Portfolio Management is the decision-making process of allocating wealth across a set of assets, it is a fundamental problem in computational finance and has been extensively studied across several research communities.
Cryptocurrencies are digital assets designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets.
In this work, we try to adapt the ideas underlying the recent success of Deep Reinforcement Learning to the problem of Cryptocurrency Portfolio Management. We propose a model-free, policy gradient algorithm based on a Convolutional Neural Network (CNN) and a full exploitation of the explicit reward function. We also use a novel method for model selection that allows controlled sampling of validation data. Our algorithm is profitable in all the examined scenarios and achieves up to a 2-fold return in a period of 14 days.
