Optimal Execution Using Reinforcement Learning

• URL: http://arxiv.org/abs/2306.17178v1
• Date: Mon, 19 Jun 2023 07:09:59 GMT
• Title: Optimal Execution Using Reinforcement Learning
• Authors: Cong Zheng and Jiafa He and Can Yang
• Abstract summary: This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time.
• Score: 6.905391624417593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.

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