Abstract
This work shows the results obtained from a comparison between a restricted and a unrestricted Bitcoin price classification, verifying whether the addition of technical indicators to the classic macroeconomic variables leads to an effective improvement in the prediction of Bitcoin price changes. The goal was achieved implementing different machine learning algorithms, such as Support Vector Machine (SVM), XGBoost (XGB), a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) neural network. Macroeconomic variables data were gained from Yahoo Finance website spanning a 4-year interval with a hourly resolution, while technical indicators data are provided by the python talib library. The variance problem on test samples has been taken into account through the cross validation technique which also allowed to evaluate a more reliable estimate of the model's performance. Furthermore, the Grid Search technique was used to find the best hyperparameters values for each implemented algorithm. The results were evaluated in terms of the well known classification metrics, i.e. accuracy, precision, recall and f1 score. Based on the results, it was possible to demonstrate that the unrestricted case outperforms the restricted one, verifying that the addition of the technical indicators to the macroeconomic variables actually improves the accuracy on Bitcoin price classification.
Published Version
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