Behind the Scenes: LP Analysis and FiRM Daily Borrow Limits Framework

Welcome to another installment of RWG: Behind the Scenes, our first of the new season! This week, we’re excited to share insights into a recent update to the methodology driving our recommendations for FiRM market daily borrow limits. This enhancement leverages comprehensive data extraction and analysis performed on the largest liquidity pools of the underlying collateral. Join us as we delve into the details of our latest framework and its implications for FiRM’s risk management and market stability.

Introduction to Daily Borrow Limits

Daily borrow limits are a crucial component in maintaining the stability and security of FiRM markets. These limits help prevent excessive borrowing that could lead to liquidity crises or market manipulation. It also caps our potential bad debt exposure to 2x the daily borrow limit of a market (as the limit resets every day and a malicious scenario could play out over consecutive blocks) were there to be an exploit to FiRM. By setting appropriate daily borrow limits, we ensure a balanced and healthy ecosystem that supports both borrowers and the security needs of Inverse Finance.

You may recall that we previously provided insight into how we derive other market parameters such as supply ceiling, collateral factor, liquidation factor, and liquidation incentive. The daily borrow limit is the missing piece of the puzzle, completing our comprehensive framework for managing our lending protocol.

Leveraging Liquidity Pool Data

By examining the largest liquidity pools for the underlying collateral, we can extract and analyze key data points that inform our borrow limit decisions. Our new methodology incorporates detailed analysis of liquidity pools, specifically available on-chain liquidity and measures of the liquidity pools’ decentralization.

Steps in Our Enhanced Methodology

  1. Data Extraction:

We start by identifying the deepest liquidity pools for each collateral asset. For many of these collaterals, the deepest liquidity pools live on Curve. Extrapolating data from Curve pools is particularly tricky as holder information is obfuscated by liquidity providers staking their LP position on Convex for extra yield and thus not readily available to view on Etherscan. To overcome this, we devised simple queries on Inverse Watch that require the Convex reward contracts for each LP and the block number corresponding to the contract’s creation.

  • Staking Activity Query: The first query logs all Convex LP staking activity since inception.

  • Withdrawal Activity Query: The second query logs all Convex LP withdrawals since inception.

  • LP Position Calculation: The third query subtracts the staking by withdrawals, ultimately outputting a list of addresses and their corresponding LP position.

This data is then converted to USD using the current LP price at the time of the snapshot, providing a clear picture of the liquidity landscape.

  1. Liquidity Analysis:

The analysis helps gauge how distributed and decentralized the liquidity providers are, which has direct implications on the safety of the underlying collateral. Based on this, we can make informed assumptions about the future liquidity of the collateral.

  1. Borrow Limit Determination:

Our framework employs a formulaic approach using the following variables:

  • Studied TVL, Number of Addresses to 50%, and Top 3 Addresses (%) derived from the above LP analysis.
  • Supply Ceiling, Collateral Factor, and Liquidation Factor, determined from our collateral parameterization framework and liquidation factor model.

These variables are all weighted equally and scored from 0-5. Each collateral receives a total score from the sum of the individual variable scores.

Borrow Limit Rules:

  • Total Score 40 Daily Borrow Limit = Supply Ceiling 20%
  • Total Score 30 Daily Borrow Limit = Supply Ceiling 15%
  • Total Score 20 Daily Borrow Limit = Supply Ceiling 10%
  • Total Score < 20 Daily Borrow Limit = Supply Ceiling 5%

Key Findings and Implications

This revised methodology makes use of a more accurate and dynamic picture of available liquidity, leading to better-informed borrow limit settings. By incorporating real-time liquidity data and scenario analysis, we can proactively adjust borrow limits in response to changing market conditions, thereby reducing the risk of liquidity crises.

The DeFi landscape is ever-evolving, and so are our strategies. We are committed to continuous monitoring of liquidity pools and adapting our borrow limit framework as needed. This ongoing process ensures that FiRM remains resilient to market fluctuations and emerging risks. As always, we invite feedback and discussions from our community. Your insights are invaluable in helping us improve and adapt our approaches to meet the evolving needs of the ecosystem.

Stay tuned for more Behind the Scenes posts. Catch you next time!