Towards a fully RL-based Market Simulator

Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann and Sumitra Ganesh

Papers from arXiv.org

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.

Date: 2021-10, Revised 2021-11
New Economics Papers: this item is included in nep-cmp
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Published in ACM International Conference on AI in Finance, 2021

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2110.06829

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