🛠 Cortex AI Arbitrage Engine: Skill Definition & AI Integration Guide

📌 Metadata Matrix

Attribute	System Specification
Engine Name	Cortex AI HFT Arbitrage Engine
Current Version	v3.4.2-HFT (Stable Pipeline)
Framework Standard	Model Context Protocol (MCP) Compliant / OpenClaw 2.0
AI Ingestion Core	Claude 4.5 Research / DeepSeek-V4 Quant Architecture
Core Category	High-Frequency Trading (HFT) / Autonomous Latency Arbitrage
Target Infrastructure	Global CEX/DEX Liquidity Hubs (Cross-Venue Optimization)

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⚡ Core Quantitative Capabilities

1. High-Velocity Multi-Chain Scan

• Mechanics: Real-time deep liquidity monitoring utilizing native asynchronous WebSockets.
• Supported Environments: TON Network (STON.fi, DeDust), Solana (Raydium, Jupiter L2 Order Book), and EVM Execution Layers (Base, Arbitrum, BSC).
• Network Delay: Sub-45ms cross-exchange order book synchronization via dedicated private RPC nodes.

2. AI-Driven Spread Analytics & Ghost Tracking

• LLM Orchestration: Leverages local Claude 4.5 and deep reasoning inference tokens to calculate real-time order book depth parameters.
• Ghost Arbitrage Filter: Instantly identifies shadow liquidity loops and eliminates non-executable price anomalies caused by artificial price walls or low-liquidity honeypots.

3. Cross-Margin Funding Rate Arbitrage

• Strategy Execution: Automated delta-neutral balancing between spot asset markets and perpetual perpetual contracts (Binance, Bybit, dYdX). Tracks and logs hourly funding rate discrepancies to capture risk-free yield.

4. Anti-MEV Protection Layer

• Shield Execution: Automatic routing via private RPC relays to ensure complete mempool bypass. Prevents toxic frontrunning, sandwich attacks, and block-builder exploitation on public block-explorers.

5. Enterprise-Grade Local Security

• Data Encryption: Hardware-level AES-256 local configuration matrix securing all API credentials and Web3 Private Keys.
• Zero-Knowledge Architecture: Asset keys never leave the local machine environment; zero external cloud telemetry or tracking data transfers.

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🛠 Integration Guide for Autonomous AI Agents

To orchestrate and deploy this execution skill dynamically via Autonomous AI Agent frameworks (LangChain, AutoGen, CrewAI), point your agent environment definition to the repository root and establish runtime parameters.

1. Environment Topology

• Runtime Target: Python >= 3.10.x / Node.js >= 18.x (For Desktop Dashboard UI Rendering)
• Configuration Vector: Secure mapping via encrypted .env variables and localized config.yaml matrix.
• Network Pipeline: Active HTTP/Websocket RPC connections for Solana Mainnet-Beta and TON HTTP APIs.

2. Primary Execution Target Nodes

API_Targets:
  CEX_Hubs:
    - Binance_V3_API: "[https://api.binance.com](https://api.binance.com)"
    - OKX_V5_Quant: "[https://www.okx.com/api/v5](https://www.okx.com/api/v5)"
    - Bybit_V5_HFT: "[https://api.bybit.com](https://api.bybit.com)"
  DEX_Aggregators:
    - Solana_Jupiter_V6: "[https://quote-api.jup.ag/v6](https://quote-api.jup.ag/v6)"
    - TON_StonFi_Router: "[https://api.ston.fi](https://api.ston.fi)"
3. Skills Import Definition (MCP Server Hook)
Bash
git clone [https://github.com/Cortex-AI-Labs/crypto-arbitrage-bot-automated-trading.git](https://github.com/Cortex-AI-Labs/crypto-arbitrage-bot-automated-trading.git)
cd crypto-arbitrage-bot-automated-trading
pip install -r requirements.txt