Dashboards
Aggregated Risk Data for Due Diligence
Detailed risk metrics for the Aave protocol – from pool utilization to asset concentration.
Detailed risk metrics for the Compound protocol – from pool utilization to asset concentration.
Cross-market analysis of stablecoin yields and risk profiles. Real-time data on peg stability, liquidity depth, and protocol interest rates.
The dashboards for Aave and Compound provide a structured overview of current risk parameters. They enable well-founded monitoring of protocol health based on verified on-chain data.
The data shown represents real-time values from the Treno platform (production environment). The dashboards are publicly accessible and can be used for due diligence checks or market analyses.
The focus is on aggregating relevant risk factors:
The scoring system transforms technical protocol data into standardized risk metrics. The methodology follows transparent risk management standards and enables an objective assessment of protocol safety.
The dashboards are available interactively and can be integrated into external systems via API or iFrame.
They serve investors and analysts as a tool for evaluating counterparty risks and monitoring market stability.
FAQ
The public dashboards show live data without access restrictions. The full version of the Treno platform extends this scope with specific portfolio tracking, historical deep analysis, and configurable alert systems.
Data is extracted directly from protocol smart contracts and via subgraphs. Treno normalizes this raw data and enriches it with external market prices and liquidity metrics.
The assessment is based on a weighted scoring model. Factors such as utilization rate, liquidity concentration, and volatility are aggregated into an overall risk score. Detailed information on methodology can be found at Risk Assessment.
The widgets are modular and can be integrated into external applications or reports via iFrame or API. Technical documentation is available on the Financial Widgets page.
Treno focuses on financially relevant metrics (solvency, liquidity, interest rate risk) rather than pure transaction volumes. This enables efficient creditworthiness assessment of protocols without manual raw data analysis. More background on the utility of on-chain data: