Stefan Jansen

Stefan Jansen

1.9K Subscribers

Author, ML for Trading · Founder, Applied AI · Investing since 2013

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Stefan Jansen takes you from idea to deployable trading strategy

Stefan is the author of ML for Trading — the book and open-source companion code (20,000+ GitHub stars) that have become a practitioner reference for applying ML to financial markets. The 2026 third edition expands to nine cross-asset case studies, with a foreword by Antonio Gulli, Senior Director, Google. He maintains the Zipline fork the quant community relies on, and built the six-library stack — data to live — behind the third edition's case studies. Investment partner since 2013, he has built trading platforms and live strategies across asset classes. In 2016 he founded Applied AI, which brings production ML to investment teams and other data-rich verticals. He has taught ML to 110,000+ professionals through DataCamp and General Assembly, incl. at Bloomberg and BlackRock.

Alumni reviews

Paolo

Live cohort
risk manager · consultinvest

ML for Trading: From Research to Production

This is a highly professional course — in its content, in the standard of the lectures, and in the rigour with which it is delivered. Following Stefan's lessons was a genuine pleasure, and I have rarely come across a course so carefully crafted and so thoroughly prepared down to the last detail. The material is advanced and current, yet it is presented clearly and with a hands-on approach; on top of that, Stefan was always available and extremely generous in going deeper on anything I was curious about. I would recommend this course to professionals in the financial industry and to students on Master's programmes in Finance or Data Science who want to learn not only the techniques and the models, but also — and perhaps more importantly — the working method. Taking this course felt like stepping into the operations room of a hedge fund, or onto an advanced proprietary trading desk at an investment bank.

Greg

Live cohort
CIO · VOFCO, LLC

ML for Trading: From Research to Production

This was a great course! There is more material in Stefan's book, accompanying GitHub and in this course than there is in many master's degrees. This is definitely high level and not a 101 course. You will get a tremendous amount from this course if you are prepared. Stefan was also always willing to answer emails and answer questions. Highly recommended!

Pedram

Live cohort
Professional practice Associate Professor · Utah State University (Department of Data Analytics and Information Systems)

ML for Trading: From Research to Production

Stefan’s knowledge of machine learning and quantitative trading is exceptional. He combines rigorous theory with deep real-world experience and explains complex ideas clearly. You will learn a tremendous amount from his practical insights and thoughtful guidance. I highly recommend this course to anyone serious about applying machine learning to financial markets.

Amir

Live cohort
CEO · Ocean Reviver Inc.

Engineering a Multi-Agent Forecasting System

Excellent course for anyone serious about building practical AI agent systems. Stefan does a great job connecting the concepts behind multi-agent architectures with hands-on engineering and real-world applications. I especially appreciated the focus on building reliable workflows rather than simply experimenting with LLMs. I’m applying what I learned to develop multi-agent systems for complex research and government proposal development, where agents can specialize in research, analysis, writing, compliance, and review—highly recommended for anyone looking to move from using AI assistants to engineering robust agentic systems.

Atsushi

Live cohort
Machine Learning Engineer · Open Database Associates BV

Engineering a Multi-Agent Forecasting System

The course was very well structured and thoughtfully prepared. I particularly appreciated its coverage of current LLM implementation patterns, which made the course highly relevant and practical.