A comparative study of Bitcoin and Ripple cryptocurrencies trading using Deep Reinforcement Learning algorithms
Dieu-Donne Fangnon, Armandine Sorel Kouyim Meli, Verlon Roel Mbingui, Phanie Dianelle Negho, Regis Konan Marcel Djaha, Lema Logamou Seknewna
TL;DR
This study addresses the challenge of applying deep reinforcement learning to volatile cryptocurrency trading by comparing four DRL algorithms (DQN, DDQN, Dueling DQN, A2C) across two assets, XRP and BTC, within a Markov Decision Process framework. Using a Yahoo Finance dataset (2015–2023) and a feature-rich state representation including MACD and RSI, the authors evaluate portfolio wealth as a primary performance metric. Results reveal asset-dependent performance: Duelling and Double DQN excel with XRP, while BTC benefits more from Double DQN; A2C generally underperforms relative to the other models. The work highlights practical considerations (data preprocessing, transaction costs) and suggests extensions like LSTM encoders and DDPG for improved trading strategies in cryptocurrency markets.
Abstract
Artificial intelligence (AI) has demonstrated remarkable success across various applications. In light of this trend, the field of automated trading has developed a keen interest in leveraging AI techniques to forecast the future prices of financial assets. This interest stems from the need to address trading challenges posed by the inherent volatility and dynamic nature of asset prices. However, crafting a flawless strategy becomes a formidable task when dealing with assets characterized by intricate and ever-changing price dynamics. To surmount these formidable challenges, this research employs an innovative rule-based strategy approach to train Deep Reinforcement Learning (DRL). This application is carried out specifically in the context of trading Bitcoin (BTC) and Ripple (XRP). Our proposed approach hinges on the integration of Deep Q-Network, Double Deep Q-Network, Dueling Deep Q-learning networks, alongside the Advantage Actor-Critic algorithms. Each of them aims to yield an optimal policy for our application. To evaluate the effectiveness of our Deep Reinforcement Learning (DRL) approach, we rely on portfolio wealth and the trade signal as performance metrics. The experimental outcomes highlight that Duelling and Double Deep Q-Network outperformed when using XRP with the increasing of the portfolio wealth. All codes are available in this \href{https://github.com/VerlonRoelMBINGUI/RL_Final_Projects_AMMI2023}{\color{blue}Github link}.