MaverickMCP

MaverickMCP: stock market MCP server for local analysis

MaverickMCP is an open-source stock market MCP server for stock analysis, portfolio tracking, and Python backtesting. It connects an AI assistant to Yahoo Finance data through yfinance, with no API key required for core tools. The server runs on your computer and stores your portfolio, watchlists, and trade journal in a local database.

MCP means Model Context Protocol, the standard that lets an AI assistant call external tools. Use MaverickMCP with clients such as Claude Desktop, Codex, Cursor, and VS Code. Optional packages add backtesting and financial research with a language model and web search.

MaverickMCP is for educational and informational use. It does not place orders or provide financial advice. Market data can be delayed, incomplete, or wrong.

Features

Area	What you can do
Market data and technical analysis	Get quotes, historical prices, fundamentals, RSI, MACD, and observed support and resistance levels.
Stock screening	Run bullish, bearish, and supply/demand screens over symbols you have loaded.
Portfolio tracking	Record positions, calculate average cost and profit or loss, review risk, manage watchlists, and keep a trade journal.
Optional backtesting and research	Test strategies on historical data, compare results, or research companies and sectors with source citations.

The current source has 38 core tools, 12 optional backtesting tools, and 3 optional research tools. The published v1.1.0 release has 37 core tools and predates the current correctness fixes. Follow the source installation below for the behavior documented here.

Daily history supports US stock symbols and the exchange suffixes .L, .T, .TO, .AX, .HK, and .DE. Other exchange suffixes return a calendar error for history and tools that depend on it. Quote and fundamentals lookups still use the symbols accepted by Yahoo Finance. See the history coverage and freshness guide.

Quick start

Install Python 3.12 or later and uv. SQLite is included. Redis and PostgreSQL are optional.

Installation

First, clone the repository and install the core tools.

git clone https://github.com/wshobson/maverick-mcp.git
cd maverick-mcp
uv sync
cp .env.example .env

Second, add optional packages if you need backtesting or research.

uv sync --extra backtesting --extra research

Third, connect your MCP client using the command below. Replace the path with the absolute path to your checkout. A client using STDIO starts the server itself, so no separate server process is needed.

uv run --directory /absolute/path/to/maverick-mcp maverick-mcp --transport stdio

The PyPI name maverick-mcp-server belongs to an unrelated project while the name-transfer request is pending. Do not install that name from PyPI. The existing release can be run from its Git tag, but it does not include the changes described for current source.

uvx --from "git+https://github.com/wshobson/maverick-mcp@v1.1.0" maverick-mcp --transport stdio

Connect your MCP client

Choose STDIO for one local client, or Streamable HTTP for clients that share a server process. For HTTP, run make dev and use http://localhost:8003/mcp. The endpoint has no trailing slash. /mcp/ redirects and can break registration.

A client that uses a mcpServers JSON configuration can launch the local checkout with the following entry.

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/maverick-mcp",
        "maverick-mcp",
        "--transport",
        "stdio"
      ]
    }
  }
}

Configuration formats vary by client. The MCP client setup guide covers Claude Desktop, Claude Code, Codex, Cursor, VS Code, GitHub Copilot CLI, OpenCode, and other clients. Use the guide for the exact file location and keys.

HTTP binds to 127.0.0.1 by default. The server has no authentication, so keep it local unless you have configured separate access controls.

Docker with persistent data

Build the current source to include the fixes documented here. The existing published 1.1.0 image predates them.

cp .env.example .env
docker build -t maverick-mcp:local .
docker run --rm --name maverick-mcp \
  -p 127.0.0.1:8003:8000 \
  --env-file .env \
  --mount source=maverick-data,target=/data \
  maverick-mcp:local

The named volume keeps your database and cache when the container is replaced. The image stores them at /data/maverick.db and /data/maverick_cache.db and runs as user 1000. A bind-mounted directory must be writable by that user. For PostgreSQL, set DATABASE_URL explicitly. The POSTGRES_URL fallback does not override the image's DATABASE_URL default. The image includes both optional packages.

If you used an older container, back up its databases under /app before removing it. See Docker data storage and migration for backup instructions and PostgreSQL overrides. Do not mount a volume over /app, which contains the installed application.

