A world model for market microstructure

A study of adaptive risk signals for high-frequency trading evaluates a sequential β-VAE world model trained on 82 days of Binance BTC, ETH, and SOL perpetual-futures order book data from January and March 2024. Rather than using learned representations for a downstream price-prediction task, the approach uses KL divergence between each observed latent state and a learned temporal prior as a measure of market surprise. Per-event surprise did not identify adverse selection or toxic individual executions in equity data, but rolling 500-event KL measures correlated positively with realized volatility on 77% of tested day-asset combinations and generally preceded volatility by a median of eight events. Predictive VAE variants, which reconstruct future rather than current observations, extended the median lead time to as much as 90 events, or roughly nine seconds at 100-millisecond snapshot frequency, although precision remained moderate at about 43–51% and did not substantially improve across model settings. Compared with rolling realized volatility and order flow imbalance, the signal led rolling volatility on most days and had stronger correlation with future volatility than order flow imbalance, suggesting it captures latent market structure rather than immediate flow. The proposed operational design combines maximum-lead, balanced, and higher-precision confirmation models into a layered alert system for adjusting spreads and quote sizes, while acknowledging that results are limited to a single volatile crypto period, coarse snapshot data, episodic activation, and untested performance during crashes, outages, or broader macroeconomic shocks.

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