Black-Litterman Portfolio Rebalancer MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Black-Litterman Portfolio Rebalancer MCP ServerRebalance my portfolio using Black-Litterman with my views and a 10% drawdown limit"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-quant-black-litterman-portfolio-rebalancer-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["GenParkQuantBlackLittermanPortfolioRebalancer"]
Core --> Analytics[Microstructure & Optimization Engine]
Analytics --> RiskGuard[Slippage & Drawdown Constraints]
RiskGuard --> MCPOutput[Standard MCP Tool Result / Execution Vector]Related MCP server: genpark-quant-concentrated-liquidity-tick-optimizer-skill
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.
This server cannot be deployed
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