Towards a fully RL-based Market Simulator
This work addresses the need for realistic market simulators in finance, though it appears incremental as it builds on existing RL methods for specific market roles.
The paper tackles the problem of simulating financial markets by developing a framework where RL-based agents for liquidity providers and takers learn simultaneously to meet their objectives, resulting in a shared policy that generalizes across a wide range of behaviors.
We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective. Thanks to a parametrized reward formulation and the use of Deep RL, each group learns a shared policy able to generalize and interpolate over a wide range of behaviors. This is a step towards a fully RL-based market simulator replicating complex market conditions particularly suited to study the dynamics of the financial market under various scenarios.