Measuring the Accuracy and Precision of Random Forest, Long Short-Term Memory, and Recurrent Neural Network Models in Predicting the Top and Bottom of Bitcoin price

The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to the nature of price behavior in financial markets, have been reported to be well in studies. In this respect, measuring and comparing the accuracy and precision of random forest (RF), long-short-term memory (LSTM), and recurrent neural network (RNN) models in predicting the top and bottom of Bitcoin prices are the main objectives of the present study. The approach to predicting top and bottom prices using machine learning models can be considered as the innovative aspect of this research, while many studies seek to predict prices as time series, simple, or logarithmic price returns. Pricing top and bottom data as target variables and technical analysis indicators as feature variables in the ۱-hour time frame from ۱/۱/۲۰۱۸ to ۶/۳۱/۲۰۲۲ served as input to the mentioned models for learning. Validation and testing are presented and used. ۷۰% of the data are considered learning data, ۲۰% as validation data, and the remaining ۱۰% as test data. The result of this research shows over ۸۰% accuracy in predicting the top and bottom Bitcoin price, and the random forest model’s prediction is more accurate than the LSTM and RNN models.

Keywords:

Algorithmic Trading , Random Forest , Recurrent Neural Network , Long-Short term memory , Top and bottom price prediction