Synthetic Data Augmentation for Deep Reinforcement Learning in Financial Trading

Abstract

Despite the eye-catching advances in the area, deploying Deep Reinforcement Learning (DRL) in financial markets remains a challenging task. Model-based techniques often fall short due to epistemic uncertainty, whereas model-free approaches require large amount of data that is often unavailable. Motivated by the recent research on the generation of realistic synthetic financial data, we explore the possibility of using augmented synthetic datasets for training DRL agents without direct access to the real financial data. With our novel approach, termed synthetic data augmented reinforcement learning for trading (SDARL4T), we test whether the performance of DRL for financial trading can be enhanced, by attending to both profitability and generalization abilities. We show that DRL agents trained with SDARL4T make a profit which is comparable, and often much larger, than that obtained by the agents trained on real data, while guaranteeing similar robustness. These results support the adoption of our framework in real-world uses of DRL for trading.