Wed, Oct 7, 2026
3:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
ML for Trading: Foundations
Stefan Jansen
Founder, Applied AI. Systematic investment partner since 2013. CFA.
Wed, Oct 7, 2026
3:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
55 students
Go deeper with a course
ML for Trading: Foundations
Stefan Jansen
Founder, Applied AI. Systematic investment partner since 2013. CFA.
What you'll learn
See where an agent carries real work
Which parts of a research pipeline a coding agent can build, and what you must specify before it starts.
Keep the judgment that decides the result
What the label means, whether validation prevents look-ahead, and whether the number at the end is real.
Read a result honestly, agent or not
Naive against corrected inference, and zero-cost against costed, on the same strategy.
Why this topic matters
Coding agents and research agents now write a large share of the code in a quantitative workflow, and the demos make it look like the whole job. It is not. This session runs the ML for Trading workflow end to end, shows the two places an agent genuinely carries the work, and names the decisions that stay with the person running it. It previews the entry-point workshop, ML for Trading in the Age of AI Agents.
You'll learn from
Stefan Jansen
Author, ML for Trading · Founder, Applied AI · Investing since 2013
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.