
Machine Trading: Deploying Computer Algorithms to Conquer the Markets
Ernest P. Chan, PredictNow.ai
John Wiley & Sons, Inc., 2017
ISBN: 978-1-119-21960-6;
Language: English
Machine Trading is a practical guide to building an 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. Readers will 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 readers will learn to design their 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.
Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets readers up to speed and walks them through the process of developing their own proprietary trading operation using the latest tools. They'll also learn how to:
- Utilize newer, easier algorithmic trading platforms
- Access markets previously unavailable to systematic traders
- Adopt new strategies for a variety of instruments
- Understand the human side of trading
The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation, and decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions.
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A supplemental set of MATLAB code files is available for download on the author's site (password required).
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