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arxiv: 2110.06829 · v2 · pith:FTJLG3AP · submitted 2021-10-13 · cs.MA · cs.AI· cs.LG· q-fin.TR

Towards a fully RL-based Market Simulator

Leo Ardon , Nelson Vadori , Thomas Spooner , Mengda Xu , Jared Vann , Sumitra Ganesh This is my paper

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classification cs.MA cs.AIcs.LGq-fin.TR
keywords marketrl-basedfinancialfullyliquiditysimulatortowardsable
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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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