DiffLOB: Diffusion Models for Counterfactual Generation in Limit Order Books

Abstract

Modern generative models for limit order books (LOBs) can reproduce realistic market dynamics, but they remain fundamentally passive: they either model what typically happens without accounting for hypothetical future market conditions, or they require interaction with another agent to explore alternative outcomes. This limits their usefulness for stress testing, scenario analysis, and decision-making. We propose DiffLOB, a regime-conditioned Diffusion model for controllable and counterfactual generation of LOB trajectories. DiffLOB explicitly conditions the generative process on future market regimes—including trend, volatility, liquidity, and order-flow imbalance, which enables the model to answer counterfactual queries of the form: “If the future market regime were X instead of Y, how would the limit order book evolve?” We introduce the first systematic evaluation framework for counterfactual LOB generation consisting of three criteria: (1) Realism, measuring how well generated trajectories can reproduce marginal distributions, temporal dependence structure and regime variables; (2) Counterfactual validity, testing whether interventions on future regimes induce consistent changes in the generated LOB dynamics; (3) Counterfactual usefulness, assessing whether synthetic counterfactual trajectories improve downstream prediction of future market regimes.