Aletheia - Confidence-Adaptive RFQ
**Team: ** pap0nt_
Submitted: March 22, 2026
Answer Capsule
Aletheia RFQ is a smart contract for oracle-aware RFQ settlement. It verifies maker-signed quotes against live Pyth BTC/USD data at execution time and blocks unsafe trades when the oracle update is stale, uncertainty is too high, or the live market has moved too far from the quoted terms.
What It Does
The core of the project is an on-chain settlement contract for BTC/USD RFQs. Instead of blindly executing a signed maker quote, the contract validates the quote against oracle freshness, confidence, and deviation limits before settlement.
This allows the contract to:
- execute trades in normal market conditions,
- reject stale oracle updates,
- reject excessively uncertain oracle states,
- reject quotes that have drifted too far from the live market.
The frontend and backend are included only as a demo layer to request quotes, stream updates, and showcase the contract’s decision logic.
Pyth Features Used
Check all that apply:
- Price Feeds (on-chain or off-chain)
- Entropy (randomness)
- Both
Links
Live Demo: aletheiarfq.xyz
Source Code: https://github.com/pap0nt/AletheiaRFQ
Litepaper: https://github.com/pap0nt/AletheiaRFQ/blob/main/docs/litepaper.pdf
Screenshots / Media
Tech Stack
Framework/Language: Solidity, TypeScript, Next.js, Node.js (Fastify)
Blockchain (if applicable): Base Sepolia
Agent Framework (if applicable): N/A
Deployment: Docker Compose + Nginx (HTTPS) + RPC provider
Why It Matters
Most RFQ systems trust an off-chain signed quote and only verify the signature on-chain. This contract goes further by using Pyth data as a live risk control layer during settlement.
It turns oracle data into execution policy:
- stale prices are rejected,
- high-uncertainty oracle states are rejected (high conf/price ratio),
- excessive quote-to-market drift is rejected.
This makes RFQ settlement safer during volatile market conditions without removing fast execution when the market is healthy.
Content Contributions (Required)
Public Post (Reddit, Dev.to, or Hashnode): Building a Confidence-Adaptive RFQ with Pyth Pro + Pyth Lazer
Technical Contribution (Stack Overflow answer or GitHub gist/example): GitHub gist
Bonus — X Platform Post: https://x.com/pap0nt/status/2035798709527588932?s=20
Licensing
This project is licensed under Apache 2.0
Eligibility Confirmation
- I am 18+ years old
- I am not located in an OFAC-sanctioned jurisdiction
- I confirm this is an original work created during the hackathon period
- I have read and agree to the Terms & Conditions