Reinforcement Learning for Trade Execution with Market and Limit Orders

TL;DR

This paper presents a reinforcement learning framework for optimizing trade execution by strategically placing market and limit orders, outperforming traditional methods in simulated noisy and strategic trading environments.

Contribution

It introduces a novel RL approach using multivariate logistic-normal distributions for dynamic trade execution in limit order books, enhancing decision-making efficiency.

Findings

Outperforms benchmark strategies in simulated environments

Effective in noisy and strategic trading scenarios

Demonstrates improved revenue maximization

Abstract

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.