TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution

TL;DR

TradeR introduces a hierarchical reinforcement learning framework tailored for practical trade execution, effectively handling abrupt market dynamics and minimizing surprises during high-frequency trading in volatile conditions.

Contribution

The paper presents a novel hierarchical RL approach for trade execution that addresses real-world challenges like abrupt price changes and surprise minimization, demonstrating robustness in volatile markets.

Findings

Robustness to abrupt market fluctuations

Minimizes catastrophic trading losses

Maintains profitability during volatile periods

Abstract

Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.