  Predicting the direction of assets have been an active area of study and a
difficult task. Machine learning models have been used to build robust models
to model the above task. Ensemble methods is one of them showing results better
than a single supervised method. In this paper, we have used generative and
discriminative classifiers to create the stack, particularly 3 generative and 6
discriminative classifiers and optimized over one-layer Neural Network to model
the direction of price cryptocurrencies. Features used are technical indicators
used are not limited to trend, momentum, volume, volatility indicators, and
sentiment analysis has also been used to gain useful insight combined with the
above features. For Cross-validation, Purged Walk forward cross-validation has
been used. In terms of accuracy, we have done a comparative analysis of the
performance of Ensemble method with Stacking and Ensemble method with blending.
We have also developed a methodology for combined features importance for the
stacked model. Important indicators are also identified based on feature
importance.
