People who want to become quant investors or traders often ask for advice on how to start. I began my learning journey ten years ago and now I have some opinions on the matter.
It is easy to put together a monster list of dozens of books but that kind of advice is counterproductive, more likely to intimidate than to help. A realistic reading list should have half a dozen entries but they should all be exceptional.
I prefer books written by practitioners over books by academics. Real-life experience shines through these books and they give you practical wisdom as well as mathematical tools.
I put together a list aimed at readers who are comfortable with basic undergraduate math. A course in calculus and a course in linear algebra should be enough to get through any of these books.
Quant Investing Basics
- Ernie Chan: Algorithmic Trading
Ernie’s first book, Quantitative Trading, was a great, gentle introduction to quant trading but it has aged by now. Its replacement, Algorithmic Trading, is a fantastic introduction to the techniques and mindset of a quant. It contains plenty of code and example strategies for starting on the quant journey.
- Rob Carver: Systematic Trading
Rob Carver was a portfolio manager running billions of dollars in fixed income quant strategies at Man Group in London. While he is a trend follower, his book teaches risk management and portfolio management techniques that will be useful to any investor, including discretionary traders (that is, non-quant traders).
Risk Management
- Aron Brown: Red Blooded Risk
Aaron Brown is a legend in the risk management industry. He was one of the early quants who entered finance in the 1980s and built the tools and approaches that we all use now. His book Red Blooded Risk is a revelation that completely changed how I think about investing. His main point is that risk is not something bad but rather it is a fuel that powers returns. Our goal is not to minimize risk but to optimize it: to build an engine that generates the highest returns for the risk that we’re taking.
- Andrew Ng: Introduction to Machine Learning (Coursera)
This course, originally offered at Stanford, is so good that it launched the whole MOOC online learning industry. I cannot think of a better introduction to machine learning.
- James, Witten, Tibshirani, Hastie: Introduction to Statistical Learning
If you’re going to get serious about machine learning, this is the book to set you up properly. It is amazing that in a fast moving field like machine learning, this book, written in 2014, has aged so well. The authors are statisticians who bring mathematical rigor to the field but they are great teachers as well.
Further Reading
My goal with this list is to be realistic. Going through these 5 books and 1 course will take months of work. There is no point in being distracted by more suggestions. I will have a follow-up post about more advanced readings at some point in the future.