Polygon Labs has announced a strategic partnership with Manifold Trading, a quantitative investing firm, to enhance institutional liquidity in its decentralized finance (DeFi) ecosystem. The agreement, revealed on October 28, 2025, focuses on bringing data-driven liquidity and institutional-grade execution to Polygon's growing DeFi markets, aiming to make them more efficient and mature as institutional capital flows into on-chain finance.
Manifold's advanced quantitative trading tactics will deploy across leading Polygon decentralized exchanges, targeting tighter spreads, stabilized prices, and reduced cross-venue dislocations. This includes actively managing trading spreads, order sizes, and responsiveness to ensure continuous two-sided liquidity, which is crucial for a professional financial environment. The partnership builds on Polygon's recent infrastructure upgrades, such as the Rio upgrade, which improved speed and cost efficiency, and AggLayer, a decentralized cross-network protocol that unifies liquidity across blockchains.
Maria Adamjee, Head of Investor Relations at Polygon Labs, emphasized the importance of this move, stating, "Access to deep, stable liquidity is foundational to any mature financial system. Manifold's ability to actively manage spreads, size, and responsiveness across multiple venues makes them an ideal ecosystem partner as we continue scaling institutional-grade DeFi across the Polygon ecosystem." This collaboration addresses longstanding issues like liquidity fragmentation, which has hindered institutional adoption, and supports new DeFi use cases such as on-chain payments and real-world asset trading by providing fair, predictable execution conditions.
The alliance positions Polygon to attract fintechs, neobanks, and other institutional participants, aligning with broader trends of increased institutional involvement in DeFi. By enhancing liquidity depth and market predictability, Polygon aims to create a decentralized financial system that rivals traditional markets in transparency and performance.
