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mcp-quant-engine

mcp-quant-engine

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量化引擎 MCP 服务器 (mcp-quant-engine)

CI

基于 Model Context Protocol (MCP) 的量化金融计算服务器，使用 FastMCP 框架，为 AI 客户端（如 Claude）提供专业的金融数学计算工具。

功能概览

本服务器提供 24 个 MCP 工具，覆盖量化金融四大核心领域：

模块

工具数量

功能

pricing.py

6

期权定价（BS模型、隐含波动率、蒙特卡洛、Greeks、二叉树）

portfolio.py

5

组合优化（均值方差、有效前沿、Black-Litterman、HRP、绩效指标）

risk.py

5

风险度量（历史VaR、参数法VaR、MC VaR、CVaR、最大回撤）

fixed_income.py

4

固定收益（债券定价、久期、凸性、Nelson-Siegel曲线）

utils.py

4

工具函数（收益解析、矩阵解析、格式化、输入验证）

Related MCP server: Bloomberg MCP

安装

# 克隆项目
git clone https://github.com/yourusername/mcp-quant-engine.git
cd mcp-quant-engine

# 安装依赖
pip install -r requirements.txt

使用

直接运行

python server.py

配置 MCP 客户端

在 Claude Desktop 配置文件中添加：

{
    "mcpServers": {
        "quant-engine": {
            "command": "python",
            "args": ["path/to/mcp-quant-engine/server.py"]
        }
    }
}

工具列表

期权定价工具 (pricing.py)

工具

描述

参数

black_scholes_call

BS看涨期权定价

S, K, T, r, sigma

black_scholes_put

BS看跌期权定价

S, K, T, r, sigma

implied_vol

隐含波动率（牛顿迭代法）

price, S, K, T, r, option_type

monte_carlo_option

蒙特卡洛期权定价

S, K, T, r, sigma, n_sims, option_type

option_greeks

全部Greeks计算

S, K, T, r, sigma, option_type

binomial_tree

二叉树定价（美式期权）

S, K, T, r, sigma, steps, option_type

组合优化工具 (portfolio.py)

工具

描述

参数

mean_variance_optimize

均值方差优化

returns_str, cov_matrix_str, target_return

efficient_frontier

有效前沿计算

returns_str, cov_matrix_str, n_points

black_litterman

Black-Litterman模型

P_str, Q_str, cov_matrix_str, market_weights_str, tau

hrp_clustering

层次风险平价

returns_str

portfolio_metrics

组合绩效指标

weights_str, returns_str, cov_matrix_str, rf

风险度量工具 (risk.py)

工具

描述

参数

var_historical

历史模拟法VaR

returns_str, confidence

var_parametric

参数法VaR

mean, std, confidence

var_monte_carlo

蒙特卡洛VaR

returns_str, confidence, n_sims

cvar

条件VaR (CVaR/ES)

returns_str, confidence

max_drawdown

最大回撤

prices_str

固定收益工具 (fixed_income.py)

工具

描述

参数

bond_price

债券定价

face, coupon_rate, ytm, maturity, freq

bond_duration

久期计算

face, coupon_rate, ytm, maturity, freq

bond_convexity

凸性计算

face, coupon_rate, ytm, maturity, freq

nelson_siegel

NS收益率曲线拟合

beta0, beta1, beta2, tau, maturities_str

工具函数 (utils.py)

工具

描述

参数

parse_returns

解析收益序列

input_str

parse_matrix

解析矩阵

input_str

format_result

格式化数值

value, precision

validate_inputs

输入验证

args_str

输入格式说明

  • 收益序列：逗号分隔的数值字符串，如 "0.01,0.02,-0.01"

  • 矩阵：分号分隔行、逗号分隔列，如 "0.04,0.01;0.01,0.09"

  • 权重向量：逗号分隔的数值，如 "0.3,0.4,0.3"

技术栈

  • MCP SDK: mcp.server.fastmcp.FastMCP

  • 数值计算: NumPy, SciPy

  • 数据处理: Pandas

  • 优化求解: scipy.optimize.minimize (SLSQP)

  • 层次聚类: scipy.cluster.hierarchy

  • 统计分布: scipy.stats.norm

理论参考

  • Black, F. & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities.

  • Cox, J., Ross, S. & Rubinstein, M. (1979). Option Pricing: A Simplified Approach.

  • Markowitz, H. (1952). Portfolio Selection.

  • Black, F. & Litterman, R. (1991). Global Portfolio Optimization.

  • Lopez de Prado, M. (2016). Building Diversified Portfolios that Outperform Out-of-Sample.

  • Nelson, C. & Siegel, A. (1987). Parsimonious Modeling of Yield Curves.

  • Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk.

  • Rockafellar, R. & Uryasev, S. (2002). Conditional Value-at-Risk.

项目结构

mcp-quant-engine/
├── server.py                # MCP Server 入口
├── mcp_quant_engine/
│   ├── __init__.py           # FastMCP 实例创建
│   ├── pricing.py            # 期权定价工具（6个）
│   ├── portfolio.py          # 组合优化工具（5个）
│   ├── risk.py               # 风险度量工具（5个）
│   ├── fixed_income.py       # 固定收益工具（4个）
│   └── utils.py              # 数学工具函数（4个）
├── README.md
├── SKILL.md
└── requirements.txt

测试

运行单元测试：

pip install pytest flake8
pytest tests/ -v --tb=short

代码质量检查：

flake8 . --count --max-line-length=120 --statistics

许可证

MIT License

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