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Vibe-Trading

An open-source research workspace that turns finance questions into runnable analysis, backtests and multi-agent reviews

Finance & Market AgentsOpen source
Latest
v0.1.11
Updated
10 Jul 2026
Language
Python
License
MIT
Coverage
1 story
$pip install vibe-trading-ai

What's new

v0.1.1110 Jul 2026

Adds NSE and BSE as first-class backtest markets, a point-in-time fundamentals layer that grows the Alpha Zoo to 460 factors, and research delivery across 16 messaging channels.

Latest news

IN SHORT

What is Vibe-Trading?

Vibe-Trading is an open-source research workspace from HKUDS that connects natural-language prompts to market-data loaders, strategy generation, backtest engines, reports, exports and persistent research memory. It sits in the Finance & Market Agents category of the Agents & Automation tools and is open-source, written in Python and licensed MIT. To get started: install from PyPI.

Updated Markdown version ↗

Overview

Vibe-Trading is an open-source research workspace from HKUDS that connects natural-language prompts to market-data loaders, strategy generation, backtest engines, reports, exports and persistent research memory. You ask a finance question in plain language and it answers by running real tools against real data, leaving behind strategy code, metrics, validation artifacts and run cards rather than a chat transcript.

The project is built for research, simulation and backtesting. It can also trade autonomously through a broker you authorise yourself, but it holds no funds, never trades outside the limits you set, and can be halted instantly. Broker credentials go to the OS keyring rather than config files, and every run writes a hash manifest over the prompt, skills, tool registry and package versions plus a hash-chained audit ledger, so you can answer "what methodology produced that number?" after the fact.

Beyond the single agent, Vibe-Trading runs multi-agent analyst teams — investment, quant, crypto, macro and risk workflows — with streaming progress and persisted reports. It reaches markets across A-share, Hong Kong, US, Canada, UK, India and Korea equities plus crypto, futures and forex, ships a pre-built alpha zoo you can bench in one line, and exposes the same runtime through a CLI, a FastAPI web UI, a REST API and an MCP server that plugs into Claude Desktop, Cursor and other MCP clients.

What it does

  • Natural-language market research with tool calls, document and chart reading, and reusable session context
  • Multi-agent trading teams for investment, quant, crypto, macro and risk reviews, with streaming progress and persisted reports
  • Cross-market backtesting across A-share, HK, US, Canada, UK, India and Korea equities plus crypto, futures and forex, with point-in-time data, validation artifacts and run cards
  • A pre-built alpha zoo benched in one command, categorising alphas as alive, reversed or dead on your own universe
  • Broker connectors kept read-only or paper by default, with credentials in the OS keyring, a committed mandate, an order guard and an instant halt
  • Governance in every run: a hash manifest over prompt, skills, tool registry and package versions, plus a hash-chained audit ledger
  • One runtime behind four transports — interactive CLI/TUI, FastAPI web UI, REST API, and an MCP server for Claude Desktop, Cursor and other clients
  • Exportable artifacts including reports, TradingView Pine Script, TDX and MetaTrader 5

Getting started

Vibe-Trading is on PyPI as `vibe-trading-ai` and needs Python 3.11+ and an LLM API key from any supported provider — or a local Ollama install, which needs no key.

Install from PyPI

One pip install gives you three commands: `vibe-trading` (CLI/TUI), `vibe-trading serve` (web server) and `vibe-trading-mcp` (MCP server).

bashbash
pip install vibe-trading-ai

Configure a provider

`init` walks you through an interactive .env setup. Supported providers include OpenRouter, OpenAI, Anthropic, DeepSeek, Gemini, Groq, DashScope/Qwen, Zhipu, Moonshot/Kimi, MiniMax and local Ollama.

bashbash
vibe-trading init

Run a first research task

Pass a prompt with -p and the agent fetches data, writes and runs the backtest, and summarises the result.

bashbash
vibe-trading run -p "Backtest a BTC-USDT 20/50 moving-average strategy for 2024 and summarize return and drawdown"

Bench the alpha zoo

Score a whole family of pre-built quant alphas against your universe and period in one line.

bashbash
vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025 --top 20

Launch the web UI or the MCP server

`serve` starts the FastAPI web UI; `vibe-trading-mcp` starts the MCP server over stdio so Claude Desktop, Cursor or another MCP client can call the same tools.

bashbash
vibe-trading serve --port 8899
vibe-trading-mcp

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Ask a market question in plain language and get an answer backed by fetched data, tool calls and a reproducible run record
  • Prototype and backtest a strategy idea across several markets without wiring up data loaders and an engine yourself
  • Run a team of analyst agents over the same thesis to get investment, quant and risk views side by side
  • Give an existing MCP client — Claude Desktop, Cursor, OpenClaw — a set of read-only market-data and quant tools

How Vibe-Trading compares

Vibe-Trading alongside other open-source finance & market agents tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
TradingAgents★ 120kLangGraph framework that mirrors a trading firm: LLM analyst, bull/bear researcher, trader and risk-management agents debate before a decision. For research, not advice.
Daily Stock Analysis★ 67.5kLLM research pipeline for A-share, Hong Kong, US, Japanese, Korean and Taiwanese tickers: pulls quotes, indicators and news, writes a decision dashboard and pushes it to chat or email.
Vibe-Trading★ 37.5kAn open-source research workspace that turns finance questions into runnable analysis, backtests and multi-agent reviews
AI Berkshire★ 17kValue-investing research skills for Claude Code and Codex that turn the methods of Buffett, Munger, Duan Yongping and Li Lu into structured, multi-agent research workflows.
QuantDinger★ 13.4kA self-hosted trading stack that carries an idea from AI market research to a Python strategy, a server-side backtest and paper or live execution on crypto exchanges and brokers, driven from the web, an API or MCP.
a-stock-data★ 11.4kA single-file agent skill that gives AI coding assistants China A-share market data (quotes, research, fund flow, filings, macro, futures and more) from 34 mostly keyless sources.
ValueCell★ 11kA multi-agent platform for financial work, pairing deep-research, news and strategy agents over US, Hong Kong, China and crypto market data, with credentials kept on the local device.
FinRobot★ 8.4kMulti-agent equity-research platform where DCF, WACC and Monte Carlo figures are computed by deterministic Python operators and the LLM only writes the narrative.