Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price
and sentiment prediction model generates the parameters for optimization procedure to make
decision and re-allocate the portfolio in the further step. Moreover, after the process of
prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and
CART model for the prediction of Bitcoin and Ethereum respectively. During the process of
portfolio optimization, this project is trying to use predictive prescription to robust the
uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the
outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration
of figure.