By: Dr. Earnest P. Chan
First and foremost, it is recommended for readers to have a basic understanding of how the stock market mechanisms function, moderate knowledge of linear algebra, and at least rudimentary-level experience with MATLAB or other programming languages commonly used for trading algorithms (such as Python) in order to gain the most out of this book.
In this book, Dr. Chan develops further into concepts he previously wrote about in his first two books, Quantitative Trading¹ and Algorithmic Trading², but he also discusses new topics.
As Amazon's description of the book summarizes:
"Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level."
Overview of Machine Trading
In the first chapter of this book, Chan analyzes sources of data to use for testing historical success of different trading strategies. The first chapter includes a quasi- benefit:cost analysis of these data sources (such as websites, aptitude of API, and of course-- cost). Additionally, readers learn about the different methods of algorithm evaluation.
A significant portion of the book relates to using the MATLAB programming language for analysis purposes. Dr. Chan offers an entire chapter on artificial intelligence, and he does not assume the reader has any prior experience with AI. This makes the book a very good starting place for quantitative traders looking to learn more about AI capabilities for trading in capital markets.
In the latter parts of the book, Dr. Chan spends a chapter on Bitcoin and discusses potential opportunities for trading the cryptocurrency since Bitcoin exchanges use open order books (at least for now). Furthermore, another chapter delves into current high-frequency trading strategies and techniques. The end of the book entails specific mechanisms of the stock market like how order placement works-- similar material to what is found in Algorithmic Trading & DMA by Barry Johnson.
Altogether, this third book by Dr. Chan is the perfect read for tech-savvy traders who want to learn more about the integral function computers have in our financial markets and how to use them to develop winning trade algorithms.
Purchase Machine Trading on Amazon
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