Cryptocurrency trading using machine learning - Thomas E. Koker; Dimitrios Koutmos (Journal of Risk and Financial Management, 2020)
We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.
Keywords: 330; cryptocurrencies; machine learning; Bitcoin; direct reinforcement; risk-return