Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading

Abstract

Crypto-assets have experienced significant growth in recent years, attracting substantial investments from institutional entities and individual investors alike. This surge in popularity necessitates sophisticated strategies to optimize returns. Concurrently, advancements in machine learning have revolutionized the forecasting of crypto-asset returns, facilitating algorithmic trading. Leveraging robust algorithms, this approach enables comprehensive market exploration and capitalizes on escalating computational capabilities. This manuscript presents a comparative analysis of neural networks, genetic algorithms, and fuzzy logic, framed within the Ordered Weighted Average (OWA) operator paradigm. These methods are integrated with deep learning and quantum computing principles to predict price movements in crypto-assets and other financial indices. Our findings indicate that the Quantum Genetic Algorithm excels in accurately forecasting asset price trends, while the Quantum Fuzzy Approach exhibits comparatively lower precision in predicting cryptocurrency price fluctuations. The empirical analysis employs high-frequency data sampled at 10-, 30-, and 60-minute intervals from October 2021 to February 2023. The dataset encompasses eleven cryptocurrencies (e.g., Bitcoin, Ethereum), ten Fan Tokens, ten NFTs, and nine reference financial indices (including Gold, WTI Oil, S&P 500, and Euro Stoxx 60). The implications of this research extend to the development of advanced algorithmic trading strategies, offering valuable tools for market participants and stakeholders in the financial sector. The methodologies discussed herein provide versatile and quantitative frameworks for analyzing diverse financial markets, highlighting their potential to enhance decision-making and improve investment outcomes.