This paper proposes a decentralized data trading approach based on the Automated Market Maker (AMM) mechanism, aiming to address 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 the slippage generation mechanism 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 that the method reduces the average slippage amplitude from 2.1% to 0.5%, representing a 76.2% reduction, and significantly enhances price stability and market efficiency. This research explores the mechanism of applying AMM to data asset trading and mitigates the limitations of AMM in this scenario, providing a theoretical and empirical foundation for building low-cost, high-fairness data trading systems through mechanism innovation and technical optimization.
Si et al. (Sun,) studied this question.
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