Information technologies and computer sciences

Modern Approaches to the Intelligent Analysis of the Cryptocurrency Market

Authors

M. V. Dobroliubova
National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
О. О. Radovetskyi
О. М. Pomazun
Kyiv National Economic University named after Vadym Hetman ROR , Kyiv National Economic University ROR
М. О. Markin
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” ROR
DOI https://doi.org/10.31649/1997-9266-2025-181-4-126-135

Keywords

cryptocurrency dataset machine learning deep learning neural networks AI systems intelligent data analysis database programming

Abstract

The article addresses the challenges of intelligent analysis in the cryptocurrency market and, due to the limitations of traditional financial analysis methods, substantiates the need for the implementation of artificial intelligence (AI) systems. These systems are essential for processing large volumes of heterogeneous data, improving forecasting accuracy, and enhancing decision-making efficiency, particularly in light of the market’s high volatility, dynamic nature, and complexity. The review of recent studies on AI-based models for cryptocurrency price prediction reveals a growing demand for the development of flexible, hybrid architectures that integrate intelligent approaches with classical analytical techniques. The paper analyses the architecture of a hybrid AI system, encompassing modules for data collection, pre-processing, model training, and results visualization. It details the handling of various data types, including time series, on-chain metrics, and textual information. Within the framework of the empirical study, a comparative analysis of three models — ARIMA, Random Forest Regressor, and LSTM — was conducted using an open dataset of historical Bitcoin prices. Special attention is given to feature engineering, with the introduction of additional parameters that significantly improved model training performance. Based on the research findings, a concept of a hybrid multi-layered AI system is proposed. This system includes data acquisition and normalization modules, a computation block utilizing GPU/TPU resources for model training, and dashboards and APIs for result visualization and integration with exchange platforms. The proposed conceptual AI model can serve as a foundation for practical implementation in investor-oriented and financial-analytical platforms. It may attract the interest of fintech developers, traders, and researchers in the digital economy, paving the way for high-precision forecasting tools and automated decision-making solutions in the cryptocurrency market.

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How to Cite

[1]
“Modern Approaches to the Intelligent Analysis of the Cryptocurrency Market”, Вісник ВПІ, no. 4, pp. 126–135, Aug. 2025, doi: 10.31649/1997-9266-2025-181-4-126-135.

Author Biographies

M. V. Dobroliubova, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

 Cand. Sc. (Eng.), Associate Professor, Associate Professor of the Chair of Information and Measurement Technologies, Associate Professor of the Chair of Information Systems in Economics

О. О. Radovetskyi

Kyiv National Economic University named after Vadym Hetman

М. О. Markin, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

 Cand. Sc. (Eng.), Associate Professor, Associate Professor of the Chair of Information and Measurement Technologies

References

Ana Todorovska, et al., “Using ML and Explainable AI to understand the interdependency networks between classical economic indicators and crypto-markets,” Physica A: Statistical Mechanics and its Applications, vol. 624, article 128900, August. 2023. https://doi.org/10.1016/j.physa.2023.128900 .
A. Stencel, What is a meme coin? Dogecoin to the moon! , 2023. [Electronic resource]. Available: https://hal.science/hal-04360574v1/document. Accessed: March 17, 2025.
Waichung Chung, et al., “Ascertaining price formation in cryptocurrency markets with machine learning”, The European Journal of Finance, vol. 30, рр. 78-100, 2024. https://doi.org/10.1080/1351847X.2021.1908390 .
В. В. Покиньчереда, і В. М. Бондаренко, «Криптовалюта: економічна сутність та підходи до облікового відображення,» Ефективна економіка, № 9, с. 1-15, 2022. https://doi.org/10.32702/2307-2105.2022.9.11.
Роль штучного інтелекту в криптовалютному ринку. [Електронний ресурс]. Режим доступу: http://www.spilnota.net.ua/ua/article/id-4839/ . Дата звернення: 30.04.2025.
С. Ткаленко та ін., «Аналіз розвитку міжнародного ринку криптовалют: стан, тренди та перспективи,» Фінансово-кредитна діяльність: проблеми теорії та практики, т. 5, № 46, с. 97-108, 2022. https://doi.org/10.55643/fcaptp.5.46.2022.3841 .
K. Muneer, and U. Fatima, “Cryptocurrencies Analytics with Machine Learning and Human-centered Explainable AI: Enhancing Decision-Making in Dynamic Market”, International Journal of Computer Applications, vol. 186, no. 62, pp. 52-67, 2025. https://doi.org/10.5120/ijca2025924418 .
M. Zubair, et al., “An Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction with Sentimental Cautioning”, IEEE Access, vol. 12, pp. 51395-51418, 2024. https://doi.org/10.1109/access.2024.3367129 .
Nrusingha Tripathy, et al., “Cryptocurrency Price Prediction: A Comprehensive Analysis of High-dimensional Features with Deep Learning Techniques,” in 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT). Conference Proceedings, 2024, pp. 1-6. http://doi.org/10.1109/IC-SIT63503.2024.10862895 .
В. С. Денисенко, «Аналіз торгових стратегій на криптовалютному ринку,» Ефективна економіка, № 5, с. 1-11, 2022. https://doi.org/10.32702/2307-2105-2022.5.83 .
R. Amirzadeh, A. Nazari, and D. Thiruvady, “Applying Artificial Intelligence in Cryptocurrency Markets: a Survey,”Algorithms, vol. 15, no. 11, рр. 1-26, 2022. https://doi.org/10.3390/a15110428 .
J. Fattah et al., “Forecasting of demand using ARIMA model,” International Journal of Engineering Business Management, vol. 10, рр. 1-9, 2018. https://doi.org/10.1177/1847979018808673 .
Y. Chen, and J. Li, “Recurrent Neural Networks algorithms and applications,” in 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). Conference proceedings, Zhuhai, China, 2021, pp. 38-43. http://doi.org/10.1109/ICBASE53849.2021.00015 .
S. Hochreiter, and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, рр. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735 .
L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, рр. 5-32, 2001. https://doi.org/10.1023/a:1010933404324 .
Xavier Amatriain, “Transformer models: an introduction and catalog,” arXiv (a curated research-sharing platform) 2024 , https://doi.org/10.48550/arXiv.2302.07730 .
О. О. Радовецький, «Інтелектуальний аналіз ринку криптовалют», на VІ Міжнар. наук.-практ. конф. мол. вч., асп. і студ. Сучасні інформаційні технології та системи в управлінні, Київ, 2025, с. 106-108.
What are the settings in the Market Making bot | Cryptohopper Documentation. Cryptohopper Documentation. [Electronic resource]. Available: https://docs.cryptohopper.com/docs/market-making/what-are-the-settings-in-the-market-making-bot. Accessed: 30.04.2025.

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