Cryptocurrency Portfolio Management with Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning model using a convolutional neural network to optimize cryptocurrency portfolios, demonstrating significant returns and outperforming existing strategies in backtests.

Contribution

The study presents a novel model-less CNN approach trained with reinforcement learning for portfolio management, applicable to cryptocurrencies and other financial markets.

Findings

Achieved 10-fold returns in 1.8 months on cryptocurrency data.

Outperformed several existing portfolio strategies in backtests.

Demonstrated model's applicability beyond cryptocurrencies.

Abstract

Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Backtest trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 months' periods. Some recently published portfolio selection strategies are also used to perform the same back-tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.