investor-agent

[](https://mseep.ai/app/ferdousbhai-investor-agent) [](https://archestra.ai/mcp-catalog/ferdousbhai__investor-agent) # 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) - **Intraday Data:** 15-minute historical stock bars via Alpaca API (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/). - **Alpaca API:** Required for intraday stock data. Get free API keys at [Alpaca Markets](https://alpaca.markets/). ## Installation ### Quick Start ```bash # Core features only uvx investor-agent # With technical indicators (requires TA-Lib) uvx "investor-agent[ta]" # With Alpaca intraday data (requires Alpaca API keys) uvx "investor-agent[alpaca]" # With all optional features uvx "investor-agent[ta,alpaca]" ``` ## 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). - **`fetch_intraday_data(stock, window=200)`** - Fetch 15-minute historical stock bars using Alpaca API. Returns CSV string with timestamp and close price data in EST timezone. Requires `investor-agent[alpaca]` installation and ALPACA_API_KEY/ALPACA_API_SECRET environment variables. ### 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.