Free Lesson
Open-Source Tools for ML Trading Research
30 min
Nov 25, 2026 11:00 AM
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What you'll learn
Choose the tools for your research workflow
Map ml4t-data, ml4t-engineer, ml4t-diagnostic, and ml4t-backtest to the steps in a trading experiment.
Follow an example from data to a trading simulation
See how market data, features, signal diagnostics, and backtest results connect in the ML4T notebooks.
Start with the documented examples
Find installation guides and example notebooks; see what runs locally and which data sources require an account.
Why this topic matters
Building an ML trading experiment involves market data, features, validation, and a realistic trading simulation. Take a practical tour of the open-source ML4T libraries behind the book, see how their inputs and outputs connect, and find the documented examples you can run. We'll follow one research example and discuss where data access, costs, and validation still require your judgment.
