Comparing the Performance of Algorithmic Trading Systems based on Machine Learning in the Cryptocurrency Market

Document Type : Research Paper

Authors

1 PhD student in financial engineering, Department of Financial Management, Islamic Azad University, Qazvin branch, Qazvin, Iran

2 Assistant Professor, Department of Management, Faculty of Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran

3 Professor, Department of Management, Faculty of Social Sciences and Economics, AlZahra University, Tehran, Iran.

10.22051/jfm.2024.41815.2742

Abstract

The purpose of this research is to use the ensemble learning model to combine the predictions of random forest models, short-term long memory and recurrent neural network to provide an algorithmic trading system based on its. In this research, a prediction model based on ensemble machine learning model is presented and its performance is compared with each of the sub-algorithms and real data. In this research, in the first stage, using three machine learning models, the price top and bottom of Bitcoin have been predicted. In the second stage, the outputs of the models are presented as feature variables to the XGboost and LightGBM models to predict the roof and floors. Then, in the third stage, the outputs of the second stage are combined with the collective voting classification pattern to predict the next ceiling and floor. Bitcoin price top and bottom data in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 have been used as target variables and 31 technical analysis indicators as feature variables for three models in the first stage. Finally, forecast values and algorithmic trading systems were evaluated and compared with real data for 3 models and the introduced ensemble learning model. The obtained results show the improvement of the precision and accuracy of the proposed collective learning model in predicting the top and bottom of Bitcoin, as well as its better performance than the sub-algorithms.

Keywords

Main Subjects

References

 
Aggarwal, D., Chandrasekaran, S., & Annamalai, B. (2020). A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices. Journal of Behavioral and Experimental Finance, 27, Article 100335. https://doi.org/10.1016/j.jbef.2020.100335
 
Aghakhani, K., & Karimi, A. (2014). Investigating methods for predicting stock prices in the stock market and introducing an optimized method. In National Conference on New Achievements in Engineering and Basic Sciences (pp. 1-7). Ardabil. (in Persian)
 
Ampomah, E., Qin, Z., & Nyame, G. (2020). Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information, 11(6), 332. https://doi.org/10.3390/info11060332
 
Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. (2018). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, Article 102-117. https://doi.org/10.1016/j.najef.2018.06.013
 
Faghihi Nezhad, M., & Minaei Bidgoli, B. (2021). Development of an ensemble learning-based intelligent model for stock market forecasting. Scientia Iranica, 28(1), 395-411. https://doi.org/10.24200/sci.2019.50353.1654
 
Gupta, A., & Nain, H. (2020). Bitcoin price prediction using time series analysis and machine learning techniques. In A. Das, K. Deep, A. Tiwari, R. Kumar, & R. Bansal (Eds.), Intelligent Communication, Control and Devices (pp. 533-542). Springer. https://doi.org/10.1007/978-981-15-7106-0_54
 
Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020). An improved stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, Article 123390.
 
Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 13(1), 139-149. https://doi.org/10.1007/s41060-021-00279-9
 
Mallqui, D. C. A., & Fernandes, R. A. S. (2019). Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Applied Soft Computing, 75, 596-606. https://doi.org/10.1016/j.asoc.2018.11.038
 
 
Mohammadi Almoti, M., Haddadi, M. R., & Nadimi, Y. (2018). Modeling and evaluating different prediction models: Short-term memory, long-term memory, Markov switching, and hyperbolic tangent in predicting crude oil price fluctuations of OPEC. Financial Engineering and Securities Management Quarterly, 9(34), 249-272. (in Persian)
 
Moshari, M., et al. (2019). Designing a combined intelligent model for predicting golden points of stock prices. Investment Knowledge Quarterly, 8(29), 45-65. (in Persian)
 
Mousavi, S. O. (2019). Algorithmic Trading Technology: New Solutions in the Iranian Capital Market. Challenge Publications. (in Persian)
 
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, 21260.
 
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2019). Random forest-based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7), 200-212. https://doi.org/10.3844/ajassp.2019.200.212
 
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 7(1), Article 20. https://doi.org/10.1186/s40537-020-00299-5
 
Shirzoor Aliabadi, Z., & Ramazanzadeh, H. (2020). Bitcoin and its future prediction. Sixth International Conference on Industrial Engineering and Systems, Mashhad. (in Persian)
Tehrani, E., & Esmaili, A. (2012). Investigating the effect of using important technical analysis indicators on short-term investors' returns in the Tehran Stock Exchange. Financial Knowledge-Based Securities Analysis, 5(13), 21-33. (in Persian)
 
Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, Article 115716. https://doi.org/10.1016/j.eswa.2021.115716
Yang, Y., Wu, Y., Wang, P., & Jiali, X. (2021). Stock price prediction based on XGBoost and LightGBM. E3S Web of Conferences, 275, Article 01040. https://doi.org/10.1051/e3sconf/202127501040

Files

History

  • Receive Date: 18 September 2022
  • Revise Date: 14 November 2022
  • Accept Date: 12 April 2023

Share

How to cite

Statistics