The problem with these types of systems are it's difficult to do backtesting on a single continuous data stream... I write about that here:

https://blog.projectpiglet.com/2018/01/perils-of-backtesting...

I recommend doing something similar to the original post. Neural networks tend to produce valid looking output for a stock price easily, a random walk does too. You have to find correlations, causations, and then review the results carefully.

For reference, I wrote my own financial advisor (not directly utilizing deep learning, that functions relatively well):

http://projectpiglet.com/

It works relatively well when checking the "causality" which also has it's limits: https://blog.projectpiglet.com/2018/01/causality-in-cryptoma...

EDIT: Added some evidence:

* 2016 stocks: https://imgur.com/a/j8YWR

* Early 2018 crypto: https://blog.projectpiglet.com/2018/01/30-weekly-returns-usi...

* Early 2018 stocks: https://twitter.com/AustinGWalters/status/976347632439209985

* All 2018 on Robinhood only: https://imgur.com/a/2CxEFqI

* Also (I am lettergram on HN), created: https://hnprofile.com and https://redditprofile.com