QuantConnect

Demo – Claude

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◈ Is this crazy?

Maybe? Either way, the world is changing and this is where we're at. Out of the box, QuantConnect MCP provides you with:

• Full Project Lifecycle: Create, read, update, compile, and manage QuantConnect projects and files programmatically.
• End-to-End Backtesting: Compile projects, create backtests, read detailed results, and analyze charts, orders, and insights.
• Live Trading Management: Deploy, monitor, liquidate, and control live algorithms with comprehensive runtime statistics and logging.
• Historical Data Access: Comprehensive data retrieval capabilities for historical and alternative data analysis.
• Advanced Analytics: Perform Principal Component Analysis (PCA), Engle-Granger cointegration tests, mean-reversion analysis, and correlation studies.
• Portfolio Optimization: Utilize sophisticated sparse optimization with Huber Downward Risk minimization, calculate performance, and benchmark strategies.
• Universe Selection: Dynamically screen assets by multiple criteria, analyze ETF constituents, and select assets based on correlation.
• Enterprise-Grade Security: Secure, SHA-256 authenticated API integration with QuantConnect.
• High-Performance Core: Built with an async-first design for concurrent data processing and responsiveness.
• AI-Native Interface: Designed for seamless interaction via natural language in advanced AI clients.

◉ Table of Contents

• ◈ Quick Start
• ◈ Authentication
• ◈ Natural Language Examples
• ◈ Comprehensive API Reference
• ◈ Architecture
• ◈ Advanced Configuration
• ◈ Testing
• ◈ Contributing
• ◈ License

◈ Quick Start

Get up and running in under 2 minutes:

Prerequisites: You must have QuantConnect credentials (User ID and API Token) before running the server. The server will not function without proper authentication. See Authentication section for details on obtaining these credentials.

Install with uvx (Recommended)

# Install and run directly from PyPI - no cloning required!
uvx quantconnect-mcp

# Or install with uv/pip
uv pip install quantconnect-mcp
pip install quantconnect-mcp

One-Click Claude Desktop Install (Recommended)

1. Download: Grab the latest quantconnect-mcp.dxt from the “Releases” page
2. Install: Double-click the file – Claude Desktop opens and prompts you to Install
3. Configure: In Claude Desktop → Settings → Extensions → QuantConnect MCP, paste your user ID and API token
4. Use it: Start a new Claude chat and call any QuantConnect tool

Why DXT?

Desktop Extensions (.dxt) bundle the server, dependencies, and manifest so users go from download → working MCP in one click – no terminal, no JSON editing, no version conflicts.

2. Set Up QuantConnect Credentials (Required)

The server requires these environment variables to function properly:

export QUANTCONNECT_USER_ID="your_user_id"        # Required
export QUANTCONNECT_API_TOKEN="your_api_token"    # Required
export QUANTCONNECT_ORGANIZATION_ID="your_org_id" # Optional

3. Launch the Server

# STDIO transport (default) - Recommended for MCP clients
uvx quantconnect-mcp

# HTTP transport
MCP_TRANSPORT=streamable-http MCP_PORT=8000 uvx quantconnect-mcp

4. Interact with Natural Language

Instead of calling tools programmatically, you use natural language with a connected AI client (like Claude, a GPT, or any other MCP-compatible interface).

"Add GOOGL, AMZN, and MSFT, then run a PCA analysis on them for 2023."

◈ Authentication

Getting Your Credentials

Credential	Where to Find	Required
User ID	Email received when signing up	◉ Yes
API Token	QuantConnect Settings	◉ Yes
Organization ID	Organization URL: /organization/{ID}	◦ Optional

Configuration Methods

Method 1: Environment Variables (Recommended)

# Add to your .bashrc, .zshrc, or .env file
export QUANTCONNECT_USER_ID="123456"
export QUANTCONNECT_API_TOKEN="your_secure_token_here"
export QUANTCONNECT_ORGANIZATION_ID="your_org_id"  # Optional

Demo – Roo Code

◈ Natural Language Examples

This MCP server is designed to be used with natural language. Below are examples of how you can instruct an AI assistant to perform complex financial analysis tasks.

