Using Reinforcement Learning in the Algorithmic Trading Problem
As someone who has written about this previously [0], worked briefly in HFT before, and read dozens of papers on the subject, I can say with very high confidence that the results are not to be trusted. This paper, just like pretty much any academic paper on the subject, ends with a backtest on historical data, not a real system.<p>Not only is it (very!) easy to overfit backtests (especially with so little data they are using here), but backtests are nothing like the real world. In the real world there are HFT traders front-running you, latency, jitter, fees, hidden order types, slippage, and a lot of other complexities that don't fit into a short HN post. Whenever you see a paper ending with a backtest you can already assume it's BS.<p>It's similar to training a robot in an extremely simplified 2D simulation environment without physics or other interactions, and then claiming one has built a real robot. A mistake many people make is believing that trading is all about AI. But in reality, the model often matters less than infrastructure/latency/system/data issues.<p>In addition to that, people who are actually "good" at trading don't publish papers, they silently make money. Papers are typically published by academics or students who have never built anything profitable but would like to put a paper on their resume. I have yet to see a single good academic paper about trading.<p>[0] <a href="https://www.tradientblog.com/2019/11/lessons-learned-building-an-ml-trading-system-that-turned-5k-into-200k/" rel="nofollow">https://www.tradientblog.com/2019/11/lessons-learned-buildin...</a>