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 companion website of the Portfolio Optimization Book with slides, code examples (Python and R), exercises, and data.

Recognition

Read endorsements from leading researchers and practitioners.

This book has been featured by:

• MOSEK — Industry-leading conic optimization solver
• skfolio (paper) — Python portfolio optimization library built on scikit-learn
• StarQube — Portfolio construction & management platform (listed first in recommended literature)
• awesome-quant — Curated list of quant resources (25k+ ★)
• microprediction/precise — Curated portfolio optimization literature by Peter Cotton
• Vincent Zoonekynd’s Article & Book Summaries — Well-known quant summarizer with a detailed chapter-by-chapter summary of the book’s financial data modeling and portfolio optimization content (1,100+ page compendium)

Chapters

• Chapter 1 – Introduction: slides

Part I – Financial Data

• 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

Part II – Portfolio Optimization

• 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

Appendices

• Appendix A – Convex Optimization Theory: slides, exercises, solutions

• Appendix B – Optimization Algorithms: slides, R code, exercises, solutions

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 Python: Primer on Python for Finance, Solvers in Python, portfolio library skfolio

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

Contact Information

Prof. Daniel P. Palomar: Website

palomar@ust.hk

GitHub: https://github.com/dppalomar

LinkedIn: https://www.linkedin.com/in/daniel-palomar-8373a1b7

YouTube: https://www.youtube.com/danielpalomar

X: https://x.com/danielppalomar

Google Scholar: https://scholar.google.com/danielpalomar