FinLab AI
Provides access to FinLab's quantitative trading documentation including 900+ data columns, backtesting APIs, and 60+ strategy examples for developing trading algorithms.
Quantitative trading toolkit with extensive data access, backtesting, and strategy development tools.
What it does
- Browse FinLab trading documentation
- Search through quantitative finance references
- Access 60+ complete strategy examples
- Get backtesting API documentation
- Explore 900+ financial data columns
- Find machine learning trading patterns
Best for
About FinLab AI
FinLab AI is a community-built MCP server published by koreal6803 that provides AI assistants with tools and capabilities via the Model Context Protocol. FinLab AI — quantitative trading toolkit with extensive data access, backtesting platform, and algorithmic trading tools It is categorized under finance, ai ml. This server exposes 4 tools that AI clients can invoke during conversations and coding sessions.
How to install
You can install FinLab AI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
License
FinLab AI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Tools (4)
List all available FinLab documentation files
Get the full content of a FinLab documentation file
Search for a keyword or phrase in all FinLab documentation
Get factor/strategy examples from the documentation
FinLab AI
Let AI discover your next alpha.
Installation
curl -sSf https://ai.finlab.finance/install.sh | sh
Auto-detects your CLI (Claude Code / Codex / Gemini), installs uv if needed, and sets up the skill.
Documentation
| Document | Content |
|---|---|
| Data Reference | 900+ columns across 80+ tables |
| Backtesting Reference | sim() API, resampling, metrics |
| Factor Examples | 60+ complete strategy examples |
| Best Practices | Patterns, anti-patterns, tips |
| ML Reference | Feature engineering, labels |
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