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Towards a fully RL-based Market Simulator

2021-10-13 16:14:19
Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, Sumitra Ganesh
arXiv_AI
   
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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.06829

PDF

https://arxiv.org/pdf/2110.06829.pdf

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