Descripción
Description
AlphaEngine is a confidential alpha network for on-chain finance.
DeFi has open rails for execution, but the best strategies are still fragmented: talented strategists lack capital, allocators lack transparency, and teams cannot cover every market opportunity alone.
For this Buildathon, we built a new leverage-looping experience that lets users privately create and submit fixed-income DeFi strategies. The demo focuses on Pendle PT opportunities, stablecoin borrowing, and lending markets such as Morpho and Aave. A user can visually build a strategy, preview its risk and return profile, encrypt the strategy, and submit it for execution without publicly revealing the full strategy before it is processed.
This fits AlphaEngine’s broader vision: private strategies, open competition, better yield. It shows how encrypted strategy submission can unlock crowdsourced fixed-income strategies while keeping execution risk measurable and capital allocation more transparent.
Progreso del hackathon
**Progress During Buildathon**
During the Buildathon we built this as a new standalone AlphaEngine module from scratch, separate from our existing production contracts and apps.
We completed:
- A branded single-page web app for building encrypted leverage-loop strategies.
- Drag-and-drop/editable UEI action flow builder for Pendle, Morpho, Aave V3, and utility actions.
- Browser-side CoFHE encryption and on-chain `submitIntent` flow.
- Shared UEI codec for typed actions, selectors, canonical intent hashing, and token-unit amount handling.
- Foundry contracts for the UEI intent controller, operator permissions, BoringVault execution path, demo tokens, and demo protocol adapters.
- Unit, fuzz, and invariant tests for the contract side.
- Operator/listener service that watches `IntentSubmitted`, decrypts handles, runs risk policy, and executes or dry-runs approved strategies.
- Backend risk API for strategy simulation before execution.
- Risk engine with utilization-based borrow-rate curves for Aave and Morpho-style markets.
- 90-day hourly lending market data to model historical borrowing rate and utilization behavior.
- Strategy risk metrics including borrow utilization, liquidation buffer, health factor, cost drag, slippage, expected drawdown, rate-tail risk, and VaR/tail-loss.
- Stress scenarios for collateral drawdowns, utilization spikes, borrow-rate increases, and liquidity pressure.
- Execution policy outputs that classify strategies as executable, warning, or blocked before the operator executes them.
- Hosted demo deployment: Vercel UI plus Railway operator/risk API on Arbitrum Sepolia.
- AI Agent skill for calling the api to simulate and select the best strategies.
