Cryptocurrency Exchange Simulation

Kirill Mansurov (), Alexander Semenov (), Dmitry Grigoriev (), Andrei Radionov () and Rustam Ibragimov ()
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Kirill Mansurov: Saint Petersburg University: Sankt-Peterburgskij Gosudarstvennyj Universitet
Alexander Semenov: University of Florida
Dmitry Grigoriev: Saint Petersburg University: Sankt-Peterburgskij Gosudarstvennyj Universitet
Andrei Radionov: Saint Petersburg University: Sankt-Peterburgskij Gosudarstvennyj Universitet
Rustam Ibragimov: Imperial College Business School

Computational Economics, 2024, vol. 64, issue 5, No 1, 2585-2603

Abstract: 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.

Keywords: Agent-based model; Reinforcement learning; Market simulations; Cryptocurrency (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10495-z

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