  Securities markets are quintessential complex adaptive systems in which
heterogeneous agents compete in an attempt to maximize returns. Species of
trading agents are also subject to evolutionary pressure as entire classes of
strategies become obsolete and new classes emerge. Using an agent-based model
of interacting heterogeneous agents as a flexible environment that can
endogenously model many diverse market conditions, we subject deep neural
networks to evolutionary pressure to create dominant trading agents. After
analyzing the performance of these agents and noting the emergence of anomalous
superdiffusion through the evolutionary process, we construct a method to turn
high-fitness agents into trading algorithms. We backtest these trading
algorithms on real high-frequency foreign exchange data, demonstrating that
elite trading algorithms are consistently profitable in a variety of market
conditions---even though these algorithms had never before been exposed to real
financial data. These results provide evidence to suggest that developing
\textit{ab initio} trading strategies by repeated simulation and evolution in a
mechanistic market model may be a practical alternative to explicitly training
models with past observed market data.
