Mavric: : AI Trading Companion
Your autonomous AI trading agent that manages cross-chain portfolios with transparent performance attestation, making DeFi trading accessible and trustworthy for everyone.
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About
The problem it solves Mavric addresses several critical challenges in DeFi trading: 1. Complex Cross-Chain Management - Users struggle to manage assets across multiple chains - Manual cross-chain trading is time-consuming and error-prone - Difficult to track portfolio performance across networks 2. Trust and Transparency - Lack of verifiable track record for trading algorithms - No standardized way to assess trading agent performance - Limited accountability in automated trading systems 3. Accessibility Barriers - DeFi trading requires deep technical knowledge - High learning curve for cross-chain operations - Complex decision-making process for optimal trades Our Solution: - AI-powered trading agent that handles cross-chain portfolio management - On-chain attestations for every trade and performance metric - User-friendly interface that simplifies complex DeFi operations - Autonomous trading with customizable risk parameters - Real-time portfolio analytics across multiple chains This makes DeFi trading accessible to regular users while maintaining transparency and trust through blockchain technology.Mavric Additional Features NO It was built in HACKATHON Duration
How it's made
Challenges we ran into 1. AI Agent Integration Challenges - Initially struggled with AgentKit's context management for trading decisions - Solved by implementing a custom prompt engineering system with dynamic market context - Created a robust error handling system for AI predictions 2. Cross-Chain Synchronization - Socket Protocol integration initially caused race conditions in cross-chain transactions - Implemented a queue-based transaction system with atomic operations - Developed fallback mechanisms for failed cross-chain transfers 3. Performance Attestation Architecture - EAS attestation format needed careful design to be both comprehensive and gas-efficient - Created a modular attestation structure that captures key metrics while minimizing storage costs - Implemented batched attestations for multiple trades to reduce gas fees 4. Real-Time Price Oracle Integration - Supra Oracle data sometimes showed latency issues - Built a caching layer with time-weighted average prices - Implemented redundancy with multiple price sources 5. User Experience Optimization - Balancing automation with user control was challenging - Created an intuitive permission system for different levels of automation - Developed real-time feedback mechanisms for AI decisions