18 Python notebooks
This page hosts the Jupyter notebooks that make the Python version of the monograph (in its first edition).
Below, the official notebooks are naturally split into chapters.
We also provide an independent implementation by Zheyuan Shen, hosted on
Google Drive.
Chapter 1: Notations & data
Chapter 2: Introduction
Chapter 3: Factor investing and asset pricing anomalies
Chapter 4: Data pre-processing
Chapter 5: Penalized regressions
Chapter 6: Tree-based methods
Chapter 7: Neural networks
Chapter 8: Support vector machines
Chapter 10: Validating & tuning
Chapter 11: Ensemble models
Chapter 12: Backtesting
Chapter 13: Interpretability
Chapter 14: Causality and non-stationarity
Chapter 15: Unsupervised learning
Chapter 16: Reinforcement learning