In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test trading strategies before deploying them to real-time trading, multi-agent market environments calibrated so that the time series that result from interaction of simulated agents resemble historical are often used. To ensure adequate testing, one must test trading strategies in a variety of market scenarios -- which includes both scenarios that represent ordinary market days as well as stressed markets (most recently observed due to the beginning of COVID pandemic). In this paper, we address the problem of multi-agent simulator parameter calibration to allow simulator capture characteristics of different market regimes. We propose a novel two-step method to train a discriminator that is able to distinguish between "real" and "fake" price and volume time series as a part of GAN with self-attention, and then utilize it within an optimization framework to tune parameters of a simulator model with known agent archetypes to represent a market scenario. We conclude with experimental results that demonstrate effectiveness of our method.

中文翻译：

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从时间序列中学习谁在市场上：通过多代理模拟器的对抗性校准发现市场参与者

在电子交易市场中，通常只有由多个市场参与者相互作用产生的价格或交易量时间序列是直接可观察的。为了在将交易策略部署到实时交易之前对其进行测试，经常使用经过校准的多代理市场环境，以便模拟代理交互产生的时间序列类似于历史。为确保进行充分的测试，必须在各种市场情景中测试交易策略——包括代表普通市场日和压力市场的情景（最近观察到由于 COVID 大流行的开始）。在本文中，我们解决了多代理模拟器参数校准的问题，以允许模拟器捕获不同市场制度的特征。我们提出了一种新颖的两步方法来训练鉴别器，该鉴别器能够区分“真实”和“虚假”价格和交易量时间序列，作为具有自注意力的 GAN 的一部分，然后在优化框架中利用它进行调整具有已知代理原型的模拟器模型的参数来表示市场场景。我们得出的实验结果证明了我们方法的有效性。