پرپیچوئل فیوچرز میں خودکار ڈی لیوریجنگ: ادائیگی کی صلاحیت، آمدنی اور منصفانہ برتاؤ

# Autodeleveraging: Impossibilities and Optimization


# Autodeleveraging: Impossibilities and Optimization









Autodeleveraging (ADL) is a last-resort loss socialization mechanism for perpetual futures venues. It is triggered when solvency-preserving liquidations fail. Despite the dominance of perpetual futures in the crypto derivatives market, with over \$60 trillion of volume in 2024, there has been no formal study of ADL. In this paper, we provide the first rigorous model of ADL. We prove that ADL mechanisms face a fundamental \emph{trilemma}: no policy can simultaneously satisfy exchange \emph{solvency}, \emph{revenue}, and \emph{fairness} to traders. This impossibility theorem implies that as participation scales, a novel form of \emph{moral hazard} grows asymptotically, rendering `zero-loss' socialization impossible. On the positive side, we show that three classes of ADL mechanisms can optimally navigate this trilemma to provide fairness, robustness to price shocks, and maximal exchange revenue. We analyze these mechanisms on the Hyperliquid dataset from October 10, 2025, when ADL was used repeatedly to close \$2.1 billion of positions in 12 minutes. By comparing production ADL to transparent benchmark allocations, we find that Hyperliquid's production algorithm overshot the minimum trader profit haircut required to cover the shortfall. Our methodology suggests the excess profits lost by profitable traders is between \$45.0M and \$51.7M. In terms of the positions liquidated, this corresponds to roughly \$653.6M of positions being closed. This comparison also suggests that Binance overutilized ADL far more than Hyperliquid. Our results show both theoretically and empirically that optimized ADL mechanisms can dramatically reduce losses of trader profitability while maintaining exchange solvency.