  Agent-based models (ABMs) are fit to model heterogeneous, interacting systems
like financial markets. We present the latest advances in Evology: a
heterogeneous, empirically calibrated market ecology agent-based model of the
US stock market. Prices emerge endogenously from the interactions of market
participants with diverse investment behaviours and their reactions to
fundamentals. This approach allows testing trading strategies while accounting
for the interactions of this strategy with other market participants and
conditions. Those early results encourage a closer association between ABMs and
ML algorithms for testing and optimising investment strategies using machine
learning algorithms.