Tools

The tables list tool names as they appear to an MCP client. Tools marked "mutates" change stored data or cache state. A portfolio entry or journal trade is a local record and does not submit an order to a broker.

Market data

Tool	Description
market_data_get_price_history	OHLCV price history for a ticker, stored locally and refreshed on access.
market_data_get_price_history_batch	Price history for multiple tickers at once.
market_data_get_quote	A single quote, cached briefly. Returns an error when Yahoo has no price for the ticker (delisted or unknown).
market_data_get_stock_fundamentals	Valuation, financials, and trading stats.
market_data_get_market_overview	Indices, sector performance, top movers, and volatility.
market_data_get_chart_links	Static external chart links for a ticker.
market_data_clear_market_cache	Clear cached quotes (mutates cache state).

Technical analysis

Tool	Description
technical_get_rsi_analysis	RSI reading and signal label.
technical_get_macd_analysis	MACD reading, signal label, and crossover state.
technical_get_support_resistance	Observed price extrema over the requested history.
technical_get_full_technical_analysis	Trend, outlook, and technical indicators.

Stock screening

Tool	Description
screening_get_bullish	Top Maverick bullish-momentum results, latest snapshot.
screening_get_bearish	Top bearish setup results, latest snapshot.
screening_get_supply_demand	Top supply/demand breakout results, latest snapshot.
screening_get_all	Latest snapshot across all three screens.
screening_get_by_criteria	Bullish results filtered by arbitrary criteria.
screening_run_screens	Recompute one screen (or all three) and persist it (mutates).

Fetch price history for the symbols you want to screen, then run a screen. A quote lookup does not add a symbol to the screening universe. A new database has no default stock universe. See the database setup guide.

Portfolio tracking, watchlists, and trade journal

Tool	Description
portfolio_add_position	Add/average into a position (mutates).
portfolio_get_my_portfolio	Portfolio snapshot with P&L using current available quotes.
portfolio_remove_position	Remove shares from a position (mutates).
portfolio_clear_portfolio	Remove every position; requires confirm=True (mutates).
portfolio_risk_adjusted_analysis	ATR-based position sizing/stop/target.
portfolio_compare_tickers	Ticker comparison, using your portfolio when tickers are omitted.
portfolio_correlation_analysis	Correlation matrix and diversification metrics.
portfolio_get_risk_dashboard	Total value, sector exposure, and risk metrics.
portfolio_check_position_risk	Pre-trade risk check for a hypothetical trade.
portfolio_get_regime_adjusted_sizing	Position size scaled by detected market regime.
portfolio_get_risk_alerts	Current sector/position/portfolio risk alerts.
portfolio_watchlist_list	List watchlist IDs and names.
portfolio_watchlist_create	Create a named watchlist (mutates).
portfolio_watchlist_add	Add a ticker to a watchlist (mutates).
portfolio_watchlist_remove	Remove a ticker from a watchlist (mutates).
portfolio_watchlist_brief	Quotes and analysis for the symbols on a watchlist.
portfolio_journal_add_trade	Log a new open trade; optional ISO entry_date records a past trade (mutates).
portfolio_journal_close_trade	Close an open trade; profit or loss calculated automatically (mutates).
portfolio_journal_list_trades	List journal trades, optionally filtered.
portfolio_journal_review	Full detail for a single journal trade.
portfolio_get_strategy_performance	Strategy performance analytics, with optional comparison.

Tools that accept an optional ticker list use your portfolio when it is omitted. See portfolio behavior and precision for details.

Python backtesting (backtesting extra)

Tool	Description
backtesting_run_backtest	Run a single-strategy backtest: metrics, trades, analysis.
backtesting_optimize_strategy	Grid-search a strategy's parameters.
backtesting_walk_forward_analysis	Rolling optimize/test windows to gauge robustness.
backtesting_monte_carlo_simulation	Bootstrap-resample trades for a return/drawdown distribution.
backtesting_compare_strategies	Backtest multiple strategies on the same symbol and rank them.
backtesting_list_strategies	List every rule-based strategy template with default parameters.
backtesting_backtest_portfolio	Backtest one strategy across multiple symbols.
backtesting_parse_strategy	Parse a natural-language description into a strategy + parameters (BYOK LLM).
backtesting_run_ml_strategy_backtest	Backtest a machine learning strategy (predictor, adaptive, ensemble, or regime).
backtesting_train_ml_predictor	Train a random-forest ML predictor for trading signals.
backtesting_analyze_market_regimes	Detect bear/sideways/bull regimes for a symbol.
backtesting_create_strategy_ensemble	Backtest a weighted ensemble of base strategies.

