MCP Python Quick Start Guide

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1. Overview of MCP Excel/Python Structure

• MCP Excel/Python includes core modules such as core constructors (mcp.tool.tools_main), parameter definitions (mcp.tool.args_def), and server version management (mcp.server_version).

• The framework supports modeling and analysis of various financial instruments, including curve modeling tools (forward curves, yield curves, bond curves), volatility surface tools (FX volatility, interest rate volatility), and financial product objects (bonds, interest rate swaps).

• For data access, MCP supports both local data construction and remote market data services.

• Key features include flexible polymorphic parameter input, high-performance C++ computation core (mcp.pyd), and standardized financial enumeration types. Developers can quickly build financial models using concise APIs, such as creating yield curves locally or fetching volatility surface data remotely, suitable for various financial quantitative analysis and derivative pricing scenarios.

2. Download and Install MCP Excel/Python

Steps:

1. Install Python Environment (recommended Python 3.9.12/3.9.13):
   • Install via Anaconda or portable Python (refer to Installation Guide).
2. Download mcp_excel:
   • Extract mcp_excel_xxx.zip to a local directory (e.g., C:\mcp_excel).
3. Configure Development Environment:
   • VSCode: Ensure PYTHONPATH includes lib/X64 (64-bit systems) or lib/win32 (32-bit systems).
   • PyCharm: Add lib/X64 and the project root directory to the interpreter path in project settings.

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3. MCP Excel/Python Directory Structure

mcp_excel/
├── lib/                  # Core library files
│   ├── X64/              # 64-bit system dependencies (DLLs, etc.)
│   └── win32/            # 32-bit system dependencies
├── example/              # Example code
│   ├── calendar/         # Calendar module examples
│   ├── curve/            # Yield curve examples
│   └── ...               # Other examples
├── mcp/                  # Python core modules
│   ├── tool/             # Utility classes
│   │   ├── tools_main.py # Main constructors (YieldCurve, FXForwardPointsCurve, etc.)
│   │   └── args_def.py   # Parameter definitions (supports polymorphism)
│   ├── mcp.py            # Object function implementations
│   └── utils/
│       └── enums.py      # Enum types (DayCounter, Frequency, etc.)
└── .vscode/              # VSCode configuration (includes PYTHONPATH preset)

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4. Python Core Module Overview

4.1 Constructors and Parameters

• Module Path: mcp.tool.tools_main and mcp.tool.args_def.py
• Features: Supports polymorphic parameter input (e.g., dictionaries or keyword arguments).
• Example:

  from mcp.tool.tools_main import McpYieldCurve
  
  # Construct via dictionary
  args = {
      "ReferenceDate": "2024-08-20",
      "Tenors": ["1M", "3M", "6M"],
      "Rates": [0.05, 0.055, 0.06],
      "DayCount": "Act365Fixed",
      ... ...
  }
  curve = McpYieldCurve(args)
  
  # Construct via keyword arguments
  curve = McpYieldCurve(
      ReferenceDate="2024-08-20",
      Tenors=["1M", "3M", "6M"],
      Rates=[0.05, 0.055, 0.06],
      ... ...
  )

4.2 Object Functions

• Module Path: mcp.mcp.py
• Functionality: Provides object methods (e.g., interest rate calculations, date adjustments).
• Example:

  from mcp.mcp import McpFXVolSurface
  
  fxVol = McpFXVolSurface(
    ... ...
  )
  # Get volatility
  vol = fxVol.GetVolatility(7.0, "2024-12-31")

4.3 Enum Types

• Module Path: mcp.utils.enums
• Common Enums:

  from mcp.utils.enums import DayCounter, Frequency
  
  print(DayCounter.Act365Fixed)  # Day count convention
  print(Frequency.Annual)        # Payment frequency

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5. Object Construction Methods

5.1 Local Construction

• Use Case: Requires manual input of market data and parameters.
• Steps:
  1. Prepare parameters (refer to definitions in args_def.py).
  2. Call the constructor (e.g., McpYieldCurve).
• Example:

  from mcp.tool.tools_main import McpFXForwardPointsCurve
  
  args = {
      "ReferenceDate": "2024-08-20",
      "FXSpotRate": 7.2,
      "ForwardPoints": [-50, -100, -150],
      "Tenors": ["1M", "2M", "3M"]
  }
  fwd_curve = McpFXForwardPointsCurve(args)

5.2 Server-Side Construction

• Use Case: Quickly fetch predefined data (requires internet connection).
• Features: Directly construct using short names (ShortName), no need for full parameter input.
• Example:

  from mcp.tool.tools_main import McpYieldCurve
  
  # Load predefined yield curve from server
  curve = McpYieldCurve(ShortName="CNY_IRS_CURVE")

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6. Quick Verification

# Test module imports
from mcp.tool.tools_main import McpYieldCurve
from mcp.utils.enums import DayCounter

# Print enum values
print("Day count convention:", DayCounter.Act365Fixed)

# Simple construction test
test_curve = McpYieldCurve(
    ReferenceDate="2024-08-20",
    Tenors=["1M"],
    Rates=[0.05]
)
print("Test curve constructed successfully!")

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7. Notes

1. Internet Dependency: Server-side construction requires a stable internet connection (access to MCP servers).
2. Path Issues: Ensure PYTHONPATH includes lib/X64 or lib/win32.
3. Version Compatibility: Python 3.9.x is recommended; other versions may require adjustments.

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With this guide, you can quickly set up the environment and start using MCP Python for financial computations!