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

Understand

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.