Free Lesson
How AI Agents Change the ML for Trading Workflow
30 min
Oct 7, 2026 11:00 AM
What you'll learn
Connect the research decisions
Follow an ETF example from hypothesis and return labels to model evaluation and portfolio rules.
Specify work for a coding agent
See how a written brief defines the inputs, forecast horizon, and checks for the generated code.
Inspect a research-agent forecast
Trace specialist evidence, disagreements, and aggregation through a recorded forecast.
Why this topic matters
See how a trading hypothesis becomes a data specification, model forecast, and portfolio decision. Selected ETF examples show how a coding-agent brief guides implementation and how to inspect a research-agent forecast. The lesson introduces coding agents and agent engineering within the research workflow. The October 10 workshop adds guided notebook practice and live Q&A.
