Nonlinear relationships in soybean commodities Pairs trading-test by deep reinforcement learning

Abstract

The pairs trading strategy involves selecting two highly correlated securities to profit from mean reversion. However, the traditional simple threshold method is subjective, random, and ignores nonlinear relationships. This paper proposes a new cointegration deep reinforcement learning (DRL) pairs trading model applied to Dalian Commodity Exchange futures to capture nonlinear relationships and gain profits. The CA-DRL model outperforms other models in terms of efficiency and performance.