Model-based Reinforcement Learning for Predictions and Control for Limit
  Order Books
  We build a profitable electronic trading agent with Reinforcement Learning
that places buy and sell orders in the stock market. An environment model is
built only with historical observational data, and the RL agent learns the
trading policy by interacting with the environment model instead of with the
real-market to minimize the risk and potential monetary loss. Trained in
unsupervised and self-supervised fashion, our environment model learned a
temporal and causal representation of the market in latent space through deep
neural networks. We demonstrate that the trading policy trained entirely within
the environment model can be transferred back into the real market and maintain
its profitability. We believe that this environment model can serve as a robust
simulator that predicts market movement as well as trade impact for further
studies.