The extra includes 12 strategy templates plus machine learning models and strategies. Install it with uv sync --extra backtesting. Without the extra, no backtesting_* tools are registered. See the backtesting API reference for metric definitions, strategy parameters, and output limits.

Financial research (research extra)

Tool	Description
research_run_comprehensive	Web research on a financial topic, with source citations.
research_analyze_company	Company research, with source citations.
research_analyze_sentiment	Market sentiment analysis for a topic or sector.

Install with uv sync --extra research, then configure a language model and Exa or SearXNG web search. Without the extra, no research_* tools are registered. Research returns source citations and an explicit error when no usable evidence remains. See research setup and behavior.

Configuration

Use environment variables or a .env file. The .env.example file lists the supported settings.

Setting	Purpose
DATABASE_URL	Select SQLite or PostgreSQL. Local processes default to sqlite:///maverick.db; containers default to sqlite:////data/maverick.db.
REDIS_HOST	Enable Redis caching. Otherwise the server uses memory and SQLite.
LLM_PROVIDER, LLM_API_KEY, LLM_MODEL	Configure the language model for research and natural-language strategy parsing.
LLM_BASE_URL	Set a custom endpoint, required for openai_compatible.
LLM_TEMPERATURE	Optionally override the model's sampling temperature. Omit it to use the provider default.
EXA_API_KEY	Enable Exa web search for research.
RESEARCH_SEARCH_BACKEND, SEARXNG_BASE_URL	Use searxng with a server that supports JSON responses instead of Exa.

Supported model providers are anthropic, openai, openrouter, and openai_compatible. Core tools do not require a model API key. Optional research can incur charges from your model and search providers.

Usage examples

Once connected, ask your assistant to use the tools. Include the dates, symbols, and prices needed for a request.

• "Get RSI and MACD for NVDA, then show the observed price range for the last 90 days."
• "Fetch one year of AAPL and MSFT price history, then run the bullish screen."
• "List my watchlists and show my portfolio with current prices."
• "Backtest the SMA crossover strategy on SPY from January 1, 2024 to January 1, 2025."

The analyze_stock and review_portfolio prompts provide guided workflows. Installing the backtesting extra also adds run_backtest_workflow. The portfolio://my-holdings resource exposes a snapshot of your default portfolio.

Common questions

Does the Yahoo Finance connection need an API key?

No. Core market data uses yfinance. Data availability and delays depend on Yahoo Finance, and requests can fail or be rate limited. Market movers use finviz. Neither source is an execution feed for placing orders.

Why does stock screening return no results?

A new database has no stock universe. Fetch price history for your chosen tickers, then call screening_run_screens. A quote request alone does not register a ticker for screening.

Why are backtesting or research tools missing?

Install the corresponding extra in the environment your MCP client starts, then restart the server. Research also needs a configured model and search backend. See the research setup guide.

Why does an HTTP client fail to connect?

Use http://localhost:8003/mcp, without a trailing slash, and check that the server is running. A STDIO client should launch its own process instead. See client troubleshooting.

Development

Install the development tools and optional packages before running all checks.

uv sync --extra dev --extra backtesting --extra research
make test
make lint
make typecheck
make docs-check

make test excludes tests marked integration, slow, or external. See the testing guide for focused tests and integration checks. Run provider tests only with the required credentials and consent.

Read the architecture before adding a tool, and keep business logic in its domain service. The contributing guide describes the review process. Current work and remaining limitations are recorded in the documentation index and debt tracker.

Help and license

Report problems in GitHub issues with the command, version, and error message. Remove credentials and personal portfolio data from logs before sharing them. Use the security policy for vulnerability reports.

MaverickMCP uses FastMCP, yfinance, vectorbt, and LangGraph. The project is licensed under the MIT License.

Disclaimer

MaverickMCP provides educational information, not financial, investment, or tax advice. Historical backtests and technical indicators do not predict future returns. Market data and generated research can contain errors. Verify the underlying sources before using an analysis to make a decision.