AI Financial Analyst Agent

Autonomous multi-agent stock research and visualization engine powered by CrewAI, FastMCP, yfinance, and local open-weight LLMs (DeepSeek-R1 / Ollama).

1. Overview

Retrieving, analyzing, and visualizing stock market data usually requires manually writing Python scripts, juggling yfinance and matplotlib parameters, and debugging code execution.

The AI Financial Analyst Agent accepts natural language queries (such as "Show me Tesla's YTD performance" or "Compare Apple and Microsoft stocks for the past year") and autonomously:

1. Parses ticker symbols, timeframes, and actions into structured Pydantic schemas.

2. Generates clean, production-ready Python visualization scripts using yfinance and matplotlib.

3. Validates & executes the generated code in a sandboxed subprocess with strict timeouts and error handling.

4. Exposes the entire pipeline as standard MCP (Model Context Protocol) tools ready for Claude Desktop and Claude Code.

Related MCP server: Yahoo Finance MCP Server

2. Architecture

flowchart TD
    User([User Prompt / MCP Client]) -->|Tool Call: analyze_stock| Server[FastMCP Server: server.py]
    Server -->|Kickoff| Crew[CrewAI Pipeline: finance_crew.py]

    subgraph CrewAI Sequential Process
        A1[Agent 1: Stock Data Analyst] -->|Structured Query Analysis| A2[Agent 2: Senior Python Developer]
        A2 -->|Draft Python Script| A3[Agent 3: Senior Code Execution Expert]
        A3 -->|Review & Self-Correction Loop| A2
    end

    Crew -->|Raw Output| Sanitizer[Output Sanitizer: utils/sanitize.py]
    Sanitizer -->|Strip <think> & Fences| Validator[AST Code Validator: utils/validate.py]
    Validator -->|Clean Validated Script| Saver[Save Code: outputs/<ticker>_<timeframe>.py]
    Saver -->|Subprocess Execution| Executor[Sandbox Executor: utils/executor.py]
    Executor -->|Render Chart PNG| Output[outputs/<ticker>_<timeframe>.png]
    Output -->|Result & Chart Path| User

Agent Roles

3. Key Hardening Features

• Output Sanitization Layer: DeepSeek-R1 reasoning models emit <think>...</think> blocks and markdown fences. utils/sanitize.py strips reasoning traces and extracts clean Python code before execution.

• AST Syntax Validation: All scripts are parsed via ast.parse() prior to disk persistence or execution, preventing syntax errors and invalid f-strings.

• Subprocess Sandbox Execution: Replaced unsafe in-process exec() with isolated subprocess.run([python, script], timeout=30, capture_output=True).

• Bounded Retry Loop: Agent delegation and re-generation are strictly bounded to a maximum of 3 attempts with human-readable error messages.

• Multi-LLM Provider Support: Run blazing fast in the cloud with Groq (llama-3.3-70b-versatile or deepseek-r1-distill-llama-70b), 100% free locally with Ollama (deepseek-r1:7b), or cloud OpenAI (gpt-4o) via .env.

4. Getting Started

Prerequisites

1. Python 3.12+

2. Ollama installed and running:

   ollama pull deepseek-r1:7b
   ollama serve

Installation

1. Clone the repository and enter the directory:

   git clone https://github.com/anshul/financial-analyst-agent.git
   cd "financial analysis"

2. Create and activate a virtual environment:

   python -m venv .venv
   # Windows PowerShell:
   .\.venv\Scripts\Activate.ps1
   # macOS / Linux:
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables:

   cp .env.example .env

   Edit .env if you wish to change the LLM provider, Ollama base URL, or logging level.

5. Usage

A. Running as an MCP Server (Claude Desktop / Claude Code)

Run the server over stdio transport:

python server.py

To connect to Claude Desktop, add the server to your claude_desktop_config.json:

{
  "mcpServers": {
    "financial-analyst": {
      "command": "C:\\Users\\anshu\\code\\financial analysis\\.venv\\Scripts\\python.exe",
      "args": [
        "C:\\Users\\anshu\\code\\financial analysis\\server.py"
      ],
      "cwd": "C:\\Users\\anshu\\code\\financial analysis"
    }
  }
}

Available MCP Tools

1. analyze_stock(query: str): Converts natural language financial requests into validated Python analysis scripts.

2. save_code(code: str, filename: Optional[str] = None): Validates AST and saves script to outputs/.

3. run_code_and_show_plot(script_path: Optional[str] = None): Safely executes the script and produces the .png chart.

4. list_saved_analyses(): Lists all historical scripts and charts generated.

B. Running as a CLI Script

You can run the multi-agent crew directly:

python finance_crew.py

C. Running the Streamlit Web Application

Launch the interactive dashboard in your browser:

streamlit run app.py

This starts a local web server (typically at http://localhost:8501) featuring natural language query input, real-time agent code generation, sandboxed chart execution, and a historical gallery of previous analyses.

6. Running Tests

The test suite includes 54 unit and integration tests covering sanitization, AST validation, subprocess execution, timeout handling, file persistence, MCP tools, and the Streamlit web app:

pytest -v

7. Known Limitations & Roadmap

• Indian Tickers (NSE/BSE): Yahoo Finance requires .NS or .BO suffixes (e.g. RELIANCE.NS). Future versions will support automatic suffix detection.

• Rate Limits: Intensive query bursts may be rate-limited by Yahoo Finance. A local SQLite/Parquet caching layer is planned.

• Fundamentals Agent: A 4th agent pulling P/E, EPS, and market capitalization alongside price charts is planned for v1.1.

8. License

This project is licensed under the MIT License.