Optimal Trading in Automated Market Makers with Deep Learning
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917461340717056 |
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| author | Jaimungal, Sebastian Saporito, Yuri F. Souza, Max O. Thamsten, Yuri |
| author_facet | Jaimungal, Sebastian Saporito, Yuri F. Souza, Max O. Thamsten, Yuri |
| contents | This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution problem where the agent hides their orders by controlling the rate at which they trade. We do so without approximating the market dynamics. The resulting dynamic programming equation is not analytically tractable, therefore, we employ the deep Galerkin method to solve it. Finally, we conduct numerical experiments and illustrate that the optimal strategy is not prone to price slippage and outperforms naïve strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_02180 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimal Trading in Automated Market Makers with Deep Learning Jaimungal, Sebastian Saporito, Yuri F. Souza, Max O. Thamsten, Yuri Trading and Market Microstructure This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution problem where the agent hides their orders by controlling the rate at which they trade. We do so without approximating the market dynamics. The resulting dynamic programming equation is not analytically tractable, therefore, we employ the deep Galerkin method to solve it. Finally, we conduct numerical experiments and illustrate that the optimal strategy is not prone to price slippage and outperforms naïve strategies. |
| title | Optimal Trading in Automated Market Makers with Deep Learning |
| topic | Trading and Market Microstructure |
| url | https://arxiv.org/abs/2304.02180 |