In this chapter, we tried to model historical Bitcoin prices using all of the ensemble methods presented in earlier chapters of this book. As with most datasets, there are many decisions that affect a model's quality. Data preprocessing and feature engineering are some of the most important factors, especially when the dataset's nature does not allow direct modeling of the data. Time series datasets fall into this category, in which the construction of appropriate features and targets is required. By transforming our non-stationary time series to stationary, we improved the algorithm's ability to model the data.
To assess the quality of our models, we used the MSE of return percentages, as well as the Sharpe ratio (in which we assumed that the model was utilized as a trading strategy). When MSE is concerned, the best performing ensemble proved to be the...
