Portfolio Optimization Book

Portfolio Optimization: Theory and Application Daniel P. Palomar Cambridge University Press, 2025.

Book available here: pdf and online html

For purchase here: Cambridge University Press, Amazon, Barnes & Noble, Bookshop.org, Hive, Indigo, Hatchards, Hugendubel

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

To contribute, check the developer GitHub webpage.

Chapters

Work in progress, Python code coming up in the coming weeks…

• Chapter 1 - Introduction: slides

Part I - Financial Data

• Chapter 2 - Financial Data: Stylized Facts: slides , R code , Python code

• Chapter 3 - Financial Data: I.I.D. Modeling: slides , R code , Python code

• Chapter 4 - Financial Data: Time Series Modeling: slides , R code

• Chapter 5 - Financial Data: Graphs

Part II - Portfolio Optimization

• Chapter 6 - Portfolio Basics: slides , R code

• Chapter 7 - Modern Portfolio Theory: slides , R code

• Chapter 8 - Portfolio Backtesting: slides , R code

• Chapter 9 - High-Order Portfolios: slides , R code

• Chapter 10 - Portfolios with Alternative Risk Measures: slides , R code

• Chapter 11 - Risk Parity Portfolios: slides , R code

• Chapter 12 - Graph-Based Portfolios: slides , R code

• Chapter 13 - Index Tracking Portfolios: slides , R code

• Chapter 14 - Robust Portfolios: slides , R code

• Chapter 15 - Pairs Trading Portfolios: slides , R code

• Chapter 16 - Deep Learning Portfolios

Appendices

• Appendix A - Convex Optimization Theory: slides

• Appendix B - Optimization Algorithms: slides , R code

Miscellanea

• 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 R: Primer on R for Finance, Solvers in R

• Resources for Python: Primer on Python for Finance, Solvers in Python

Contact

Feel free to reach out to me via email or follow me on X (formerly Twitter).