Vasileios Kochliaridis, Anastasia Papadopoulou, Ioannis Vlahavas, "UNSURE - A Machine Learning Approach to Cryptocurrency Trading", Accepted in Applied Intelligence, Springer, 2024
Author(s): Vasileios Kochliaridis, Anastasia Papadopoulou, Ioannis Vlahavas
Keywords: Deep Reinforcement Learning, Machine Learning, Cryptocurrency Trading, Time-Series Clustering, Unsupervised Learning, Supervised Learning
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Abstract: Although cryptocurrency trading can be highly profitable, it carries significant risks due to extreme price fluctuations and high degree of market noise. To increase profits and minimize risks, traders typically use various forecasting methods, such as technical analysis and Machine Learning (ML), but developing effective trading strategies in noisy markets still remains a challenging task. Recently, Deep Reinforcement Learning (DRL) agents have achieved high performance on challenging tasks, including algorithmic trading, however it requires significant amount of time and high-quality data to train effectively. Additionally, DRL agents lack explainability, making them a less popular option for traders. The purpose of this paper is to address these challenges by proposing a reliable trading framework. Our framework, named UNSURE, generates high-quality features from candlestick data using technical analysis along with a novel parameterization method, and then exploits high price fluctuations by combining three ML components: A) Unsupervised component, which further improves feature quality by clustering market data; B) DRL component, which is responsible for training agents that open Buy or Short positions; C) Supervised component, which estimates price fluctuations in order to open and close positions efficiently, while reducing trading uncertainty. We demonstrate the effectiveness of this approach on nine cryptocurrency markets using several risk-adjusted performance metrics.