Aurelic

Demo

About

The problem it solves ## The Problem Aurelic Solves Leverage in DeFi is capital-inefficient, risky, and difficult to manage for most users. Existing leverage protocols typically require: - Full or overcollateralized upfront capital - Continuous monitoring to avoid liquidation - Exposure to sudden liquidation cascades - Reliance on external liquidators and MEV-driven execution Aurelic introduces a **prefunding-based leverage model** that changes how risk is handled. Instead of reacting to risk through liquidations, Aurelic defines and funds risk **upfront**, making leverage more predictable and safer by design. --- ## What Can People Use Aurelic For? Aurelic enables users to access leverage with lower upfront capital and clearer risk boundaries. - Opening leveraged positions using only a fraction of upfront collateral - Avoiding liquidation cascades through prefunded downside coverage - Using leverage without constant position monitoring - Accessing leverage with deterministic, protocol-enforced outcomes - Participating in leverage strategies with bounded and predefined risk - Building structured or automated strategies on top of prefunded positions Aurelic shifts leverage from **reactive liquidation** to **predefined settlement**, improving safety, UX, and capital efficiency.

How it's made

Challenges we ran into ## Challenges We Ran Into During the hackathon, most challenges came from implementing a prefunded leverage model that had to work end-to-end on-chain: from position creation, DEX execution, to deterministic settlement — all within tight safety constraints. --- ## Implementing Prefunding Without Incentive Misalignment **Challenge** Prefunding introduces a coordination problem between three actors: users, prefunders, and the protocol. Early implementations risked allowing prefunders to take excessive exposure without clearly bounded downside. **How We Addressed It** - Enforced strict exposure caps per prefunded position - Locked all risk parameters (leverage, coverage ratio, payout curve) at position creation - Implemented payoff curves directly in smart contracts - Ensured maximum loss is always computable upfront This made risk explicit and prevented hidden incentive leakage. --- ## Integrating DEX Execution Into Prefunded Positions **Challenge** Opening and closing leveraged positions requires DEX swaps, where execution price directly affects risk and settlement. Incorrect handling could break invariants or shift losses beyond prefunded limits. **How We Addressed It** - Routed all swaps through a dedicated execution module - Used exact-input swaps with strict slippage bounds - Validated post-swap balances before finalizing position state - Reverted the entire transaction if execution violated risk constraints DEX execution became deterministic and bounded by design. --- ## Replacing Liquidations With Deterministic On-Chain Settlement **Challenge** Without liquidators, the protocol must resolve positions purely through on-chain logic, including edge cases such as early exit or boundary outcomes. **How We Addressed It** - Implemented fully on-chain settlement functions - Removed dependency on keepers or off-chain actors - Computed outcomes solely from on-chain state and execution results - Explicitly handled boundary conditions and early close paths This eliminated liquidation races and MEV-driven execution. --- ## Handling Complex Accounting Across Multiple Value Sources **Challenge** Each position combines user collateral, prefunded coverage, DEX execution output, and protocol fees. Partial failures or incorrect ordering could break accounting invariants. **How We Addressed It** - Applied strict checks-effects-interactions across all flows - Finalized internal accounting before external calls - Ensured all position updates are atomic (all succeed or revert) - Used invariant checks instead of balance assumptions This ensured payouts and losses remained predictable and auditable. --- ## Balancing Capital Efficiency With Safety Under Hackathon Constraints **Challenge** Improving capital efficiency while maintaining safety required careful trade-offs, especially with limited time and no oracle dependency. **How We Addressed It** - Started with conservative leverage and coverage parameters - Avoided price oracles; relied on execution-time constraints - Modeled worst-case loss scenarios explicitly - Designed the system to fail conservatively under stress Aurelic prioritizes correctness and survivability over aggressive optimization.