  In today's era of big data, deep learning and artificial intelligence have
formed the backbone for cryptocurrency portfolio optimization. Researchers have
investigated various state of the art machine learning models to predict
Bitcoin price and volatility. Machine learning models like recurrent neural
network (RNN) and long short-term memory (LSTM) have been shown to perform
better than traditional time series models in cryptocurrency price prediction.
However, very few studies have applied sequence models with robust feature
engineering to predict future pricing. in this study, we investigate a
framework with a set of advanced machine learning methods with a fixed set of
exogenous and endogenous factors to predict daily Bitcoin prices. We study and
compare different approaches using the root mean squared error (RMSE).
Experimental results show that gated recurring unit (GRU) model with recurrent
dropout performs better better than popular existing models. We also show that
simple trading strategies, when implemented with our proposed GRU model and
with proper learning, can lead to financial gain.
