The practical challenge in applying ML to markets is not just model selection but assembling realistic data pipelines, engineered features, and backtests that avoid common biases. This repository bundles worked examples that bridge academic ideas and deployable trading experiments, making the book's methods reproducible.
What Sets It Apart
- Executable pedagogy: 150+ notebooks (many in executed state) that map directly to the book’s 23 chapters and appendix, so you can follow concepts with runnable examples — useful for learning and reproduction.
- Broad data & methods coverage: examples span market and fundamental data, alternative text and image sources, feature engineering, tree methods, deep nets (CNN/RNN), GANs for synthetic series, and deep reinforcement learning — so you can compare classical and modern approaches on the same workflow.
- End-to-end workflow focus: emphasizes the ML4T workflow (data sourcing → features → model tuning → strategy design → realistic backtesting) rather than isolated model recipes — this helps reveal practical gotchas (look-ahead bias, survivorship, execution assumptions).
Who It's For & Tradeoffs
Great fit if you are a quant researcher, data scientist, or advanced practitioner wanting reproducible, book-linked examples to prototype ML-driven strategies and learn best practices in financial feature engineering and backtesting. Look elsewhere if you only want lightweight tutorials or a production trading framework — notebooks are research-oriented and require nontrivial environment setup, licensed/paid data for some examples, and domain knowledge to adapt for live execution.
