Algorithmic Trading: Winning Strategies and Their Rationale (Wiley Trading)

This is certainly a worthwhile book. Importantly, it is rare in its integration of insights into market dynamics with a discussion of their most appropriate accompanying statistical tools. As the first reviewer accurately described, "in effect it is an ideal primer for the quant newbie". To that end, it is important to zero in on "primer" and "newbie". Many experienced investors and traders understand the pitfalls and benefits of algorithmic trading. Unfortunately, many newbie quants seem to have little idea of the biases and unrealistic results in their approaches. This makes it difficult for experienced portfolio managers to communicate their insights about markets and securities and relate it to the mathematical and statistical framework of their quant teams. To that end, I'd expand the first reviewer's definition and say that this is a very valuable primer for highly experienced market professionals who are looking to expand their quantitative framework. Algorithmic Trading: Winning Strategies and Their Rationale makes this process easier through its unique combination of broad generalizations and specific examples. As such, it can be a point of discussion and specific statistical work on algorithmic strategies. Chen has done a great job of building a bridge for communication, testing, and discussion of trading strategies. This book provides the foundation blocks for approaching and understanding mean reversion strategies in a quantitative framework.

So nearly four and half years after writing the first review of Dr Chan's first book I am back again writing the first review for his second. Things to note: 1. All the examples in the book are again in MATLAB, so if you don't have MATLAB you will be at a disadvantage. 2. Whilst the title of the book includes the phrase Algorithmic Trading. It, like the first book, doesn't actually show you how to connect a MATLAB model or system to the market so it can run as an algorithmic trading platform. This was a criticism of the first book. However, if you Google "MATLAB as an Automated Execution System" you'll find a paper that Dr Chan wrote that shows you how to connect MATLAB to Interactive Brokers via a third party MATLAB interface. 3. Whilst the title doesn't use the word quant, be assured the models are again from the quant school. Readers from the TA school of school of oscillators, Gann, MACD etc are not catered for. Now the book itself: In the introduction Dr Chan makes it clear the book contains prototype strategies. The book isn't a collection of "strategy recipes" (his term) rather it's about why some strategies should work and how we can look to test and refine them. For each presented strategy we are given a model using MATLAB code. The code is only a snippet; you need to go to Dr Chan's website for the full code. Many of the models will need further work to accommodate the reader's circumstances, but Dr Chan is clear that he isn't presenting complete models. The book is essentially about why certain approaches to the market should work in theory given the "maths" and what we know about market operations. Many of the discussed strategies will be familiar to readers of Dr Chan's blog and his first book. The main division in the book is between mean reversion and momentum strategies, with mean reversion getting the greatest attention. Dr Chan highlights the challenges facing traders of mean reversion, particularly those focusing on pure stock pairs, his preference now is more towards ETFs. As you come to expect from Dr Chan his theories are well supported by maths and any reader will get a good primer on stationarity, cointegration, dickey fuller test and the Hurst Exponent. My Summary: I devoured the first book and spent many hours coding and testing the ideas that were presented. This time around I felt there isn't much new content for a reader or practitioner with a reasonable interest in pair trading, basket trading or a quant approach to momentum trading. If you haven't read the first book, then this is a better book. It has been updated to reflect the market conditions of the last few years, plus there are greater descriptions of the theory behind why some of these quant models work and ways in which we should look to improve them. So in effect it is an ideal primer for the quant newbie. As a standalone book and with the knowledge the ideal reader is quant focused then the book is a four. Readers who already have the first book and maintain an interest in quant will probably feel a little short changed this time around.

As a retail participant dealing in quantitative approaches to trading and investing in futures, options and stocks, I found Dr. Chan's book very well done, pragmatic and useful. The book describes methods to locate short to long term trading strategies and to systematically make money from them over time. The author carefully describes not just the mechanics and details of several algorithmic strategies, but also their enabling market factors. Popular technical indicators are avoided in favor of a more rigorous quantitative analysis. This is not a get-rich-quick-book. Dr. Chan rightly points out that even with his strategies' trading rules, program code and backtesting discipline, trading these strategies on a daily basis is a lot tougher than people expect. Without additional homework, you could end up depleting your capital accounts. Why would he tell you all of this in view of the secrecy and mystique that quantitative traders typically exhibit? He does not describe details of extremely high frequency trading strategies or strategies with low capacity that would suffer or cease to be profitable if more capital comes into the same game. It's a great sequel to his original book, Quantitative Trading. It amplifies and expands on his earlier strategies -- and that helps you make up your own mind about their usefulness.

Great book. Mean reversion and momentum strategies clearly explained.

This is the first trading book I have read which both tells truths I learned from harsh and laborious experience, and also is teaching me a huge amount of valuable information I did not know even existed. This kind of credibility, from personal experience of "yeah why didn't others tell me this (which I learned the hard way) before?" combined with so much and so very well explained know-how to learn from reading this book is unprecedented, IMHO. I get the impression that reading this book is a very rare opportunity to learn (mostly, if not all) TRUTH without having to laboriously try to separate wheat from chaff when it comes to learning what has been really happening in the trading world. Now, if only we could all get access to MATLAB and the appropriate required toolboxes for a less prohibitive price!

Excellent and detailed description of different strategies for different markets. At times, the author mentions where his actual tradings have failed, which might be of great help to new comers. However, to appreciate all these in depth, you may have to know linear algebra, statistics and probability. Especially, knowledge on basic time series analysis including the Brownian motion will be helpful, even though it's not absolutely required. On the programming side, it's pity that only MATLAB codes are provided by the author. In fact, most of them can be easily converted to R, one of the open source languages.

A comprehensive guide to professional grade algo strategies. The book is concise and to the point. The author provides matlab code which I did not test. While the book describes statistical tools in good detail, in my view experienced systems developers get more value from it than those who are just starting out. I recommend the book to both new traders and pros. New traders can use the book as a good starting point in their research. Pros can use the tools described in the book to enhance their existing strategies.

This is a great book, targeted to people with an intermediate level of understanding of programming. It covers topics ranging from back testing pitfalls to laying out detailed mean reversion and momentum strategies. The book includes code samples for each strategy introduced. Some readers have complained that the examples use Matlab (which is quite pricey), but as of the time of this writing (April 2019), the author has also included python samples on his website!