A deep Q-learning portfolio management framework for the cryptocurrency market

【Author】 Lucarelli, Giorgio; Borrotti, Matteo

【Source】NEURAL COMPUTING & APPLICATIONS

【影响因子】5.102

【Abstract】Deep reinforcement learning is gaining popularity in many different fields. An interesting sector is related to the definition of dynamic decision-making systems. A possible example is dynamic portfolio optimization, where an agent has to continuously reallocate an amount of fund into a number of different financial assets with the final goal of maximizing return and minimizing risk. In this work, a novel deep Q-learning portfolio management framework is proposed. The framework is composed by two elements: a set of local agents that learn assets behaviours and a global agent that describes the global reward function. The framework is tested on a crypto portfolio composed by four cryptocurrencies. Based on our results, the deep reinforcement portfolio management framework has proven to be a promising approach for dynamic portfolio optimization.

【Keywords】Deep reinforcement learning; Q-learning; Portfolio management; Dueling double deep Q-networks

【发表时间】2020

【收录时间】2022-01-02

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