I’ve been repeatedly asked this question and my answer always was something like ‘I have no evidence’. Unfortunately, in this domain every person who able to say something valuable instantly turns covert, vague and mysterious when it comes to real application.

But after all I think we can now track at least one case of successful application of RL in asset management. What is incredible about it is that we talk about two decades-old research work.

I consider myself decent information retriever so it is absolute shame I’ve missed this thread until now. If I wouldn't - my work with BTGym would pace times faster. Though I have independently repeated some findings (like time-series preprocessing via differencing stack of moving averages or recurrent policies), other key features didn’t came so easily. I mainly mean performance functions like Differential Sharpe or Downside Deviation Ratio found in these papers (I’m absolutely sure it is my ignorance of domain specific performance functions and attempts to use only linear combinations of returns as source of reward is main cause of suboptimal performance of algorithms included in BTGym).

So here is a stack of accessible works of John Moody, Matthew Saffell et all:
https://www.researchgate.net/scientific-contributions/31497186_Matthew_Saffell
https://www.researchgate.net/scientific-contributions/10597646_John_Moody

Going through full stack form 1996 through 2004 one can easily see evolution of ideas. Some key papers to read are:
Learning to Trade via Direct Reinforcement https://pdfs.semanticscholar.org/1a49/99c918c6206cd9804c48f7dce1bac6ec5b4a.pdf

PERFORMANCE FUNCTIONS AND REINFORCEMENT
LEARNING FOR TRADING SYSTEMS AND PORTFOLIOS http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.87.8437&rep=rep1&type=pdf

Reinforcement Learning for Trading https://pdfs.semanticscholar.org/93b8/17deef9dd5afc66ccf43174a07ddaa49854f.pdf

Think again, it is early bimillenary, no TF, PPO and all that deep stuff we used to have at hand until a decade passes.

Than in 2004 line breaks. The last public document available is Saffell’s 2005 thesis:

Knowledge discovery for time series
https://www.semanticscholar.org/paper/Knowledge-discovery-for-time-series-Moody-Saffell/002165064501911ca06679ba762bd7ffc00bf44d
… and some Paris conference ppt presentation.

Given industry realities, there are some reasons to interrupt such a line:

• no practical result can be made of these research, so everything just stops when grants are exhausted; but is not the case here because at least one of the researchers should surface later with some publications on fresh supported topic;
• there are good practical perspectives, group get hired by an institution and signs confidentiality papers; nothing to find here cause any practical applications and reports would be locked forever inside some corporate intarnet;
• perspectives are so promising and novel one can start it’s own business. Indeed, the company was founded in 2011 as ‘J E Moody & Company LLC’ in Portland, OR’: http://www.jemoody.com/

I think these guys are hilarious. Small team, no fuss, no conference talks, no private investors, no fancy landing pages but 2018 ‘HFM US Performance Award^ for Best Quantitative Strategy under $1B’.

As for me, everything just says: “ If you want to evidence financial RL going practical - look no further”.