标题： 带有市场影响的交易执行强化学习 显示英文标题

标题： Reinforcement Learning for Trade Execution with Market Impact

摘要： 在本文中，我们引入了一种新颖的强化学习框架，用于限价单簿中的最优交易执行。我们将交易执行问题形式化为一个动态分配任务，其目标是最佳放置市价单和限价单以最大化预期收益。通过采用多元逻辑正态分布来建模随机分配，该框架实现了强化学习算法的有效训练。数值实验表明，在包含噪声交易者提交随机订单、战术交易者对订单簿不平衡做出反应以及战略交易者寻求获取或清算资产头寸的模拟限价单簿环境中，所提出的方法优于传统的基准策略。 显示英文摘要

摘要： 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 employing multivariate logistic-normal distributions to model random allocations, 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.