Leveraged positions on decentralized lending platforms

2026-01-20

AI Summary

This study addresses the optimization of multi-market leveraged staking (“loopy”) strategies on decentralized lending platforms to maximize returns while accounting for dynamic borrowing rates, leverage constraints, and transaction costs. We formulate this problem as a convex optimization framework for the first time and derive closed-form optimal solutions under linear, piecewise, and Morpho adaptive interest rate models, incorporating market-specific constraints and fee structures. Cross-chain backtesting on Ethereum and Base demonstrates that a dynamically rebalanced strategy achieves an annualized return of 6.2% in the wstETH/WETH markets from January to April 2025, substantially outperforming the 3.1% yield from unleveraged staking. This work establishes a verifiable and automatable theoretical foundation and empirical benchmark for DeFi leveraged strategies.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of pool utilization, we reduce the multi-market problem to a convex allocation over market exposures and obtain closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). The framework incorporates market-specific leverage limits, utilization-dependent borrowing costs, and transaction fees. Backtests on the Ethereum and Base blockchains using the largest Morpho wstETH/WETH markets (from January 1 to April 1, 2025) show that rebalanced leveraged positions can reach up to 6.2% APY versus 3.1% for unleveraged staking, with strong dependence on position size and rebalancing frequency. Our results provide a mathematical basis for transparent, automated DeFi portfolio optimization.

Problem

• leveraged staking
• DeFi
• lending platforms
• portfolio optimization
• borrow rates

Innovation

• leveraged staking
• convex optimization
• DeFi lending
• adaptive interest rate model
• portfolio optimization