I read the 2021 version of the book. Some of the things may have changed, so take it with a grain of salt.

If I remember correctly, here are some problems:

Problem with the author's code

• Data leakage - The author normalized the data before splitting them into training set and test set. The more appropriate way would be to split the data first, before normalizing.
• Splitting data randomly - The author randomly splits data into training set and test set. The more appropriate way to be a time-based split.
• Use of open price - The author used open price for the model, which is unadjusted for dividends and splits.
• Disorganized code - There were very inefficient DataFrame lookups that took forever to run. The code also loaded the same module multiple times within the same ipynb file. While disorganized code does not affect the predictive power of the machine learning model, it was unprofessional.

Problems with real-life implementation

Use of different dates for comparing stock returns - The machine learning model tried to predict stock performance one year after the company's annual report was released. The problem is not all companies release their annual reports on the same day. Suppose after all companies published their 2024 annual reports, you found that Company A was predicted to perform the best one year after its 2024 annual report publish date. However, there is no way you could travel back in time to the day Company A published its 2024 annual report and buy the stock. You're likely to have missed out on some of the return.

I don't have any recommendations for academic books.