Journal
NEURAL COMPUTING & APPLICATIONS
Volume -, Issue -, Pages -Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-05359-8
Keywords
Deep reinforcement learning; Q-learning; Portfolio management; Dueling double deep Q-networks
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Funding
- Universita degli Studi di Milano - Bicocca within the CRUI-CARE Agreement
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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.
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