Liqui Documentation v1.1
  • πŸ“OVERVIEW
    • Introduction
    • Mission
    • Vision
  • πŸ‘¨β€πŸ’»BUILT ON MEGAETH
    • Built on MegaETH
  • ⭐LIQUI ECOSYSTEM
    • Protocol Architecture
  • Core Features
  • Hybrid Liquidity Architecture
  • Real-Time On-Chain Execution
  • Autonomous Margin Layer
  • Intelligent Leverage Calibration
  • Smart Liquidation Logic
  • Dynamic Buyback and Burn Engine
  • AI Decentralized Marketing Assistant (ADMA)
  • πŸ“ˆTrading
    • Launchpad
    • Spot Trading
    • Margin Trading
    • Order Types
  • Order Options
  • ⏳Perpetual Futures (coming soon)
  • πŸ—ΊοΈRoadMap
    • ROADMAP
  • 🀝DAO DEVELOPMENT
    • DAO Evolution Phases
  • πŸ‘ΎSECURITY & RELIABILITY
    • Security & Reliability
  • πŸ’ΈLIQUI TOKENOMICS
    • $LIQUI Tokenomics
    • $LIQUI Allocation
    • Pre-Seed Round
    • How to Buy $LIQUI Token
  • πŸ₯½COLLABORATION
    • The future of Web3 is built through collective effort
  • πŸ”—SOCIALS
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  • πŸ”‘OTHER
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  • Below is an overview of the key system features:
  • AI-Driven Order Routing
  • Real-Time Liquidity Mapping
  • Predictive Execution Modeling
  • Secure Smart Contract Execution
  • AI Feedback Loop
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Core Features

Liqui is a protocol powered by AI for intelligent order routing, liquidity analysis, and adaptive execution on MegaETH.

Below is an overview of the key system features:

AI-Driven Order Routing

  • Liqui’s AI engine continuously analyzes liquidity conditions, volatility, and historical flows to find the most efficient execution path.

How it Works:

  • The AI agent collects data from MegaETH liquidity pools

  • Simulates the best execution route based on slippage and depth

  • Sends the decision to smart contracts via the oracle

  • The contract executes the trade atomically

Real-Time Liquidity Mapping

The AI constantly monitors the state of liquidity across the network:

  • Identifies depth, spread, volatility, and TVL

  • Detects fragmentation or anomalies

  • Updates routing map in near real time

Implementation:

  • AI uses RPC or WebSocket access to MegaETH DEX pools

  • Continuously refreshes internal map

  • Sends results or hashed data to on-chain contracts

Predictive Execution Modeling

Before a trade is committed, the AI simulates outcomes:

  • Assesses slippage risk and fill probability

  • Predicts latency and price impact

  • Models the effect on subsequent orders

Implementation:

  • AI trained on historical data

  • Before execution, submits a risk-assessed payload

  • Contract validates slippage conditions before execution

Secure Smart Contract Execution

  • Trades are split and routed across multiple pools when needed

  • Slippage and gas limits are enforced

  • Execution is atomic: all or nothing

AI Feedback Loop

After each trade:

  • The AI receives logs (fill success, gas used, price deviation)

  • Models are updated to improve future execution

  • Routing becomes more efficient over time

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Last updated 7 days ago