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- Amazon Sales Rank: #58882 in Books
- Published on: 2017-02-06
- Original language: English
- Dimensions: 9.10" h x 1.20" w x 6.00" l, 1.47 pounds
- Binding: Hardcover
- 264 pages
From the Inside Flap
Following up on his widely popular algorithmic trading guides, Quantitative Trading and Algorithmic Trading, this third installment is written for quant traders and investors ready for more advanced examinations and techniques. Machine Trading is your accessible companion for the state-of-the-art of algo-trading in today's complex markets.
Don't worry if you lack trading and finance experience—if you've worked in a quantitative field, such as computer science, engineering, or physics, this step-by-step resource makes the transition into algorithmic trading seamless. It starts out with a comprehensive look at the latest backtesting and trading platforms, the best- rated and most cost-effective vendors' data, and the easiest way to optimize allocations in different assets and strategies. Acquire a firm grasp on options and volatility strategies; factor models, and why they can be useful to short-term traders; and the intricacies of intraday and high-frequency trading, including market microstructure, dark pools, order flow, and backtesting intraday strategies with tick data. There are no canned solutions inside—each prototype trading strategy provides a rock solid foundation for you to customize. Hone your skillset on topics such as:
- Using factor models for long-term returns and short-term trades,including using option prices as factors
- Real-world trading with time series techniques, including ARIMA, VAR, and State Space Models with hidden variables
- Cutting-edge techniques to reduce outfitting in artificial intelligence and machine learning strategies
Every chapter includes hands-on exercises walking you through the critical modifications to make on your own to gain control of the strategies and discover their potential. From stocks to futures and options, foreign exchange, and bitcoins, Machine Trading is your one-stop training ground for finding algo-trading solutions.
From the Back Cover
Praise for Machine Trading
"It is easy to make simple ideas complex. It is far more difficult to make complex ideas seem simple. In this book, Ernie has done exactly that. I cannot think of any trader who would not benefit from reading Machine Trading."—Euan Sinclair, Partner, Talton Capital Management; author, Volatility Trading
"As with his first two books, Ernie delves into the practical matters of automated trading in Machine Trading. He carefully explains everything from where to find quality data, to which platforms to consider, to timely and topical strategies. In the years since his last book, automation of the investment industry has accelerated, due in large part to the recent Cambrian Explosion in financial technology (FinTech). Ernie has long been teaching the hard-to-find methods of automated trading; now he is also a guide to the dizzying array of new companies, technologies, and services for automated trading. Machine Trading takes you deeper into the field with new concepts, while still delivering Ernie's signature explanation of the concrete steps of pursuing your own passion for automated trading."—John Fawcett, CEO and Founder, Quantopian
"Dr. Chan has written another accessible and information-packed book for the quant-minded trader. The book starts with a clear discussion of factor models, advanced time series analysis, and Kalman filters, all of which lead into a detailed description of machine learning and artificial intelligence techniques applied to volatility trading, market microstructure, and even Bitcoins. His focus on finance in a MATLAB context is refreshing and opens the algorithmic trading domain to a whole generation of engineers and quantitative practitioners not familiar with finance but involved in numerate fields like self-driving cars and proteomics. I highly recommend it."—Dr. Taha Jaffer, President, OXOBOXO Inc.; formerly Principal at The Carlyle Group
About the Author
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.
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