Paper: arXiv 2307.13624

Authors: Arman Abgaryan, Utkarsh Sharma

Abstract

Decentralised automated market makers (AMMs) have gained significant attention recently. We propose an adaptive and automated Dynamic Function Market Maker (DFMM) that addresses challenges in this space. Our DFMM protocol includes a data aggregator and an order routing mechanism. It synchronises price-sensitive market information, asserting the principle of one price, and ensuring market efficiency. The data aggregator includes a virtual order book, asserting efficient asset pricing by staying synchronised with information from external venues, including competitors. The protocol’s rebalancing and order routing method optimises inventory risk through arbitrageurs, who are more likely to assist DFMM, enhancing protocol stability. DFMM incorporates protective buffers with non-linear derivative instruments to manage risk and mitigate losses caused by market volatility. The protocol employs an algorithmic accounting-asset, connecting all pools and resolving the issue of segregated pools and risk transfer. The settlement process is entirely protocol-driven, maximising risk management efficiency, and eliminating subjective market risk assessments. In essence, DFMM offers a fully automated, decentralised, and robust solution for automated market making. It aims to provide long-term viability and stability in an asset class that demands robustness.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces a novel Dynamic Function Market Maker (DFMM) protocol using non-linear derivative instruments and automated routing to manage inventory risk. However, it relies predominantly on conceptual and theoretical formulations with limited empirical backtesting or real-world data validation.

Research Flowchart

  flowchart TD
    A["Research Goal: Create Adaptive & Stable AMM (DFMM)"] --> B["Inputs: External Venues & Competitor Price Data"]
    B --> C["Data Aggregator & Virtual Order Book"]
    C --> D["Dynamic Order Routing & Arbitrage Rebalancing"]
    C --> E["Risk Buffer: Non-linear Derivatives & Accounting Asset"]
    D --> F["Protocol-Driven Automated Settlement"]
    E --> F
    F --> G["Outcome: Optimal Inventory Risk & Unified Segregated Pools"]