learning for Algorithmic Trading”

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).

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