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We build a profitable electronic trading agent with Reinforcement Learningthat places buy and sell orders in the stock market. An environment model isbuilt only with historical observational data, and the RL agent learns thetrading policy by interacting with the environment model instead of with thereal-market to minimize the risk and potential monetary loss. Trained inunsupervised and self-supervised fashion, our environment model learned atemporal and causal representation of the market in latent space through deepneural networks. We demonstrate that the trading policy trained entirely withinthe environment model can be transferred back into the real market and maintainits profitability. We believe that this environment model can serve as a robustsimulator that predicts market movement as well as trade impact for furtherstudies.

Model-based Reinforcement Learning for Predictions and Control for Limit Order Books