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arXiv · 2110.06829

Towards a fully RL-based Market Simulator

Leo Ardon·Nelson Vadori·Thomas Spooner·Mengda Xu·Jared Vann·Sumitra Ganesh
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Abstract

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.

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Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, Sumitra Ganesh. 2021-10-13. Towards a fully RL-based Market Simulator. https://doi.org/10.1145/3490354.3494372

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