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

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 re...

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Autores: Alaminos Aguilera, David, Salas Compas, M. Belén, Cisneros Ruiz, Ana J.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2026
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/226855
Acceso en línea:https://hdl.handle.net/2445/226855
Access Level:acceso embargado
Palabra clave:Algorismes computacionals
Algorismes genètics
Neurociència computacional
Computer algorithms
Genetic algorithms
Computational neuroscience
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spelling Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic TradingAlaminos Aguilera, DavidSalas Compas, M. BelénCisneros Ruiz, Ana J.Algorismes computacionalsAlgorismes genèticsNeurociència computacionalComputer algorithmsGenetic algorithmsComputational neuroscienceCrypto-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.John Wiley & Sons2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/226855Articles publicats en revistes (Empresa)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: https://doi.org/10.1002/isaf.70033Intelligent Systems in Accounting, Finance and Management, 2026, vol. 33, num.1https://doi.org/10.1002/isaf.70033(c) John Wiley & Sons, 2026info:eu-repo/semantics/embargoedAccessoai:diposit.ub.edu:2445/2268552026-05-27T06:46:51Z
dc.title.none.fl_str_mv Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
title Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
spellingShingle Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
Alaminos Aguilera, David
Algorismes computacionals
Algorismes genètics
Neurociència computacional
Computer algorithms
Genetic algorithms
Computational neuroscience
title_short Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
title_full Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
title_fullStr Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
title_full_unstemmed Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
title_sort Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading
dc.creator.none.fl_str_mv Alaminos Aguilera, David
Salas Compas, M. Belén
Cisneros Ruiz, Ana J.
author Alaminos Aguilera, David
author_facet Alaminos Aguilera, David
Salas Compas, M. Belén
Cisneros Ruiz, Ana J.
author_role author
author2 Salas Compas, M. Belén
Cisneros Ruiz, Ana J.
author2_role author
author
dc.subject.none.fl_str_mv Algorismes computacionals
Algorismes genètics
Neurociència computacional
Computer algorithms
Genetic algorithms
Computational neuroscience
topic Algorismes computacionals
Algorismes genètics
Neurociència computacional
Computer algorithms
Genetic algorithms
Computational neuroscience
description 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.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/226855
url https://hdl.handle.net/2445/226855
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1002/isaf.70033
Intelligent Systems in Accounting, Finance and Management, 2026, vol. 33, num.1
https://doi.org/10.1002/isaf.70033
dc.rights.none.fl_str_mv (c) John Wiley & Sons, 2026
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv (c) John Wiley & Sons, 2026
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv Articles publicats en revistes (Empresa)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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