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Pools as Portfolios: Observed arbitrage efficiency & LVR analysis of dynamic weight AMMs

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  • Matthew Willetts
  • Christian Harrington

Abstract

Dynamic-weight AMMs (aka Temporal Function Market Makers, TFMMs) implement algorithmic asset allocation, analogous to index or smart beta funds, by continuously updating pools' weights. A strategy updates target weights over time, and arbitrageurs trade the pool back toward those weights. This creates a sequence of small, predictable mispricings that grow until taken, effectively executing rebalances as a series of Dutch reverse auctions. Prior theoretical and simulation work (Willetts & Harrington, 2024) predicted that this mechanism could outperform CEX-style rebalancing. We test that claim on two live pools on the QuantAMM protocol, one on Ethereum mainnet and one on Base, across two short rebalancing windows six months apart (July 2025 and January 2026). We perform block-level arbitrage analysis, and then measure long term outcomes using Loss-vs-Rebalancing (LVR) and Rebalancing-vs-Rebalancing (RVR) benchmarks. On mainnet, rebalancing becomes markedly more efficient over time (more frequent arbitrage trades with lower value extracted per trade), reaching performance comparable to or better than CEX-based models. On Base, rebalancing persists even when per-trade extraction is near (or below) zero, consistent with routing-driven execution, and achieves efficiencies that meet or exceed standard "perfect rebalancing" LVR baselines. These results demonstrate dynamic-weight AMMs as a competitive execution layer for tokenised funds, with superior performance on L2s where routing and lower data costs compress arbitrage spreads.

Suggested Citation

  • Matthew Willetts & Christian Harrington, 2026. "Pools as Portfolios: Observed arbitrage efficiency & LVR analysis of dynamic weight AMMs," Papers 2602.22069, arXiv.org.
    • Handle: RePEc:arx:papers:2602.22069
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    References listed on IDEAS

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    1. Matthew Willetts & Christian Harrington, 2024. "Optimal Rebalancing in Dynamic AMMs," Papers 2403.18737, arXiv.org.
    2. Guillermo Angeris & Alex Evans & Tarun Chitra, 2020. "When does the tail wag the dog? Curvature and market making," Papers 2012.08040, arXiv.org.
    3. Matthew Willetts & Christian Harrington, 2024. "Closed-form solutions for generic N-token AMM arbitrage," Papers 2402.06731, arXiv.org.
    4. Guillermo Angeris & Tarun Chitra & Alex Evans & Stephen Boyd, 2022. "Optimal Routing for Constant Function Market Makers," Papers 2204.05238, arXiv.org.
    5. Matthew Willetts & Christian Harrington, 2024. "Rebalancing-versus-Rebalancing: Improving the fidelity of Loss-versus-Rebalancing," Papers 2410.23404, arXiv.org.
    6. Theo Diamandis & Max Resnick & Tarun Chitra & Guillermo Angeris, 2023. "An Efficient Algorithm for Optimal Routing Through Constant Function Market Makers," Papers 2302.04938, arXiv.org.
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