On methods of building the trading strategies in the cryptocurrency markets

Cover Page

Cite item

Full Text

Abstract

The paper proposes a trading strategy for investing in the cryptocurrency market that uses instant market entries based on additional sources of information in the form of a developed dataset. The task of predicting the moment of entering the market is formulated as the task of classifying the trend in the value of cryptocurrencies. To solve it, ensemble models and deep neural networks were used in the present paper, which made it possible to obtain a forecast with high accuracy. Computer analysis of various investment strategies has shown a significant advantage of the proposed investment model over traditional machine learning methods.

About the authors

Eugeny Yu. Shchetinin

Financial University under the Government of Russian Federation

Author for correspondence.
Email: riviera-molto@mail.ru
ORCID iD: 0000-0003-3651-7629

Doctor of Physical and Mathematical Sciences, Lecturer of Department of Mathematics

49, Leningradsky Prospect, Moscow, 125993, Russian Federation

References

  1. E. Y. Shchetinin, “Study of the impact of the COVID-19 pandemic on international air transportation,” Discrete and Continuous Models and Applied Computational Science, vol. 29, no. 1, pp. 22-35, 2021. doi: 10.22363/2658-4670-2021-29-1-22-35.
  2. E. Y. Shchetinin, Y. G. Prudnikov, and P. N. Markov, “Long range memory modeling and estimation for financial time series,” RUDN Journal of Mathematics, Information Sciences and Physics, no. 1, pp. 98- 106, 2011, in Russian.
  3. J. Spörer, “Backtesting of algorithmic cryptocurrency trading strategies,” Available at SSRN, 2020. doi: 10.2139/ssrn.3620154.
  4. A. Y. Mikhailov, “Cryptoassets pricing and equity indices correlation,” Finance and Credit, vol. 24, no. 3, pp. 641-651, 2018, in Russian. doi: 10.24891/fc.24.3.641.
  5. A. Geron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems, 2nd Edition. O’Reilly Media, Inc., 2019.
  6. G. G. Ognev and E. Y. Shchetinin, “Deep neural networks with LSTM architecture for predicting financial time series,” in Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems 2020 (ITTMM 2020), in Russian, Moscow, Russia, April 13-17, 2020, pp. 280-283.
  7. A. Arratia and A. X. Lopez-Barrantes, “Do Google trends forecast bitcoins? Stylized facts and statistical evidence,” Journal of Banking and Financial Technology, vol. 5, no. 1, pp. 45-57, 2021.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download