Many very smart people have tried and failed. State of the art remains very basic supervised models with hand engineered features. In the markets, data is permanently scarce, so these methods don't work well. In the RL problems that DeepMind is solving, data is literally unlimited, and that's the problem space that these methods have been designed for.
It's not so clear to me how you would train a reinforcement learning agent for the stock market. You have historic data for prices etc. But that's more of a supervised learning thing. You could set it loose on one of those realtime market simulators, but the agents actions wouldn't have any impact on the simulation right?
There's two problems in markets, price prediction and execution (ie what to do with your prediction). The former is a supervised learning problem but the latter is an action space problem ie an RL problem. Although nobody in industry has gotten any RL methods too work, they overfit to the incredibly small data sets.
hnracer|5 years ago
patagurbon|5 years ago
hnracer|5 years ago