Machine Trading: Deploying Computer Algorithms to Conquer the Markets (Wiley Trading)

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.

This book is 3rd in a series by author. I like the 1st book for its simplicity and easy to follow. 2nd is a little more advanced and formal but still easy to follow. This one is most advanced, and a little difficult to follow. If you liked previous books, this one is also worth a reading as a guide into advanced techniques.

High programming familiarity would be a plus, great and advanced concepts discussed in the book which will open your mind to other subjects such as trading AI and learning more complex programming tasks

No good if you barely know what you're doing. Great guidelines, but hard to follow if you've got only a bit of experience, great idea generation if you do know what you're doing, but light on critical details and rigour, and finally next to useless if you're advanced/practitioner, because you wouldn't be worth your salt if you didn't know these things already. That said, all in all a good read if it isn't your only one.

Ernest Chan writes with clarity and an experts perspective on algorithmic trading tools and techniques.

this is a good book if you do your homework. People here giving bad reviews think the author will give his full blown strategies for free - nobody does that. As with any book in this subject, it adds up to your knowledge but you still need to do the hard work. It does lack some depth though and I wish he could give some more information on cryptocurrencies - this chapter is pretty vague. Overall a good book.

Great entry level book for quantitative trading. Targets primarily individual (retail) hobby-traders who have some basic understanding of statistics and programming. What I particularly like is that this material doesn't give ready made strategies, but teaches a methodology how to develop one.

All the books so far from algorithmic trading to this book on machine trading is a pure junk filled with vague ideas.

Poor print-on-demand quality, not worth 50€.

I liked the book's tone. While a bit folksy and really practical (some of which is now mildly out of date), it is an honest and effective book on the journey into machine trading. Very helpful and insightful.

È un buon libro, che esprime concetti non nuovi per molti ma, riuniti insieme, danno qualcosa in piu. Libro, a mio parere, non adatto a principianti o a persone con poca familiarita con la matematica

Machine Trading is largely a discussion of possible trading strategies and the algorithms that might be used as a foundation for an automated system. The book is more about researching algorithms than about writing production code. The code fragments are mainly in MatLab; Python and R are also mentioned in the opening part, but not pursued in the main examples. More complete code samples are provided online, so you don’t have to plough through loads of code in this book, it’s mainly about the underlying algorithms. The first part of Machine Trading covers where to get data from and what software to use. A number of different approaches are then explored in the book from the more basic techniques of linear regression and neural networks, to more advanced financial concepts like using options and advanced programming techniques like high-frequency. There’s a huge amount of material covered in the book that would take many months to complete the algorithm research for and then go on to use in anger, but this is a good starting point for anyone contemplating working in this area.

I’m a web developer and a geeky one at that, so one of the things I am currently looking at is machine learning with Python and R. There is a lot of introductory stuff to get you going, but as soon as you want to move to something specialised the books dry out on information. Lots on chat-bots and the actual mechanics of building a neural net or image recognition, but far less when you want to drill down into a specific domain that would suggest a career. This book is a good one to get you started in machine trading because it presents specific rather than general theory. It also gets a massive bonus point from me for using the right terminology; there is no such thing as ‘big data’ because it’s a buzzword for machine learning (machine learning works on unstructured data to find patterns, and that data is usually large, but the correct terms for this process in the tech-literature is actually ‘machine learning’ not ‘big data’). The downside is of course that this book is specific and therefore assumes a body of expertise; a university level maths and programming education. This book only provides the modelling and predictive side of things, so you would almost certainly need to know another technology to actually build your application, typically one or more web based single page application (SPA). Also worth noting that the author has a fair pedigree (you can look him up at his website, epchan com), and this is his third book on the subject, so if author track record is a buying factor for you, this will tick that box. Overall a good book on a specialised subject; be prepared for some serious study to get through the book though!