Vibe-Trading
An open-source AI trading agent you run yourself, for research, backtesting and paper trading.
- Stars
- 37.4k
- Forks
- 6k
- Licence
- MIT
- Language
- Python
- Last update
- 5 days ago
- Type
- AI model / agent
What it is
Vibe-Trading is a Python project that lets you use an AI agent for trading-related work, such as research and backtesting. The README describes it as a personal trading agent that you install with a single pip command and then connect to your own tools. Its topics include backtesting, multi-agent systems, quantitative finance and MCP, and the README mentions paper trading and portfolio connections to brokers like IBKR and Futu.
- Ask the agent to research a market question and produce an analysis
- Run backtests on trading strategies
- Use paper trading to simulate orders without real money
- Expose trading capabilities to another agent through the API or MCP server
Great for
- Developers who want an AI agent for quantitative trading research
- People who want to test trading strategies with backtesting before risking money
- Students learning quantitative finance with a hands-on tool
- Users who already run an LLM-based agent and want trading tools through the API or MCP
Not ideal for
- People looking for a ready-made system that makes profitable trades; the README does not claim performance and this is research software
- Anyone who needs a polished no-code app, since the README centers on a command-line install and Python setup
- Users who want to connect a wallet or buy tokens; the README warns that related token and memecoin claims are not official
How to get it
- 1
Install with a package manager
You'll need: Python 3.11+
Install from PyPI
pip install vibe-trading-ai - 2
Run it
- Install the package with pip using the command above.
- Follow the Quick Start section of the README to launch the agent.
- Explore the Examples section of the README for sample tasks.
- 3
Get the most out of it
- Use the API / MCP section of the README to connect the agent to other tools.
- Configure broker gateway endpoints for IBKR or Futu if you use those accounts, as described in the news section.
- Try paper trading before any live use, since the README describes it as a simulation feature.
Downloads
Strengths
- Open source under the MIT license
- Actively maintained, with frequent commits listed in the README news section
- Covers several areas in one project: research, backtesting, paper trading and API/MCP access
Trade-offs
- The README is truncated and does not fully explain every feature, so details may be incomplete
- Many recent changes are marked unreleased and listed as fixes, so behavior may shift between versions
- Trading software carries financial risk, and the README does not provide investment guidance
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| Vibe-Trading (this) | 37.4k | AI model / agent | intermediate | 5 days ago | Developers who want an AI agent for quantitative trading research |
| FinRL | 17k | Library | intermediate | 12 days ago | Students learning how reinforcement learning is applied to financial markets |
| qlib | 49.9k | Library | advanced | 8 days ago | Quant researchers who want a structured ML workflow for stock data |
| Codewhale | 41.2k | CLI | intermediate | 4 days ago | Developers who want an AI agent working inside a terminal and a git repository |
| ai-berkshire | 17k | AI model / agent | intermediate | 5 days ago | Chinese-speaking investors who want a structured value investing checklist applied to specific companies |
| paperclip | 110k | App | advanced | 4 days ago | Developers already running several AI coding agents who want one place to track them |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.