[P] Introduction to Learning to Trade with Reinforcement Learning • r/MachineLearning

This is an interesting expository piece, and it seems to me that there are a lot of fundamental barriers that need to be addressed before RL can be successfully applied to trading, the most fundamental one appears to me to be the large number of competing agents in the environment. We're still some ways off success in multiplayer environments with a relatively small number of players and if we succeed there's no guarantee that the strategies may generalize to an environment with orders of magnitude more players operating across a spectrum of timescales. Given the size of the state space, the most likely path for (short-term) success could be a number of RL models each specialized to particular market conditions to be invoked by a human operator. AFAIK (happy to be corrected on this), the big prop shops eschew deep learning in favor of simple logistic-like models on FPGAs because of a) speed, b) (more fundamentally) the fact that the former tend to badly overfit to historical data (IIRC stock prices are martingale-ish? If someone with more experience can chime in, that would be great).