Is there any real-life cases of successful application of reinforcement learning in trading / asset management? about btgym HOT 4 OPEN

@mysl , there are many match and CS experts and even more die-hard traders and asset-managers. The mix of both expertise in one head or team is still rare. One needed courage and independent vision to propose model-free policy search while living in the era of linear regression-based econometric models.

from algorithm's aspect?

• differentiable utility function is number one, proper feature extraction is number two IMHO.

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From 'Multi Objective Reward Discussion' #110:

I have been recently thinking on how to incorporate risk adjusted returns, like sharpe ratio, as a way to form a richer and more complex reward function.

Differential risk adjusted measurement is just a brilliant concept!
I went over the derivation of 'The Differential Sharpe Ratio' and 'Differential Downside Deviation Ratio' and they are both quite interesting, although I probably will need to read them a couple more times to really get them.

In principle this derivation can be applied to the 'N'th Lower Partial Moment' and get a whole family of those differential risk adjusted measurement.

@Kismuz, you did a real detective job for this post :)
It really seems like they were on the right path since 20+ years ago

While looking for related articles in google, I came across this nice survey on the topic.
Reinforcement Learning in Financial Markets

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@Kismuz, differential risk adjusted are based on having some moving average statistic of the previous rewards. So it means that for the first stage (moving average initialization period), we don't get any risk adjusted reward feedback to learn from.

Is it a problem from RL stand point? or is it ok to have a very sparse reward at the beginning and then dense rewards from that point onward? (especially in light of that the sparse reward are due to an arbitrary number of actions taken and not environment dynamics)

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@Kismuz Long time no see your update :-) thanks for the information. If J E Moody & Company LLC succeeded 15+ years ago with applying RL in trading in practice and turned it into business as a small team. Without nowadays computing power, software stack and recent advances in ML/DL field, which part do you think is most likely the key to their success from algorithm's aspect? thanks

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