  This work aims to analyse the predictability of price movements of
cryptocurrencies on both hourly and daily data observed from January 2017 to
January 2021, using deep learning algorithms. For our experiments, we used
three sets of features: technical, trading and social media indicators,
considering a restricted model of only technical indicators and an unrestricted
model with technical, trading and social media indicators. We verified whether
the consideration of trading and social media indicators, along with the
classic technical variables (such as price's returns), leads to a significative
improvement in the prediction of cryptocurrencies price's changes. We conducted
the study on the two highest cryptocurrencies in volume and value (at the time
of the study): Bitcoin and Ethereum. We implemented four different machine
learning algorithms typically used in time-series classification problems:
Multi Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short
Term Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM).
We devised the experiments using the advanced bootstrap technique to consider
the variance problem on test samples, which allowed us 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 study shows that, based on the hourly frequency results, the
unrestricted model outperforms the restricted one. The addition of the trading
indicators to the classic technical indicators improves the accuracy of Bitcoin
and Ethereum price's changes prediction, with an increase of accuracy from a
range of 51-55% for the restricted model, to 67-84% for the unrestricted model.
