Gauntlet Update: V3 Markets Integration Progress

Governance General
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Simple Summary

Over the last several months, Gauntlet has been expanding its simulation platform to provide continuous market risk management for Aave’s V3 markets. Gauntlet will first launch risk coverage for V3 AVAX and then will fast follow with V3 ETH (pending the V2 → V3 upgrade). Here, we update the community on our integration progress and timelines.

Context

Risk management follows where protocol development takes it. As part of Dynamic Risk Parameters, Gauntlet manages market risk for the V2 Ethereum market. We have since received community demand to manage market risk for other markets and have expanded Dynamic Risk Parameters coverage to Aave Arc to drive institutional comfort for the protocol.

As mentioned on the forums, community calls, and other channels, after Aave Arc integration Gauntlet has been prioritizing V3 AVAX integration over the last several months. While we recognize the importance of covering all Aave markets, we are focusing on the largest markets first to drive the most impact. The Aave AVAX V3 market currently has ~$1.4B supplied, ~$770M borrowed, and generates significant fees for the protocol. We are leveraging our experience in managing risk for money markets protocols in the Avalanche ecosystem to build robust data, modeling, and simulation infrastructure for Aave V3.

Specification

Risk Modeling

Aave V3 introduces new mechanisms that pose opportunities and challenges as they relate to managing market risk and optimizing capital efficiency. Building robust agents and models require mathematical research including statistical analysis on areas such as user elasticity (e.g., how will parameter changes impact user collateralization) and liquidator behavior (e.g., how do we predict which assets a liquidator chooses to liquidate, the slippage of the liquidation, and the price impact of the liquidation). These factors are already incorporated in our V2 simulations and below are several areas Gauntlet has been building infrastructure to support for V3 specifically. Our experience working with Aave during special situations including xSUSHI, stETH, and the ETH Merge helped guide our design and optimization architecture.

At a high level, Gauntlet will manage risk across chains, modeling different token and market metrics and analyzing their implications on user behavior and protocol impact. Given the upgraded market structure (e-mode, isolation mode, etc) user positions on V3 may look markedly different from V2. Our simulations ingest user positions and cross-chain data so that capital efficiency can be optimized through a robust understanding of market risk.

Efficiency mode: when supplied and borrowed assets are correlated in price, this feature allows users to extract greater borrowing power.

  • Gauntlet support: although e-mode enables greater capital efficiency, it may over-incentivize certain markets (e.g., stETH/ETH recursive positions). This may add outsized risk to the protocol in the event of “drawdown from par” scenarios. Thus, it is especially important to balance capital efficiency with risk in e-mode.

New risk parameters: parameters including borrow and supply caps.

  • Gauntlet support: optimize caps to maximize capital efficiency while minimizing insolvency risk, which our platform has done so before on Avalanche.

Isolation mode: borrowers can only supply one isolated asset as collateral for the main purpose of exposure management.

  • Gauntlet support: as liquidity and usage grow for isolated assets, Gauntlet’s simulations to quantify market risk help the community make better decisions as to whether those assets have become safe enough to be promoted out of isolation mode.

Portal: allows assets to seamlessly flow between Aave V3 markets on different networks.

  • Gauntlet support: modeling liquidations across chains will be essential in predicting the risk of bad debt.

Siloed borrowing: allow assets to be listed on Aave as a single borrowable asset.

  • Gauntlet support: depending on the nature of the asset, the drivers of risk for the siloed markets can differ significantly (stablecoin vs. volatile assets, etc.). Gauntlet’s simulations ingest the composition of user positions to tease out the scenarios that present the greatest market risk.

New governance controls: for example, updating parameters without impacting existing user positions.

Risk Admin role: provide entities the ability to alter certain risk parameters without requiring a governance vote

  • Gauntlet support: with the Risk Admin role, risk can be managed more efficiently to changing market conditions.

