Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Cryptocurrency automatic trading research by using facebook deep learning algorithm

페이스북 딥러닝 알고리즘을 이용한 암호화폐 자동 매매 연구

  • Hong, Sunghyuck (Baekseok University, Division of Smart IT Engineering, FinTech major)
  • 홍성혁 (백석대학교 스마트IT공학부, 핀테크 전공)
  • Received : 2021.09.13
  • Accepted : 2021.11.20
  • Published : 2021.11.28
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

최근 인공지능의 딥러닝과 머신러닝을 이용한 예측시스템에 관한 연구가 활발히 진행되고 있다. 인공지능의 발전으로 인해 투자관리자의 역할을 인공지능을 대신하고 있으며, 투자관리자보다 높은 수익률로 인해 점차 인공지능으로 거래를 하는 알고리즘 거래가 보편화하고 있다. 알고리즘 매매는 인간의 감정을 배제하고 조건에 따라 기계적으로 매매를 진행하기 때문에 장기적으로 접근했을 때 인간의 매매 수익률보다 높게 나온다. 인공지능의 딥러닝 기법은 과거의 시계열 데이터를 학습하고 미래를 예측하여 인간처럼 학습하게 되고, 변화하는 전략에 대응할 수 있어 활용도가 증가하고 있다. 특히 LSTM기법은 과거의 데이터 일부를 기억하거나 잊어버리는 형태로 최근의 데이터의 비중으로 높여 미래 예측에 사용하고 있다. 최근 facebook에서 개발한 인공지능 알고리즘인 fbprophet은 높은 예측 정확도를 자랑하며 주가나 암호화폐 시세 예측에 사용되고 있다. 따라서 본 연구는 fbprophet을 활용하여 실제 값과 차이를 분석하고 정확한 예측을 위한 조건들을 제시하여 암호화폐 자동매매를 하기 위한 새로운 알고리즘을 제공하여 건전한 투자 문화를 정착시키는 데 이바지하고자 한다.

Keywords

Acknowledgement

This research was supported by 2021 Baekseok University Research Fund.

References

  1. Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174. https://doi.org/10.1002/isaf.1459
  2. Kim, S. W. (2021). Profitability of Trading System for Cryptocurrency. Journal of Digital Contents Society, 22(3), 555-562. https://doi.org/10.9728/dcs.2021.22.3.555
  3. Hong, S. (2020). A study on stock price prediction system based on text mining method using LSTM and stock market news. Journal of Digital Convergence, 18(7), 223-228. https://doi.org/10.14400/JDC.2020.18.7.223
  4. Hong, S. (2020). Research on Stock price prediction system based on BLSTM. Journal of the Korea Convergence Society, 11(10), 19-24. https://doi.org/10.15207/JKCS.2020.11.10.019
  5. Hoyos-Rivera, G. J., Gomes, R. L., Willrich, R., & Courtiat, J. P. (2006). Colab: A new paradigm and tool for collaboratively browsing the web. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 36(6), 1074-1085. https://doi.org/10.1109/tsmca.2006.883173
  6. Phelps, R., Krasnicki, M., Rutenbar, R. A., Carley, L. R., & Hellums, J. R. (2000). Anaconda: simulation-based synthesis of analog circuits via stochastic pattern search. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 19(6), 703-717. https://doi.org/10.1109/43.848091
  7. Gaur, S. (2020). Global forecasting of covid-19 using ARIMA based FB-Prophet. International Journal of Engineering Applied Sciences and Technology, 5(2), 463-467. https://doi.org/10.33564/IJEAST.2020.v05i02.077
  8. Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in science & engineering, 13(2), 22-30. https://doi.org/10.1109/MCSE.2011.37
  9. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. IEEE Annals of the History of Computing, 9(03), 90-95.
  10. Chikkakrishna, N. K., Hardik, C., Deepika, K., & Sparsha, N. (2019, December). Short-term traffic prediction using sarima and FbPROPHET. In 2019 IEEE 16th India Council International Conference (INDICON) (pp. 1-4). IEEE.
  11. Carneiro, T., Da Nobrega, R. V. M., Nepomuceno, T., Bian, G. B., De Albuquerque, V. H. C., & Reboucas Filho, P. P. (2018). Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677-61685. https://doi.org/10.1109/access.2018.2874767
  12. Lahmiri, S., & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35-40. https://doi.org/10.1016/j.chaos.2018.11.014
  13. Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of Information Security and Applications, 55, 102583. https://doi.org/10.1016/j.jisa.2020.102583
  14. Kumar, D., & Rath, S. K. (2020). Predicting the trends of price for ethereum using deep learning techniques. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 103-114). Springer, Singapore.
  15. Zoumpekas, T., Houstis, E., & Vavalis, M. (2020). ETH analysis and predictions utilizing deep learning. Expert Systems with Applications, 162, 113866. https://doi.org/10.1016/j.eswa.2020.113866
  16. Introduction to time series forecasting package Prophet. Hyper Connect Tech Blog[Website]. (2020.03.09.). URL:https://hyperconnect.github.io/2020/03/09/prophet-package.html