Domain-adapted Learning and Interpretability: DRL for Gas Trading

TL;DR

This paper demonstrates a practical Deep Reinforcement Learning approach for natural gas futures trading, achieving superior Sharpe Ratios and introducing an ensemble scheme that enhances stability, robustness, and reduces transaction costs.

Contribution

It presents a novel application of Deep RL in gas trading with an ensemble method that improves performance and interpretability over existing strategies.

Findings

Sharpe Ratio exceeds benchmark strategies

Ensemble learning improves model stability and robustness

Lower transaction costs due to reduced turnover

Abstract

Deep Reinforcement Learning (Deep RL) has been explored for a number of applications in finance and stock trading. In this paper, we present a practical implementation of Deep RL for trading natural gas futures contracts. The Sharpe Ratio obtained exceeds benchmarks given by trend following and mean reversion strategies as well as results reported in literature. Moreover, we propose a simple but effective ensemble learning scheme for trading, which significantly improves performance through enhanced model stability and robustness as well as lower turnover and hence lower transaction cost. We discuss the resulting Deep RL strategy in terms of model explainability, trading frequency and risk measures.