Reinforcement Learning for Trade Execution with Market and Limit Orders
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908789340372992 |
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| author | Cheridito, Patrick Weiss, Moritz |
| author_facet | Cheridito, Patrick Weiss, Moritz |
| contents | In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By modeling market and limit order allocations with multivariate logistic-normal distributions, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed method outperforms traditional benchmark strategies in simulated limit order book environments featuring noise traders submitting random orders, tactical traders responding to order book imbalances, and a strategic trader seeking to acquire or liquidate an asset position. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06345 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Trade Execution with Market and Limit Orders Cheridito, Patrick Weiss, Moritz Trading and Market Microstructure Computational Finance Statistical Finance In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By modeling market and limit order allocations with multivariate logistic-normal distributions, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed method outperforms traditional benchmark strategies in simulated limit order book environments featuring noise traders submitting random orders, tactical traders responding to order book imbalances, and a strategic trader seeking to acquire or liquidate an asset position. |
| title | Reinforcement Learning for Trade Execution with Market and Limit Orders |
| topic | Trading and Market Microstructure Computational Finance Statistical Finance |
| url | https://arxiv.org/abs/2507.06345 |