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