Domain-adapted Learning and Interpretability: DRL for Gas Trading

2023/01/19 by Yuanrong Wang, Yinsen Miao, Wang, Yuanrong +7
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Energy Load and Power Forecasting #FOS: Economics and business #Market Dynamics and Volatility #Stock Market Forecasting Methods #Trading and Market Microstructure (q-fin.TR)

paper · pdf · doi:10.48550/arxiv.2301.08359

openalex publication_date 2023/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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