Stefan Jansen
835 Subscribers
Author, ML for Trading · Founder, Applied AI · Investing since 2013
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 (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.
Courses
Multi-day, guided programs to get real results
Workshops
Single-day, hands-on sprints to practice new skills
Workshop5.5 hours
Engineering a Multi-Agent Forecasting System
Build a multi-agent forecasting system you can audit — in one day.
4.9(8)
·Sep 19Workshop4 hours
Loop Engineering: Reliable Work From Coding Agents
Reliable work from coding agents starts with the loop, not the prompt.
Oct 10
View workshop Lightning Lessons
Free, interactive sessions to explore new topics
Lightning Lesson30 minutes
Why Multi-Agent Systems Break, and How To Fix It
LIVE·Wed, Nov 4, 12:00 PM
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How to Be Productive with Coding Agents, Beyond Code
LIVE·Wed, Sep 30, 12:00 PM
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How to Engineer a Multi-Agent System
LIVE·Wed, Sep 9, 12:00 PM
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Managing Your Strategy Research Process
LIVE·Wed, Aug 26, 12:00 PM
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Navigate ML for Trading, 3rd Edition
WATCH
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From Prompts to Loops: Engineering Reliable Agents
WATCH
Watch now Alumni reviews
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.
Amir
Cohort 2
CEO · Ocean Reviver Inc.
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.
Atsushi
Cohort 2
Machine Learning Engineer · Open Database Associates BV
Great workshop! I learned how to build and inspect an end-to-end multi-agent forecasting system—from ReAct agents and tools to combining forecasts, calibrating them, and evaluating the results.
Mark
Cohort 2
developer · *
The insights and expertise Stefan shared in this workshop have really taken my AI coding skills to the next level.
Karry
Cohort 1
R&D Lead · Beifang Investigation, Design & Research Co.,Ltd.
Excellent course for anyone serious about using coding agents in production. Stefan goes beyond basic prompting to build reliable, repeatable engineering workflows with clear planning, verification, and execution practices. I left with practical techniques I can apply immediately to AI-assisted software development—highly recommended for engineers, technical founders, and engineering leaders.
Amir
Cohort 1
CEO · Ocean Reviver Inc.