Cryptocurrency portfolio management with deep reinforcement learning

Zhengyao Jiang · Jinjun Liang

298 citations0/0 files storedIntelligent Systems Conference (IntelliSys)

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Stock Market Forecasting Methods Blockchain Technology Applications and Security Risk and Portfolio Optimization

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. Back test trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 month's 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.

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References

1964CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK*1959Portfolio Selection: Efficient Diversification of Investments 1962Portfolio Selection: Efficient Diversification of Investments.2016Deep Learning for Finance: Deep Portfolios 2001Learning to trade via direct reinforcement 1979Speculative bubbles, crashes and rational expectations 1986Modern Investment Theory.

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2021Forecasting and trading cryptocurrencies with machine learning under changing market conditions 2018Anticipating Cryptocurrency Prices Using Machine Learning 2022How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda 2020Deep reinforcement learning for automated stock trading 2021Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey 2020Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

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