We propose building multiple simulation and risk monitoring tools for the Euler DAO. These tools will allow anyone to validate the robustness of collateral and borrow factors, simplify collateral asset onboarding analyses, and enable protocol-wide simulations.
We are asking for a $80,000 grant from the Euler DAO grant program to build these simulation tools.
Lending protocols like Euler need to constantly monitor their risk exposure as market conditions change. We want to provide the Euler DAO with custom tooling to more easily and more transparently monitor risk vectors.
We believe that allowing users to easily simulate scenarios and regularly backtest collateral parameters will be extremely useful to the DAO. We believe these tools will enable community members to more actively participate in the DAO’s risk management decisions while making them more transparent.
Our team has completed multiple analytics and tooling projects for well-known DeFi protocols such as Aave, dYdX, Instadapp, Uniswap, and Notional. For example here a some of the tools we have built:
- Notional analytics dashboard
- Aave account simulation tool (still under development)
- Dune profile
- Euler subgraph
We have also collaborated with the Euler core team over the past few months by contributing to the development of the Euler subgraph, the Euler Dune dashboard, and minor parts of the Euler UI. Our knowledge of DeFi lending protocols and of Euler specifically will be key in allowing us to deliver useful tools to the DAO.
We propose to build the following risk and monitoring tools for the Euler DAO:
- Simulate the impacts of changes in asset prices for one or all Euler accounts. The simulation will output the simulated account values, LTV, and healthscores of all Euler accounts.
- Simulate the impacts of changes in collateral or borrow factors to see how many accounts would be at risk of liquidation given certain parameter changes.
- Simulate the protocol system-wide Value at Risk (VaR) based on custom scenarios (95%, 99% VaR).
- Monitor the cumulative Debt at Risk for all collateral assets.
- Monitor account sizes by liquidation prices for all collateral assets.
- Simulate the historical liquidation profitability of a simulated account based on historical data (gas prices, spot prices, historical on-chain liquidity).
- Assess how quickly the account would have been profitable to liquidate given its size and market conditions. Showcase the historical worst time to liquidation.
- Run a sensitivity analysis looking at how much bad debt the protocol would have accrued for a given set of accounts using different borrow and collateral factors.
- Analyze the historical on-chain liquidity of new potential collateral assets. The tool will output an analysis of historical slippage on major DEXes.
- Analyze the historical on-chain volatility of new potential collateral assets. The tool will output an analysis of historical volatility and maximum drawdown measures for given periods of time.
- Run a borrow and collateral factor backtesting analysis of new potential collateral assets.
We estimate the development cost of building and hosting these tools at $80K. We propose that the DAO funds 25% of the grant upfront and 75% upon delivery of the tools.
We estimate the total development time at 8-12 weeks from the time of the initial grant approval. This includes backend work, design, UI, and testing.
We propose setting a bi-weekly call with key members of the Euler core team to update them on our progress and get additional feedback.
- Yes - Approve and fund the development of a system-wide simulation tool, collateral factor backtesting tool, and of a collateral onboarding tool by the shippooor team.
- No - Vote against the funding and development of a system-wide simulation tool, collateral factor backtesting tool, and of a collateral onboarding tool by the shippooor team.
We are looking to hear feedback from the community before moving this proposal to a snapshot vote.