Portfolio Optimization Book

Portfolio Optimization: Theory and Application
Daniel P. Palomar

Cambridge University Press, 2025.

Book available here: pdf and online html

This is the homepage for the Portfolio Optimization Book. It contains slides, code examples (R and Python), exercises with solutions, and data.

To contribute, check the developer GitHub webpage.

Chapters

Chapter 2 – Financial Data: Stylized Facts: slides, R code, Python code, exercises, solutions
Chapter 3 – Financial Data: I.I.D. Modeling: slides, R code, Python code, exercises, solutions
Chapter 4 – Financial Data: Time Series Modeling: slides, R code, Python code, exercises, solutions
Chapter 5 – Financial Data: Graphs: slides, R code, exercises

Chapter 6 – Portfolio Basics: slides, R code, Python code, exercises, solutions
Chapter 7 – Modern Portfolio Theory: slides, R code, Python code, exercises, solutions
Chapter 8 – Portfolio Backtesting: slides, R code, Python code, exercises
Chapter 9 – High-Order Portfolios: slides, R code, exercises, solutions
Chapter 10 – Portfolios with Alternative Risk Measures: slides, R code, Python code, exercises, solutions
Chapter 11 – Risk Parity Portfolios: slides, R code, Python code, exercises, solutions
Chapter 12 – Graph-Based Portfolios: slides, R code, exercises
Chapter 13 – Index Tracking Portfolios: slides, R code, Python code, exercises, solutions
Chapter 14 – Robust Portfolios: slides, R code, exercises
Chapter 15 – Pairs Trading Portfolios: slides, R code, exercises, solutions
Chapter 16 – Deep Learning Portfolios: slides, exercises, solutions

Sample slide title page with customizable course info on a textbox.
Stock and crypto data used in the sample code can be conveniently found at https://github.com/dppalomar/pob
Resources for Python: Primer on Python for Finance, Solvers in Python, portfolio library skfolio
Resources for R: Primer on R for Finance, Solvers in R