Machine Trading : Deploying Computer Algorithms to Conquer the Markets

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.

Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools.

• Utilize the newer, easier algorithmic trading platforms
• Access markets previously unavailable to systematic traders
• Adopt new strategies for a variety of instruments
• Gain expert perspective into 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; 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.

ERNEST P. CHAN is the managing member of QTS Capital Management, LLC, a commodity pool operator and trading advisor since 2011. An alumnus of Morgan Stanley and Credit Suisse, he received his PhD in physics from Cornell University, and was a researcher in machine learning at IBM's T. J. Watson Research Center before joining the financial industry. He is the author of Quantitative Trading and Algorithmic Trading. Find out more about Ernie at www.epchan.com.