Reinforcement Learning for Trade Execution with Market and Limit Orders

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 th...