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.
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Acknowledgements
Financial support from the Saint-Petersburg University, Russia (D. Grigoriev and K. Mansurov, project ID: 101748259) and the Russian Science Foundation (R. Ibragimov, Project No. 20-18-00365) for various and non-overlapping parts of this research is gratefully acknowledged.
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Mansurov, K., Semenov, A., Grigoriev, D. et al. Cryptocurrency Exchange Simulation. Comput Econ (2024). https://doi.org/10.1007/s10614-023-10495-z
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DOI: https://doi.org/10.1007/s10614-023-10495-z