Predicting the Top and Bottom Prices of Bitcoin Using Ensemble Machine Learning

محورهای موضوعی : Financial and Economic Modelling

Emad Koosha ¹ (Department of Financial Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran)
Mohsen Seighaly ² (Department of Financial Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran)
Ebrahim Abbasi ³ (Department of Management, Faculty of Social Sciences and Economics, ALzahra University, Tehran, Iran)

تاریخ دریافت : 1401/06/27 تاریخ پذیرش : 1401/08/30 تاریخ انتشار : 1402/04/10

کلید واژه: ensemble machine learning, XGBoost, top and bottom price prediction, LightGBM, Algorithmic Trading,

چکیده مقاله :

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.

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