Aave Analytics Dashboard
Detailed risk metrics for the Aave protocol – from pool utilization to asset concentration.
Aave V3 is a decentralized liquidity protocol with algorithmic interest rate determination.
This dashboard aggregates real-time data on protocol health: liquidity distribution, debt concentration, and specific risk metrics.
The data basis serves due diligence, risk monitoring, and market analysis.
Historical yield analyses are available in the Lending Yield Backtester.
Risk Metrics and Indicators
- Liquidity Structure: Analysis of available liquidity across all markets
- Debt Distribution: Detailed breakdown of outstanding credit volumes by asset
- Scenario Simulation: Stress tests to assess protocol stability under volatile market conditions
- Concentration Risks: Identification of concentration risks in asset distribution
- Real-time Status: Continuous synchronization with the current ledger state
- Pool Utilization: Monitoring of utilization rates and remaining capacities
Liquidity Distribution
Debt Distribution
The widgets are based on direct on-chain queries of the Aave V3 smart contracts. All views allow filtering by assets and adjustment of time periods.
Liquidity: Capital allocation and market distribution.
Liabilities: Credit volume and risk structure.
Scenarios: Simulation of market stress and impact analysis.
FAQ
Aave operates as a pool-based lending protocol on the Ethereum blockchain (and various Layer-2 networks). The protocol enables the supply and borrowing of liquidity against collateral, governed by smart contracts.
Flash Loans enable uncollateralized loans under the condition that liquidity is returned within the same transaction. This mechanism is primarily used for arbitrage and liquidation processes, contributing to market efficiency.
The Safety Module serves as a safeguard against shortfall events. Participants stake AAVE tokens, which can be liquidated (up to 30%) in the event of a deficit to recapitalize the protocol.
Interest rates are determined algorithmically based on the utilization rate of the respective liquidity pool. Higher borrowing demand leads to increasing interest rates to incentivize the supply of additional liquidity and mitigate the risk of a liquidity crunch.
The Treno Analytics API offers access to normalized historical data and real-time metrics. These datasets are suitable for quantitative analysis, strategy backtesting, and integration into risk management systems.