Proof of RL viability for trading with data leakage

Hi guys,

Your FinRL project has been very helpful - I have been using the StockTradingEnv to make sure I do not mess up my environment.

However, I am encountering very low performance with RL algorithms. In order to test if the RL models are working properly I have created features that leak data about the future returns in the next 1,2,3,5 days. In theory, this should make the task very easy - if future returns are low, sell. However, the model is not able to learn any strategy other than buy and hold.

To replicate:
* Take the notebook https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/1-Introduction/Stock_NeurIPS2018_SB3.ipynb
* Set ticker list to single stock
* Disable tech indicators, vix, turbulence and add indicators of type close.pct_change(-1), close.pct_change(-5)
* Run a2c model

Do you know why the standard RL algorithm is failing even when given future information? Could you show a notebook where it is able to outperform a buy and hold strategy on a stock, while using information from the future?

Thank you,
Evgeny.

Contact: gluzman64@gmail.com