Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period.

翻译：基于前期基础工作（Lussange等，2020），本研究提出了一种模拟加密货币市场的多智能体强化学习（MARL）模型，并以币安（Binance）在2018年至2022年间连续交易的153种加密货币每日收盘价进行校准。与以往依赖零智能体或单一自主智能体方法的基于智能体的模型（ABM）或多智能体系统（MAS）不同，我们的方法通过赋予智能体强化学习（RL）技术来建模加密货币市场。这种集成旨在以自下而上的复杂性推理方式模拟个体与集体智能体，确保模型在近期此类市场的波动环境及COVID-19时期具有鲁棒性。该模型的关键特征还在于，其自主智能体基于两种信息来源进行资产价格估值：市场价格本身，以及超越市场价格的加密资产基本面价值近似值。基于真实市场数据校准的MAS模型，能够准确模拟加密货币市场的微观结构，并探测该特定时期熊市与牛市中的关键市场行为。