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

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

• algorithmic trading
• top and bottom price prediction
• ensemble machine learning
• xgboost
• lightgbm