ESS-LP: An Effective Slippage Scheme Based on Liquidity Pools for Data Trading

This paper proposes a decentralized data trading approach based on the Automated Market Maker (AMM) mechanism, aiming to break through the bottlenecks in data trading efficiency and fairness via the collaborative innovation of market-oriented pricing mechanisms and automated trading processes. We focus on constructing an automatic pricing and matching mechanism based on liquidity pools. Subsequently, mathematical modeling and simulations reveal slippage generation mechanisms in data liquidity pools under trading shocks and imbalances. To address these issues, a novel dual slippage optimization mechanism integrating dynamic trade splitting and alternating order sorting is proposed, which decomposes orders into sub-orders and reorganizes their timing, establishing a dynamic equilibrium model. Experiments show the method reduces average slippage amplitude from 2.1% to 0.5% and representing a 76.2% reduction, significantly enhancing price stability and market efficiency. This research explores the mechanism of applying AMM to data asset trading and overcomes AMM's limitations, providing a theoretical and empirical foundation for building low-cost, high-fairness data trading systems through mechanism innovation and technical optimization.