Black-Litterman Portfolio Rebalancer MCP Server
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-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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Related MCP Connectors
Build, backtest, and deploy quantitative trading strategies from your AI agent.
Unified financial infrastructure connecting AI agents directly to trade live/demo brokerage accounts, Web3 non-custodial wallets, real-time market data across equities, ETFs, crypto, forex, options, DeFi swaps, and prediction markets, institutional research feeds, and algorithmic strategy backtesters.
Agent-native crypto market-data over MCP+REST: order flow, whales, liquidations, calibrated scores
Market intelligence for AI agents. Real-time data, cross-market analysis, and regime detection.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to perform Black-Litterman portfolio optimization with investor views, backtesting, and asset analysis, generating dashboards for visualization.MIT
- AlicenseNot gradedqualityBmaintenanceEnables real-time optimization of Uniswap v3/v4 concentrated liquidity ticks by balancing fee APY against impermanent loss under asset volatility, alongside L2 orderbook imbalance metrics, triangular arbitrage detection, and Bayesian portfolio rebalancing via MCP.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables computing weighted orderbook imbalance, micro-price, and market-order VWAP execution slippage, alongside concentrated liquidity optimization, negative-cycle triangular arbitrage detection, and Bayesian Black-Litterman portfolio rebalancing, through standard MCP tool calls.7MIT
- AlicenseNot gradedqualityNot gradedmaintenanceEnables real-time L2 orderbook microstructure analysis with weighted imbalance and micro-price, concentrated liquidity optimization, triangular arbitrage detection, and Bayesian portfolio rebalancing through MCP tools.MIT