Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning

Abstract

Algorithmic trading allows investors to avoid emotional and irrational trading decisions and helps them make profits using modern computer technology. In recent years, reinforcement learning has yielded promising results for algorithmic trading. Two prominent challenges in algorithmic trading with reinforcement learning are (1) extracting robust features and (2) learning a profitable trading policy. Another challenge is that it was previously often assumed that both long and short positions are always possible in stock trading; however, taking a short position is risky or sometimes impossible in practice. We propose a practical algorithmic trading method, SIRL-Trader, which achieves good profit using only long positions. SIRL-Trader uses offline/online state representation learning (SRL) and imitative reinforcement learning. In offline SRL, we apply dimensionality reduction and clustering to extract robust features whereas, in online SRL, we co-train a regression model with a reinforcement learning model to provide accurate state information for decision-making. In imitative reinforcement learning, we incorporate a behavior cloning technique with the twin-delayed deep deterministic policy gradient (TD3) algorithm and apply multistep learning and dynamic delay to TD3. The experimental results show that SIRL-Trader yields higher profits and offers superior generalization ability compared with state-of-the-art methods.