Automated Bitcoin Trading via Machine Learning Algorithms

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Statistical Arbitrage in Cryptocurrency Markets (2019, methodology): “Utilizing some of the ideas proposed by Shah and Zhang (2014), Madan et al. (2015) deploy several classiﬁcation models to predict the sign of Bitcoin price changes, leveraging information on prices, transaction volume, and data about the underlying blockchain.”
Cryptocurrency trading: a comprehensive survey (2020, methodology): “[171] modeled the Bitcoin price prediction problem as a binomial classification task, experimenting with a custom algorithm that leverages both random forests and generalized linear models.”
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis (2015, methodology): “Another approach predicted the price change of Bitcoin using random forests with 98.7% accuracy [4].”
Predicting the Price of Bitcoin Using Machine Learning (2018, methodology): “[15] also used Blockchain data, implementing SVM, Random Forests and Binomial GLM (generalised linear model) noting prediction accuracy of over 97% however without cross-validating their models limiting the generalisability of their results.”
An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information (2018, methodology): “Reference [15] compares the accuracy of predicting Bitcoin price through binomial logistic regression, support vector machine, and random forest.”