Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading

TL;DR

This paper introduces a reinforcement learning algorithm for algorithmic trading that improves generalization across different market segments by leveraging data cross-segmentation, especially effective in less liquid markets.

Contribution

The paper proposes a novel RL-based trading approach that incorporates data cross-segmentation to enhance generalization in diverse and illiquid financial markets.

Findings

Effective in thinly traded markets

Outperforms traditional supervised methods

Demonstrates robustness over 20+ years of data

Abstract

The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.