Towards a fully RL-based Market Simulator

• URL: http://arxiv.org/abs/2110.06829v1
• Date: Wed, 13 Oct 2021 16:14:19 GMT
• Title: Towards a fully RL-based Market Simulator
• Authors: Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, Sumitra Ganesh
• Abstract summary: We present a new financial framework where two families of RL-based agents learn simultaneously to satisfy their objective. This is a step towards a fully RL-based market simulator replicating complex market conditions.
• Score: 4.648677931378919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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