MCP_Financial_Analyst — DeepSeek AI Agent
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    MCP_Financial_Analyst

    MCP_Financial_Analyst

    adityapatel1010 June 19, 2025
    1 copies 0 downloads

    A local MCP powered financial analyst agent using the DeepSeek-R1 model via Ollama, utilizing Github Copilot and Python. It analyzes user queries related to stocks and financial data, running entirely offline.

    MCP Financial Analyst

    MCP Financial Analyst is a local, multi-agent system built using the MCP. It enables natural language-driven financial analysis by leveraging CrewAI agents and running a local DeepSeek-R1 language model through Docker-hosted Ollama.

    This project converts natural language queries into Python code that retrieves, analyzes, and visualizes stock market data, then securely executes the code and returns the results.


    Features

    • Natural language interface for financial data analysis
    • Modular, agent-based architecture using CrewAI:
      • Query Parser Agent – interprets natural language queries
      • Code Writer Agent – generates Python code for analysis and plotting
      • Code Executor Agent – safely executes code in a sandbox
    • Fully local LLM inference using DeepSeek-R1 via Docker-hosted Ollama
    • Chart generation with pandas, yfinance, and matplotlib
    • Runs locally using the MCP runtime

    Prerequisites

    • Python 3.10+
    • Visual Studio Code
    • Docker
    • Ollama installed and running via Docker
    • uv for running the MCP server (or use pip)

    To start Ollama with DeepSeek-R1 in Docker:

    docker run -it -p 11434:11434 --name give_name ollama/ollama
    docker_terminal:
        ollama pull deepseek-r1
        exit
    

    Ensure the Ollama server is up and listening on localhost:11434


    Install Dependencies

    Install Python dependencies using uv:

    uv pip install -r requirements.txt
    

    Running the MCP Server

    You can run the server directly from VS Code terminal:

    uv --directory /absolute/path/to/financial-analyst-deepseek run server.py
    

    This will launch the MCP server that manages the CrewAI agents and handles user queries.


    Example Query

    Query:

    Plot year-to-date performance for AAPL and MSFT
    

    The system will:

    1. Parse the query into structured instructions
    2. Generate Python code to fetch stock data using yfinance
    3. Plot the result using matplotlib
    4. Exec

    Tags

    ai-agentscrewaideepseek-r1dockerfinancial-analystllmmcpollama

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