A simple learning agent interacting with an agent-based market model: Julia code

<p>We consider the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event driven agent-based financial market model. Trading takes place asynchronously through a matching engine in event time. The optimal execution agent is considered at different levels of initial order-sizes and differently sized state spaces. The resulting impact on the agent-based model and market are considered using a calibration approach that explores changes in the empirical stylised facts and price impact curves. Convergence, volume trajectory and action trace plots are used to visualise the learning dynamics. </p> <p>Please follow the README.md on the GitHub page for instructions on how to run the code. For example, the Calibrated-ABM branch contains the functionality to perform the simulation, sensitivity analysis, and calibration of the event-time ABM, while the RL-ABM branch extends the Calibrated-ABM branch and allows you to train an RL agent inside an event-based ABM.</p> <p>Thet dataset used in this project can be found <a href="https://doi.org/10.25375/uct.13187591.v1" target="_blank">here</a>.</p> <p><br></p>