电邮这篇文章 Optimization of Shopping Strategy in Cryptocurrency Markets Based on Artificial Neural Networks
- 作者: Savostyanov A.V.1, Grineva N.V.1
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隶属关系:
- Financial University under the Government of the Russian Federation
- 期: 卷 20, 编号 1 (2024)
- 页面: 141-147
- 栏目: Mathematical, Statistical and Instrumental Methods in Economics
- URL: https://bakhtiniada.ru/2541-8025/article/view/284311
- EDN: https://elibrary.ru/UNNQIK
- ID: 284311
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详细
This study is relevant in the context of modern finance, where neural networks play a key role in the analysis and forecasting of price dynamics. The research focuses on identifying buy signals for the BTCUSDT and ETHUSDT trading pairs in the cryptocurrency market. To construct a neural network model that can automate the identification of moments beneficial for purchasing selected assets, various technical analysis indicators and convolutional neural networks (CNN) were used. The research includes an analysis of scientific literature, data collection, indicator selection, signal algorithm development, and the construction of neural network models. The main contribution of this work lies in the development and testing of models capable of predicting buy signals with a high accuracy, confirmed by accuracy indicators of over 92%. The findings of this study can be useful for private investors and financial institutions in forming investment strategies based on machine learning.
作者简介
Aleksandr Savostyanov
Financial University under the Government of the Russian Federation
编辑信件的主要联系方式.
Email: s.aleks-02@mail.ru
Faculty of Information Technology and Big Data Analysis
俄罗斯联邦, MoscowNatalia Grineva
Financial University under the Government of the Russian Federation
Email: ngrineva@fa.ru
Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Data Analysis and Machine Learning
俄罗斯联邦, Moscow参考
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