| Autor: |
Ardon, Leo, Vadori, Nelson, Spooner, Thomas, Xu, Mengda, Vann, Jared, Ganesh, Sumitra |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
ACM International Conference on AI in Finance, 2021 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1145/3490354.3494372 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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