Bitcoin price forecasting by applying combination of stacking method and Differential Evolution Algorithm

Document Type : Original Article


1 Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran,

2 Finance, Faculty of Management, Islamic Azad University E-campus, Tehran, Iran

3 Department of Information Technology Management, Electronic Branch, Islamic Azad University, Tehran, Iran,

4 Department of Economics, Imam Sadegh University, Tehran, Iran,



Due to the high market share and the leadership of Bitcoin’s price in the cryptocurrency market, this crypto asset has always been the leader of the trading trends of digital currencies and its movements have a significant impact on the price trends of other cryptocurrencies. In this article, by using a combination of meta-heuristic and machine learning methods, a model with the least error for estimating the price of Bitcoin is presented. This research uses the stacking approach of popular machine learning algorithms along with optimizing the parameters of the algorithms. In this study, for the first time in the field of cryptocurrencies, the differential evolution method has been used to find the most optimal stack combination and also to identify the most suitable parameters of each algorithm. This research in terms of type, data collection method and purpose is descriptive, modeling, and applied ,respectively.
In this study, the model results have been compared in three scenarios with inputs including OHLC features, technical indicators (more than 160 indicators), fundamental analysis indices (7 indicators) and their combination. Considering that most articles have applied technical indicators as input to machine learning models, the use of fundamental indices is one of the distinguishing features of this research. The results show that depending on the features used in each scenario, the type and order of placement of learning algorithms in the price prediction stack changes. All three scenarios investigated in this research have acceptable accuracy for price prediction.


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