Optimal Adaptive Market Making: A Framework for Liquidity in Perpetual Futures Markets

On April 5, 2026, researchers Minmin Zeng and Yi Liu introduced a theoretical framework for optimal market making in perpetual futures markets. Their study focuses on high-yield liquidity provision without maker fees, presenting a rigorous model for market makers.

Understanding the Framework for Market Making

The framework models the market maker's problem as a stochastic optimal control issue, utilizing filtered probability spaces. The controls analyzed include adaptive bid-ask spreads and inventory hedging decisions across two exchanges, aiming to maximize profitability.

Key contributions of this research include:

• PnL Decomposition Theorem: Separates revenue into spread income, adverse selection loss, inventory carrying costs, hedging friction, and funding rate exposure.
• Hamilton-Jacobi-Bellman Equation: Addresses the joint spread-inventory-hedging control problem under CARA utility with a verification theorem.
• High-APY Regime Theorems: Characterizes profitable regions through five dimensionless parameters, leading to a Master APY Formula.

Impact of Zero-Fee Economics on Decentralized Exchanges

The analysis extends to zero-fee economics on decentralized perpetual exchanges. The research identifies optimal entry-exit thresholds, enhancing the understanding of market dynamics in a zero-fee environment. This aspect is crucial for traders seeking to optimize their strategies in decentralized venues.

Moreover, the study explores optimal cross-exchange hedging policies, considering funding rate dynamics and introducing a hedge regime trichotomy. This comprehensive approach provides insights into managing risks and maximizing returns in volatile markets.

Robustness and Inventory Management

A significant portion of the research quantifies parameter uncertainty tolerance through a robustness margin. This aspect is vital for market makers who must navigate unpredictable market conditions. Additionally, the study presents exponential drawdown probability bounds and a universal APY-VaR identity, offering practical tools for risk assessment.

The researchers also introduce an ergodic inventory distribution under optimal control using Bayesian adaptive estimation. This innovative approach supports market makers in maintaining balanced inventories while navigating the complexities of perpetual futures trading.

In conclusion, the findings of Zeng and Liu unify and extend existing paradigms in market making, providing a valuable resource for traders and researchers alike.