Cryptocurrency portfolio management with deep reinforcement learning

By Zhengyao Jiang, Jinjun Liang. 2016

240 citations recorded by the source corpus.

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.

Subfields: Finance, Information Systems, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “In [140], portfolio management was implemented by CNN and DRL on 12 most-volumed cryptocurrencies.”
Statistical Arbitrage in Cryptocurrency Markets (2019, background): “Jiang and Liang (2017) follow a different approach based on deep reinforcement learning.”
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017, methodology): “We call these streams mini-machines or more formally Identical Independent Evaluators (IIE), and this topology feature Ensemble of IIE (EIIE) nicknamed mini-machine approach, to distinguish with the wholesome approach in an earlier attempt (Jiang and Liang, 2017).”
Cryptocurrency trading: a comprehensive survey (2020, background): “[129] proposed a two-hidden-layer CNN that takes the historical price of a group of cryptocurrency assets as an input and outputs the weight of the group of cryptocurrency assets.”
Deep reinforcement learning for automated stock trading: an ensemble strategy (2020, unknown): “The actor-only approach has been used in [24], [25], [26].”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “be applied to achieve the maximum long-term reward of the cryptocurrency management for wireless users as in [198].”
Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, unknown): “The most recent strand of literature focuses on cryptocurrencies’ portfolio (Jiang and Liang 2017; Liu 2019; Brauneis and Mestel 2019).”