A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading
This work addresses the problem of developing more profitable automated trading systems for cryptocurrency traders, though it appears incremental as it builds on existing reinforcement learning methods with causal enhancements.
The researchers tackled the challenge of profitable automated cryptocurrency trading by developing a reinforcement learning framework that integrates causal analysis, resulting in a system that surpassed benchmark strategies in profitability across five altcoins, though effectiveness varied.
Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This research focuses on developing a reinforcement learning (RL) framework to tackle the complexities of trading five prominent altcoins: Binance Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present the CausalReinforceNet~(CRN) framework, which integrates both Bayesian and dynamic Bayesian network techniques to empower the RL agent in trade decision-making. We develop two agents using the framework based on distinct RL algorithms to analyse performance compared to the Buy-and-Hold benchmark strategy and a baseline RL model. The results indicate that our framework surpasses both models in profitability, highlighting CRN's consistent superiority, although the level of effectiveness varies across different cryptocurrencies.