TY - JOUR

T1 - Cryptocurrency Exchange Simulation

AU - Мансуров, Кирилл Дмитриевич

AU - Семёнов, Александр Владимирович

AU - Григорьев, Дмитрий Алексеевич

AU - Радионов, Андрей Владимирович

AU - Ибрагимов, Рустам Маратович

PY - 2024/1/2

Y1 - 2024/1/2

N2 - 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.

AB - 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.

KW - Agent-based model

KW - Cryptocurrency

KW - Market simulations

KW - Reinforcement learning

UR - https://www.mendeley.com/catalogue/7328d002-dd4e-35c7-abfc-7138ebd589b3/

U2 - 10.1007/s10614-023-10495-z

DO - 10.1007/s10614-023-10495-z

M3 - Article

JO - Computational Economics

JF - Computational Economics

SN - 0927-7099

ER -