Cryptocurrency exchange simulation
File(s)
Accepted version
Author(s)
Mansurov, Kirill
Semenov, Alexander
Grigoriev, Dmitry
Radionov, Andrei
Ibragimov, Rustam
Type
Journal Article
Abstract
In this paper, we consider the approach of applying state-of-the-art machine learning algorithms to simulate some financial markets. In this case, we choose the cryptocurrency market based on the assumption that such markets more active today. As a rule, they have more volatility, attracting riskier traders. Considering classic trading strategies, we also introduce an agent with a self-learning strategy. To model the behavior of such agent, we use deep reinforcement learning algorithms, namely Deep Deterministic policy gradient. Next, we develop an agent-based model with following strategies. With this model, we will be able to evaluate the main market statistics, named stylized-facts. Finally, we conduct a comparative analysis of results for constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. As a result, we conclude that our model with a self-learning agent gives a better approximation to the real market than a model with classical agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern stock markets.
Date Issued
2024-11-01
Date Acceptance
2023-10-03
Citation
Computational Economics, 2024, 64, pp.2585-2603
ISSN
0927-7099
Publisher
Springer
Start Page
2585
End Page
2603
Journal / Book Title
Computational Economics
Volume
64
Copyright Statement
Copyright © 2024 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10614-023-10495-z
Identifier
http://dx.doi.org/10.1007/s10614-023-10495-z
Publication Status
Published
Date Publish Online
2024-01-02