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https://github.com/ferdousbhai/investor-agent

A Model Context Protocol server for building an investor agent
https://github.com/ferdousbhai/investor-agent

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A Model Context Protocol server for building an investor agent

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# investor-agent: A Financial Analysis MCP Server



## Overview



The **investor-agent** is a Model Context Protocol (MCP) server that provides comprehensive financial insights and analysis to Large Language Models. It leverages real-time market data, fundamental and technical analysis to deliver:



- **Market Movers:** Top gainers, losers, and most active stocks with support for different market sessions

- **Ticker Analysis:** Company overview, news, metrics, analyst recommendations, and upgrades/downgrades

- **Options Data:** Filtered options chains with customizable parameters

- **Historical Data:** Price trends and earnings history

- **Financial Statements:** Income, balance sheet, and cash flow statements

- **Ownership Analysis:** Institutional holders and insider trading activity

- **Earnings Calendar:** Upcoming earnings announcements with date filtering

- **Market Sentiment:** CNN Fear & Greed Index, Crypto Fear & Greed Index, and Google Trends sentiment analysis

- **Technical Analysis:** SMA, EMA, RSI, MACD, BBANDS indicators (optional)



The server integrates with [yfinance](https://pypi.org/project/yfinance/) for market data and automatically optimizes data volume for better performance.



## Architecture & Performance



**Robust Caching & Error Handling Strategy:**



1. **`yfinance[nospam]`** → Built-in smart caching + rate limiting for Yahoo Finance API

2. **`hishel`** → HTTP response caching for external APIs (CNN, crypto, earnings data)

3. **`tenacity`** → Retry logic with exponential backoff for transient failures



This multi-layered approach ensures reliable data delivery while respecting API rate limits and minimizing redundant requests.



## Prerequisites



- **Python:** 3.12 or higher

- **Package Manager:** [uv](https://docs.astral.sh/uv/). Install if needed:

  ```bash

  curl -LsSf https://astral.sh/uv/install.sh | sh

  ```



### Optional Dependencies



- **TA-Lib C Library:** Required for technical indicators. Follow [official installation instructions](https://ta-lib.org/install/).



## Installation



### Quick Start



```bash

# Core features only

uvx investor-agent



# With technical indicators (requires TA-Lib)

uvx "investor-agent[ta]"

```



## Tools



### Market Data

- **`get_market_movers(category="most-active", count=25, market_session="regular")`** - Market movers data including top gainers, losers, or most active stocks. Supports different market sessions (regular/pre-market/after-hours) for most-active category. Returns up to 100 stocks with cleaned percentage changes, volume, and market cap data

- **`get_ticker_data(ticker, max_news=5, max_recommendations=5, max_upgrades=5)`** - Comprehensive ticker report with essential field filtering and configurable limits for news, analyst recommendations, and upgrades/downgrades

- **`get_options(ticker_symbol, num_options=10, start_date=None, end_date=None, strike_lower=None, strike_upper=None, option_type=None)`** - Options data with advanced filtering by date range (YYYY-MM-DD), strike price bounds, and option type (C=calls, P=puts)

- **`get_price_history(ticker, period="1mo")`** - Historical OHLCV data with intelligent interval selection: daily intervals for periods ≤1y, monthly intervals for periods ≥2y to optimize data volume

- **`get_financial_statements(ticker, statement_types=["income"], frequency="quarterly", max_periods=8)`** - Financial statements with parallel fetching support. Returns dict with statement type as key

- **`get_institutional_holders(ticker, top_n=20)`** - Major institutional and mutual fund holders data

- **`get_earnings_history(ticker, max_entries=8)`** - Historical earnings data with configurable entry limits

- **`get_insider_trades(ticker, max_trades=20)`** - Recent insider trading activity with configurable trade limits

- **`get_nasdaq_earnings_calendar(date=None, limit=100)`** - Upcoming earnings announcements using Nasdaq API (YYYY-MM-DD format, defaults to today).



### Market Sentiment

- **`get_cnn_fear_greed_index(indicators=None)`** - CNN Fear & Greed Index with selective indicator filtering. Available indicators: fear_and_greed, fear_and_greed_historical, put_call_options, market_volatility_vix, market_volatility_vix_50, junk_bond_demand, safe_haven_demand

- **`get_crypto_fear_greed_index()`** - Current Crypto Fear & Greed Index with value, classification, and timestamp

- **`get_google_trends(keywords, period_days=7)`** - Google Trends relative search interest for market-related keywords. Requires a list of keywords to track (e.g., ["stock market crash", "bull market", "recession", "inflation"]). Returns relative search interest scores that can be used as sentiment indicators.



### Technical Analysis

- **`calculate_technical_indicator(ticker, indicator, period="1y", timeperiod=14, fastperiod=12, slowperiod=26, signalperiod=9, nbdev=2, matype=0, num_results=100)`** - Calculate technical indicators (SMA, EMA, RSI, MACD, BBANDS) with configurable parameters and result limiting. Returns dictionary with price_data and indicator_data as CSV strings. matype values: 0=SMA, 1=EMA, 2=WMA, 3=DEMA, 4=TEMA, 5=TRIMA, 6=KAMA, 7=MAMA, 8=T3. Requires TA-Lib library.



## Usage with MCP Clients



Add to your `claude_desktop_config.json`:



```json

{

  "mcpServers": {

    "investor": {

      "command": "uvx",

      "args": ["investor-agent"]

    }

  }

}

```



## Local Testing



For local development and testing, use the included `chat.py` script:



```bash

# Install dev dependencies

uv sync --group dev



# Set up your API key

export OPENAI_API_KEY="your-api-key"  # or ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.



# Optional: Set custom model (defaults to openai:gpt-5-mini)

export MODEL_IDENTIFIER="your-preferred-model"



# Run the chat interface

python chat.py

```



For available model providers and identifiers, see the [pydantic-ai documentation](https://ai.pydantic.dev/models/).



## Debugging



```bash

npx @modelcontextprotocol/inspector uvx investor-agent

```



## License



MIT License. See [LICENSE](LICENSE) file for details.