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Dive into algo trading with step-by-step tutorials and expert insight
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.
- Sales Rank: #17162 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.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Machine Trading Offers Practical And Informative Trading Advice
By William P Ross
Machine Trading had a ton of useful information for me. The first chapter looked at data sources for back-testing trading strategies. There was information about which are the best sites to get data from, which have good APIs, and the costs of these services. It was easy to see that the author had worked with lots of different types of data and vendors.
The next few chapters look at analysis using MATLAB as the programming language. The code snippets are well described and fairly easy to understand or translate to another language.
Another chapter which I enjoyed was the one on Artificial Intelligence. Actual techniques were shown in a simple manner and the techniques progressed from simple to more complicated. A good point was made how you want to try the simplest machine learning techniques first, before trying more advanced one. It was shown how neural networks generally do not perform that well on financial data. There was an example given showing that a neural network with only layer performed better than networks with too many layers.
Later there is a detailed section about how orders work in the stock market, and how closing prices are often "consolidated". The consolidated closing (and opening) prices can cause trouble for back-testing strategies. Explanations were clear about potential pitfalls of using different types of market or limit orders.
Another surprise was a chapter on bitcoin which had possible trading strategies on the crypto-currency. One of the advantages of bitcoin for analysis is that the order book is open and each trade specifies whether it was a buy or sell.
The book wraps up with a discussion about maintaining a career in trading. I was impressed with the content, the explanations, and details that went into this book.
5 of 6 people found the following review helpful.
Excellent Presentation of a Complex Topic
By JoeP
Chan’s Machine Trading book was my introduction to algorithmic trading. Chan is clearly skilled in presenting complex ideas in accessible language. Machine trading is a difficult topic to present because of all the interwoven disciplines. This is a guide to theory and practical implementation.
I would recommend having some basic knowledge of three areas: linear algebra, a scripting language (MATLAB, Python), and how the stock market works. Text books are written on all of those topics and Chan did an excellent job of staying on point without going on tangents to explain underlying principles, but still using principles that are simple enough to understand. I found the MATLAB Machine Learning e-book a great complement to the Artificial Intelligence chapter for understanding the goal of each algorithm. (Found on the mathworks website.)
He does not, however, assume knowledge of algorithmic trading, so if you are just starting out, this is a great resource. The first chapter lays out where to find data, how to test and verify algorithms, and different metrics for evaluation. The rest of the book is well organized and advances in complexity from time series analysis to AI technology to the latest high frequency (< 1 ms) methods. The book is rich with examples of how to implement each method and Chan even provides a link to the MATLAB code for readers who would like to see more.
Machine Trading cites Chan’s first two books frequently, but this can be read on its own. That said, this was so good I immediately bought his other two books!
Like I said, this was my first book on algorithmic trading, and I think it was exceptional because of its clarity, but also something I will be returning to frequently for examples and more advanced ideas. Highly recommend for anyone interested in the topic.
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