Development of a Cryptocurrency Trading Strategy Using Machine Learning Methods

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Resumo

This article presents the results of a study aimed at forecasting signals for buying and selling Bitcoin cryptocurrency using machine learning models. The conducted analysis included the study of cryptocurrency features and markets, technical analysis, development of trading strategies, application of mathematical methods based on moving averages, and building classification models for buy or sell signals. The results demonstrate the effectiveness of applying machine learning models in modern trading strategies in the cryptocurrency market.

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Sobre autores

Svetlana Mikhaiylova

Financial University under the Government of the Russian Federation

Autor responsável pela correspondência
Email: ssmihajlova@fa.ru
ORCID ID: 0000-0001-9183-8519

Dr. Sci. (Econ.), Associate Professor, Professor, Department of Data Analysis and Machine Learning, Faculty of Information Technologyand and Big Data Analysis

Rússia, Moscow

Sabina Sabirova

Financial University under the Government of the Russian Federation

Email: 202617@edu.fa.ru

Faculty of Information Technology and Big Data Analysis

Rússia, Moscow

Bibliografia

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  8. Ferdiansyah. Research on Bitcoin stock market forecasting: Methods, techniques and tools. Annual research seminar for graduate students. April 10–11, 2019, Universiti Teknologi Malaysia, Johor Bahru, Skudai.
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2. Fig. 1. Global market capitalization of cryptocurrencies

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3. Fig. 2. Visualization of moving averages and signals

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4. Fig. 3. Correlation of features and signal

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5. Fig. 4. Analysis of correlations of an expanded set of features

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6. Fig. 5. Modeling results based on initial features

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7. Fig. 6. Simulation results based on an extended set of features

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8. Fig. 7. Assessing the importance of features

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9. Fig. 8. Building models after reducing the dimensionality of features

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