Reinforcement learning for trade execution with market and limit orders
提出一个强化学习框架，将交易执行问题建模为动态分配任务，通过多元逻辑斯蒂正态分布优化市价单和限价单的放置，在模拟环境中优于传统基准策略。
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.