Within-Venue Monitoring of BTC/USDT Liquidity and Resiliency on Binance: A Queueing-Theoretic Framework

This paper develops a queueing-organized framework for within-venue monitoring of BTC/USDT liquidity, signed-flow pressure, and resiliency on Binance. The model treats latent buy and sell pressure as occupancy processes and organizes three empirical diagnostics: the variance-per-BTC liquidity measure Rr, the mean-reversion rate θeff, and the companion signed-flow proxy βeffproxy. Using Binance trade data from 2020–2025, the empirical results show a pooled first-order variance–volume regularity away from the highest-volume tail, material rolling variation in liquidity and resiliency, and stronger next-day risk sorting from rolling Rr than from Amihud illiquidity or a Kyle-style minute-impact benchmark. In overlapping 30-day windows, θeff is positive by point estimate in roughly two-thirds of windows but clearly positive in only about two-fifths under a simple uncertainty buffer, implying that local recovery is often fragile or ambiguous. The one-hour symmetric benchmark is useful as a coarse center benchmark, but the heaviest tails require a Student-t VaR overlay. The framework is intended for risk managers, execution desks, market makers, and exchange surveillance teams that need indicators of thinning liquidity, weakening recovery, and one-sided signed flow. Queueing is useful because it turns those signals into one monitoring dashboard for market quality and short-horizon risk.