Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Optimizing Dynamic Portfolio Management in the Cryptocurrency Market Using Multi-Agent Deep Reinforcement Learning and the Fear and Greed Index

Document Type : Research Paper

Authors
Mohammad Vahidpour 1
Amir Daneshvar 2
Mohsen Amini Khouzani 3
Mahdi Homayounfar 4
1 Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Industrial Management, Electronic Branch, Islamic Azad University, Tehran, Iran
3 Department of Financial Engineering, Shahr-e-Qods Branch, Islamic Azad University, Shahr-e-Qods, Iran
4 Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Abstract
Selecting an appropriate investment portfolio is the foundation of modern financial theories and is particularly crucial in the highly diverse, complex and risky cryptocurrency market. Traditional models, such as the Markowitz model, are inadequate for addressing the dynamics of these financial environments, particularly in dealing with high computational complexity. Enhanced investment performance, and the fulfillment of diverse investor objectives hinge on selecting the right model and strategy. This study introduces a deep reinforcement learning environment capable of adapting to a dynamically changing state space with various assets. The proposed model adopts a multi-agent approach, with each agent assigned to a specific asset. It utilizes two DQN neural network models for action selection and LSTM neural network for predicting trend. For selecting appropriate actions, nine different indicators were utilized, including the Fear and Greed Index to identify market sentiment and other technical indicators. The dataset comprises 11 non-stable cryptocurrencies and one stable coin to preserve capital value. Two different strategies were used to test the model. The cumulative profit and the Sharpe ratio were employed as evaluation metrics. The results indicate that the average profitability of the proposed model is 2.32 times higher, and the Sharpe ratio is 1.45 times greater than the buy-and-hold strategy. Additionally, the use of LSTM alongside DQN leads to more appropriate action selection, ultimately optimizing and enhancing the profitability of the investment portfolio.
Keywords
Cryptocurrency
Deep reinforcement learning
Long short-term memory
Machine Learning
Portfolio optimization
Fear and Greed Index
Subjects
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  • Receive Date 25 January 2023
  • Revise Date 16 February 2023
  • Accept Date 02 April 2023