GenParkQuantTriangularArbitrageBellmanFordDetector
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-quant-triangular-arbitrage-bellman-ford-detector-skill
Production-grade Quantitative Finance & DeFi Microstructure skill engineered strictly with Python standard library. Provides real-time concentrated liquidity optimization, L2 orderbook imbalance metrics, negative-cycle triangular arbitrage detection, and Bayesian Black-Litterman portfolio rebalancing via the Model Context Protocol (MCP).
Architecture & Data Flow
flowchart TD
MarketFeed[L2 Tick / Orderbook / Exchange Rate Matrix / Covariance] --> Normalizer[Data Normalizer]
Normalizer --> Core["GenParkQuantTriangularArbitrageBellmanFordDetector"]
Core --> Analytics[Microstructure & Optimization Engine]
Analytics --> RiskGuard[Slippage & Drawdown Constraints]
RiskGuard --> MCPOutput[Standard MCP Tool Result / Execution Vector]Related MCP server: GenParkQuantConcentratedLiquidityTickOptimizer
Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Standalone MCP Server Launch
Run the native STDIO MCP server:
python mcp_server.pyLicense
This project is licensed under the MIT License - see the LICENSE file for details.
Tool Schema Changelog
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