  Simulated environments are increasingly used by trading firms and investment
banks to evaluate trading strategies before approaching real markets.
Backtesting, a widely used approach, consists of simulating experimental
strategies while replaying historical market scenarios. Unfortunately, this
approach does not capture the market response to the experimental agents'
actions. In contrast, multi-agent simulation presents a natural bottom-up
approach to emulating agent interaction in financial markets. It allows to set
up pools of traders with diverse strategies to mimic the financial market
trader population, and test the performance of new experimental strategies.
Since individual agent-level historical data is typically proprietary and not
available for public use, it is difficult to calibrate multiple market agents
to obtain the realism required for testing trading strategies. To addresses
this challenge we propose a synthetic market generator based on Conditional
Generative Adversarial Networks (CGANs) trained on real aggregate-level
historical data. A CGAN-based "world" agent can generate meaningful orders in
response to an experimental agent. We integrate our synthetic market generator
into ABIDES, an open source simulator of financial markets. By means of
extensive simulations we show that our proposal outperforms previous work in
terms of stylized facts reflecting market responsiveness and realism.
