  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.
