Free Lesson

Managing Your Strategy Research Process

Hosted by Stefan Jansen

Wed, Aug 26, 2026

4:00 PM UTC (30 minutes)

Virtual (Zoom)

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Machine Learning for Trading: From Research to Production
Stefan Jansen
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What you'll learn

Identify Key Research Decisions

Understand the decisions you need to take from trading idea to live deployment, and how to evaluate your options

Diagnose the Binding Constraints

Analyze empirical ML and trading strategy results to prioritize high-leverage opportunities for improvement

Choose a controlled next experiment

Select the most effective experiment that could change a proceed, revise, monitor, or stop decision.

Why this topic matters

Trading strategy research requires numerous decisions from inception to deployment. This lesson lays out these decisions and shows how to assess the available options, given the research results, to effectively iterate on your chosen strategy toward deployment. It outlines the concept behind our ML for Trading: Research to Production course that teaches a systematic process for strategy research.

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 (20K+ 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.


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 100K+ professionals through DataCamp and General Assembly, incl. at Bloomberg and BlackRock.

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