The author doesn't provide sufficient details to implement a system similar to what he is using. Very high level review of a very particular implementation. You can only understand the subject if you already know it. The real benefit of reading this book is to find out where an average financial enterprise is in terms of adopting the AI.
The book that I am currently reading is the best to learn about machine learning in the financial industry. However in order to understand the book, you need at least an intermediate level in machine learning, computational skills, and knowledge in time series. If you read the whole book, you will find that the author focuses on the following topics:
How to deal with raw financial data
The importance of backtesting
As long as you have fundamental knowledge in data science, you should know the importance of the three points stated above. Fortunately, this books gives a great guide that shows us how to solve these problems. Although the solutions provided in the books can be disproved, it does not matter as you cannot find another author that is willing to share his ideas to the industry.
This book is an important milestone in the field of Machine Learning and Financial Engineering. Many books have been written about both subjects but none were actually bridging all the various aspect that are fundamental to any good ML + financial solutions. I really enjoyed and learned many things reading the sections on backtesting and feature engineering.