Gauntlet’s platform upgrades for V2 will be integrated into V3 as well. Including:

Integration Timelines

Integrating with a new market to robustly model market risk takes more than a click of a button. We provide this document here to help the community track our progress. At a high level, we are targeting our first set of parameter recommendations for V3 AVAX by early October. Below is a summary of the major integration milestones (workstreams may be parallelized):

  • Smart Contract Integration [DONE :white_check_mark:]
    • Gauntlet has integrated directly with the V3 contracts so that our simulations incorporate the nuances to robustly model risk.
  • Extract, Transform, and Load Pipelines [2 weeks]
    • Models are only as accurate as the data they ingest. Gauntlet builds ETL pipelines to ingest market data including asset volatility, DEX / CEX liquidity, trading volume, in addition to user data from Aave V3.
  • Model Development [3 weeks]
    • Building rational agents including Borrower, Supplier, and Liquidator with the proper economic logic under different market scenarios. Modeling out e-mode, portal, and isolation mode and their effects on capital efficiency and risk.
  • Dashboard Development [2 weeks]
    • Updating our dashboards to incorporate new features in Aave like e-mode and isolation mode.
  • Model QA [1 week]

In addition, Gauntlet is concurrently working on V3 Ethereum integration and expects to complete it soon after the upgrade.

Next Steps

  • Gauntlet will keep the community posted on our progress and V3 launch
  • Welcome any thoughts and questions from the community

Quick Links:

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Curious if y’all have run into any data quality issues (assuming you’re leveraging the Aave V3 subgraph).

Some grant applicants I’ve chatted with have run into issues with Aave subgraphs.

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Subgraphs are generally unreliable and we don’t use them for any engagements. Given the frequency and robustness needed for ongoing risk management we’ve had to develop internal infrastructure. Depending on the network our platform team develops and maintains ETLs from various data sources (blockchain-etl, archive nodes, third-party providers, etc.) to get a full picture of price/vol/slippage for the relevant collateral assets.

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Great context :slightly_smiling_face:

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Any interest in open sourcing any of the infra for others to leverage? Or even opening up maintained resources without revealing the source?

I’m sure it’s proprietary, but for a price, maybe it’s valuable enough to the ecosystem as a whole?

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Yes, we want to help improve the data fidelity for other service providers (e.g., analytics). This will take some time though. Suggestions in the interim are very welcome.

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No immediate suggestions. Would be really interesting to gauge demand for multi-chain, V3 data. I can think of three potential leads (want clean-enough data, but haven’t found a reliable source). If y’all make a public post outlining what data pipelines you have and are building, a dialogue could start.

Does Aave companies Data / Risk team have any thoughts here?

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Yonatan, CTO of Chaos Labs here. Before Chaos, I spent years building infrastructure at Apple and Meta (fka Facebook), focusing on distributed systems, networking, and big data infrastructure.

I can’t agree that subgraphs are unreliable. Subgraphs are simply a piece of tech that scrape data, process it, normalize and index it, so this statement is generalized. Any custom data pipeline faces the same challenges. These challenges become even more significant when discussing high throughput blockchain and data sets. The benefit of subgraphs is that they are open-sourced data collection frameworks with years of open source contributions and broad community adoption.

At Chaos, we use multiple data sources to power our applications, such as subgraphs, custom ETLs, and 3rd party data providers. Redundancy and reliability are critical when dealing with sensitive financial data. Multiple data sources allow us to compare and verify the validity of data. When critical data is on the line relying on a single source is extremely risky.

Errors in subgraph data validity are most likely due to errors in the collection logic and data parsing (and this applies to any data infra). For example, our pipelines detected anomalies in Aave data when integrating with the v3 Risk application. After researching the root cause, we uncovered a bug in the event logic parsing. We have already shipped our proposed solution to fix that issue. You can check out the subgraph repo here. This highlights a significant benefit of open source data collection frameworks such as subgraphs: community transparency. Any individual contributor can verify how data is collected and processed when something is off. Additionally, more eyes usually produce better software.

@AndrewA, since releasing the v3 Risk Application, we’ve been getting a lot of inbound around data availability and integrity for v3. We’re happy to participate in any ideation or initiatives to make this more accessible for the community. We think subgraphs are an excellent place to start as they are the most widely adopted, but we are also open to any other ideas or proposals.