genpark-black-scholes-merton-greeks-engine-skill
mcp
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Black-Scholes-Merton European option analytical pricing and first/second-order Greeks (Delta, Gamma, Vega, Theta, Rho) engine.
README.md
genpark-black-scholes-merton-greeks-engine-skill
Production-Grade Quantitative Finance & Risk Engineering Agent Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
⚡ Overview & Architectural Significance
genpark-black-scholes-merton-greeks-engine-skill delivers zero-dependency quantitative finance, option Greeks calculation, Monte Carlo stochastic simulations, and fixed-income analytics engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities
- Zero External Dependencies: Operates exclusively via pure Python (
math,random,json). Zero NumPy/SciPy/QuantLib build dependencies. - Enterprise Financial Invariants: Implements formal Black-Scholes-Merton analytic differentials, Geometric Brownian Motion stochastic walks, Historical & Parametric VaR/CVaR, Macaulay/Modified duration & convexity, and Nelson-Siegel yield curve parameterizations.
- Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
🏗️ Architectural Topology & State Machine
flowchart TD
MarketData["Market Feed: Spot, Vol, Rates, Cash Flows"] --> RiskRouter["Quantitative Financial Router"]
RiskRouter --> BSMEngine["Black-Scholes-Merton Greeks Engine"]
RiskRouter --> MonteCarlo["Monte Carlo GBM Simulation Engine"]
RiskRouter --> VaREngine["Value-at-Risk & Expected Shortfall"]
RiskRouter --> BondEngine["Bond Duration & Convexity Evaluator"]
RiskRouter --> YieldCurve["Nelson-Siegel Yield Curve Interpolator"]
BSMEngine --> PortfolioSynthesis["Autonomous Risk Report & Hedging Strategy"]
MonteCarlo --> PortfolioSynthesis
VaREngine --> PortfolioSynthesis
BondEngine --> PortfolioSynthesis
YieldCurve --> PortfolioSynthesis
🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import BlackScholesGreeksEngine
# Initialize engine
engine = BlackScholesGreeksEngine()
# Execute self-testing benchmark suite
result = engine.benchmark_greeks_calculation()
print("Execution Result:", result)
🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-black-scholes-merton-greeks-engine-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-black-scholes-merton-greeks-engine-skill/mcp_server.py"]
}
}
}
📦 Smithery.ai & PyPI Deployment
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-black-scholes-merton-greeks-engine-skill.git
Maintained with ❤️ by GenPark AI Engineering • Powering Autonomous Quantitative Agents 🌍
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