With existing Zorro functionality, particularly the normalization functions, you can implement a lot of what's in TSSB directly in Zorro. Using the R bridge, you can actually extend it even further. You could build yourself a complete TSSB toolkit using off-the-shelf Zorro and R tools.

TSSB (the book) is quite useful as it is one of the few publications that focuses on the practical aspects of machine learning in algo trading. I would suggest that it is fast becoming dated however due to the lack of coverage of deep learning.

An interesting read, unrelated to R though. I wonder where this leads:

7,500 Faceless Coders Paid in Bitcoin Built a Hedge Fund’s Brain
https://www.wired.com/2016/12/7500-facel...?mbid=social_fb

Probably the future way to avoid overfitting using neural networks for time series:
https://arxiv.org/pdf/1511.06434v2.pdf

https://arxiv.org/pdf/1406.2661v1.pdf

Someone knows about a library for R?

Maybe MXNet package (haven't tried it but the key words are present in its tutorial:))

Stumbled upon this free course today:

Machine Learning for Trading
https://www.udacity.com/course/machine-learning-for-trading--ud501

It currently works only for phyton tho and currently used for image clasification.

Nanitek: And do you happen to have any papers on the use of aforementioned technique in trading? I haven't found much on the topic.

No I do not, I posted in case someone finds out. There is something to transform time series to pass them into a convolutional network and then that net structure could work, since mxnet exist for R as well, maybe that net struct work as well over there

Mike Halls-Moore finally finished his last book, and it IMHO worth your attention. One of the most clear, clever and useful books I ever read about quantitive trading:
https://www.quantstart.com/advanced-algorithmic-trading-ebook

The book looks good. Any review? Is the book mainly on Python and little on R. It will be good if the book has more examples of R so that users of Zorro can benefit.