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A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm
Market making is a high-frequency trading problem for which solutions based on reinforcement learning (RL) are being explored increasingly. This paper presents an approach to market making using deep reinforcement learning, with the novelty that, rather than to set the bid and ask prices directly, t...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9767337/ https://www.ncbi.nlm.nih.gov/pubmed/36538547 http://dx.doi.org/10.1371/journal.pone.0277042 |