From Market Facts to Executable Markets: Grounding Heterogeneous Trader Populations in Empirical Market Evidence

Data-driven financial market models can reproduce or predict observable outcomes, such as limit order book states and order flows, but they provide limited access to the participant-level mechanisms that generate them. Agent-based simulators make such mechanisms explicit, yet typically begin from prescribed trader populations rather than structures constrained by real market evidence. We address this gap by treating a heterogeneous, executable trader population as the object of calibration. It jointly specifies capital allocation across traders, asset allocation, strategy influence, and strategy-specific behavioral parameters, allowing changes in participant structure to propagate endogenously through order generation, limit-order-book execution, price formation, and resource updates. We use observed stylized facts to restrict the space of plausible populations and search for candidate markets whose simulated behavior is consistent with these observations. To make this high-dimensional problem tractable, we reparameterize constrained allocations and factorize high-dimensional residual variation, reducing the search dimension while retaining the full set of traders in every simulation. Experiments show that the resulting markets reproduce key financial stylized facts and achieve better held-out matching than alternative search strategies under comparable simulation budgets. These solutions should be viewed as evidence-consistent executable markets rather than unique reconstructions of the real trader population, providing controlled starting points for studying how changes in resources, strategies, and behavior propagate through endogenous market dynamics. This work reframes financial market simulation from reproducing market outputs toward empirically grounded experimentation on the mechanisms that generate them.