From trading idea to validated strategy
Wed, Jun 24, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Wed, Jun 24, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
What you'll learn
The five points where v1 quietly becomes wrong
Universe, label-time, portfolio rule, stress test, deployment ladder — every clean version is won or lost here.
How to read what v2 actually tells you
Take a plausible v1, find the weak handoff, run a targeted change, and accept the answer — even when v2 says stop.
Coding agents inside the loop, not bolted on
Where agents accelerate the work (probes, scripts, stress grids) and where judgment stays with you. The honest split.
Why this topic matters
A first-pass ML trading strategy almost never deploys as-is. What separates work that holds up is the discipline of running the loop: take a v1, identify what's really holding it back, run a targeted change, and read what v2 honestly tells you — including when it tells you to stop. The book shows first passes; R2P is where you actually run them.
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 (19,000+ 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.