Optimization of Shopping Strategy in Cryptocurrency Markets Based on Artificial Neural Networks
( Pp. 141-147)
More about authors
Aleksandr V. Savostyanov
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation
Abstract:
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.
How to Cite:
Savostyanov A. V., Grineva N. V. Optimization of Shopping Strategy in Cryptocurrency Markets Based on Artificial Neural Networks // ECONOMIC PROBLEMS AND LEGAL PRACTICE. 2024. Vol. 20. № 1. P. 141-147. (in Russ.) EDN: UNNQIK
Reference list:
Omer Berat Sezer, Murat Ozbayoglu Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach [Electronic resource], DOI: https://doi.org/10.1016/j.asoc.2018.04.024.
Omer Berat Sezer, Murat Ozbayoglu, Erdogan Dogdu An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework // ACM SE '17: Proceedings of the SouthEast Conference, DOI: https://doi.org/10.1145/3077286.3077294.
Binance API Documentation [Electronic resource], URL: https://binance-docs.github.io/apidocs.
Stock Statistics / Indicators Calculation Helper [Electronic resource], URL: https://pypi.org/project/stockstats.
Chuen Yik Kang, Chin-Poo Lee, Kian Ming Lim Convolutional Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit [Electronic resource], DOI: https://doi.org/10.3390/data7110149.
Keiron O'Shea and Ryan Nash An Introduction to Convolutional Neural Networks // Arxiv. —2015.
Aurélien Géron Hands-On Machine Learning with Scikit-Learn & TensorFlow [Electronic resource], URL: https://prognoztech.com/resources/content/Hand-on-ML.pdf.
Koroteev M.V. Textbook for the discipline «Machine Learning» —2023 [Electronic resource], URL: http://elib.fa.ru/rbook/books137315.pdf.
Savostyanov A., Grineva N., Stroeva E. FORECASTING TIME SERIES OF FINANCIAL INDICATORS USING ARTIFICIAL NEURAL NETWORKS // In the collection: Management of large-scale system development. Vol. CFP23GAE-ART, 2023. № 125, EDN: DBWWUQ, DOI: 10.1109/MLSD58227.2023.10304040
Savostyanov A.V., Grineva N.V., Stroeva E.N. Application of neural networks to assess the trajectory of development of financial markets // In the collection: Management of the development of large-scale systems (MLSD'2023). Proceedings of the Sixteenth International Conference. Moscow, 2023. pp. 803–809. EDN: FXDBUE, DOI: 10.25728/mlsd.2023.0803.
Grineva N.V. Construction of a neural network to predict option prices. // Problems of economics and legal practice. 2022. Vol. 18. No. 5. pp. 190–199. EDN: QKLZVC.
Omer Berat Sezer, Murat Ozbayoglu, Erdogan Dogdu An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework // ACM SE '17: Proceedings of the SouthEast Conference, DOI: https://doi.org/10.1145/3077286.3077294.
Binance API Documentation [Electronic resource], URL: https://binance-docs.github.io/apidocs.
Stock Statistics / Indicators Calculation Helper [Electronic resource], URL: https://pypi.org/project/stockstats.
Chuen Yik Kang, Chin-Poo Lee, Kian Ming Lim Convolutional Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit [Electronic resource], DOI: https://doi.org/10.3390/data7110149.
Keiron O'Shea and Ryan Nash An Introduction to Convolutional Neural Networks // Arxiv. —2015.
Aurélien Géron Hands-On Machine Learning with Scikit-Learn & TensorFlow [Electronic resource], URL: https://prognoztech.com/resources/content/Hand-on-ML.pdf.
Koroteev M.V. Textbook for the discipline «Machine Learning» —2023 [Electronic resource], URL: http://elib.fa.ru/rbook/books137315.pdf.
Savostyanov A., Grineva N., Stroeva E. FORECASTING TIME SERIES OF FINANCIAL INDICATORS USING ARTIFICIAL NEURAL NETWORKS // In the collection: Management of large-scale system development. Vol. CFP23GAE-ART, 2023. № 125, EDN: DBWWUQ, DOI: 10.1109/MLSD58227.2023.10304040
Savostyanov A.V., Grineva N.V., Stroeva E.N. Application of neural networks to assess the trajectory of development of financial markets // In the collection: Management of the development of large-scale systems (MLSD'2023). Proceedings of the Sixteenth International Conference. Moscow, 2023. pp. 803–809. EDN: FXDBUE, DOI: 10.25728/mlsd.2023.0803.
Grineva N.V. Construction of a neural network to predict option prices. // Problems of economics and legal practice. 2022. Vol. 18. No. 5. pp. 190–199. EDN: QKLZVC.
Keywords:
financial markets, cryptocurrencies, forecasting, artificial neural networks..
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