Factor‑Driven Portfolio Construction Pipeline

“Build a global equity long/short portfolio for 2025:

1. Pull the constituents of QQQ, SPY, and EEM as of 2024‑12‑31 (survivor‑bias free).
2. For each symbol, calculate Fama‑French 5‑factor and quality‑minus‑junk loadings using daily data 2022‑01‑01 → 2024‑12‑31.
3. Rank stocks into terciles on value (B/M) and momentum (12‑1); go long top tercile, short bottom, beta‑neutral to the S&P 500.
4. Within each book, apply Hierarchical Risk Parity (HRP) for position sizing, capped at 5 % gross exposure per leg.
5. Target annualised ex‑ante volatility ≤ 10 %; solve with CVaR minimisation under a 95 % confidence level.
6. Benchmark against MSCI World; report annualised return, vol, Sharpe, Sortino, max DD, hit‑rate, turnover for the period 2023‑01‑01 → 2024‑12‑31.
7. Export the optimal weights and full tear‑sheet as pdf + csv.
8. Schedule a monthly rebalance job and push signals to the live trading endpoint.”

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Robust Statistical‑Arbitrage Workflow

“Test and refine a pairs‑trading idea: • Universe: US Staples sector, market cap > $5 B, price > $10. • Data: 15‑minute bars, 2023‑01‑02 → 2025‑06‑30. • Step 1 – For all pairs, calculate rolling 60‑day distance correlation; keep pairs with dCor ≥ 0.80. • Step 2 – Run Johansen cointegration (lag = 2) on the survivors; retain pairs with trace‑stat < 5 % critical value. • Step 3 – For each cointegrated pair:    – Estimate half‑life of mean‑reversion; discard if > 7 days.    – Compute Hurst exponent; require H < 0.4. • Step 4 – Simulate a Bayesian Kalman‑filter spread to allow time‑varying hedge ratios. • Entry: z‑score crosses ±2 (two‑bar confirmation); Exit: z = 0 or t_max = 3 × half‑life. • Risk: cap pair notional at 3 % NAV, portfolio gross leverage ≤ 3 ×, stop‑loss at z = 4. • Output: trade log, PnL attribution, bootstrapped p‑value of alpha, and Likelihood‑Ratio test for regime shifts.”

Automated Project, Backtest & Hyper‑Parameter Sweep

“Spin up an experiment suite in QuantConnect:

1. Create project ‘DynamicPairs_Kalman’ (Python).
2. Add files:    • alpha.py – signal generation (placeholder)    • risk.py – custom position sizing    • config.yaml – parameter grid:        yaml        entry_z: [1.5, 2.0, 2.5]        lookback: [30, 60, 90]        hedge: ['OLS', 'Kalman']        
3. Trigger a parameter‑sweep backtest labelled ‘GridSearch‑v1’ using in‑sample 2022‑23.
4. When jobs finish, rank runs by Information Ratio and max DD < 10 %; persist top‑3 configs.
5. Automatically launch out‑of‑sample backtests 2024‑YTD for the winners.
6. Produce an executive summary: tables + charts (equity curve, rolling Sharpe, exposure histogram).
7. Package the best model as a Docker image, push to registry, and deploy to the live‑trading cluster with a kill‑switch if 1‑day loss > 3 σ.”

Statistical Analysis Workflow

"Are Coca-Cola (KO) and Pepsi (PEP) cointegrated? Run the test for the period from 2023 to 2024. If they are, analyze their mean-reversion properties with a 20-day lookback."

Project and Backtest Management

"I need to manage my QuantConnect projects. First, create a new Python project named 'My_Awesome_Strategy'. Then, create a file inside it called 'main.py' and add this code: ...your algorithm code here.... After that, compile it and run a backtest named 'Initial Run'. When it's done, show me the performance resu

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README truncated. View full README on GitHub.