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hsliuping/tradingagents-cn

★ 26,903PythonAudience · developerComplexity · 4/5MaintainedLicenseSetup · hard

TLDR

Multi-agent AI system that analyzes stocks across China, Hong Kong, and US markets by combining technical, fundamental, and sentiment analysis into research reports.

Mindmap

mindmap
  root((repo))
    What it does
      Multi-agent analysis
      Stock research reports
      China A-shares focus
    Key features
      Technical analysis agent
      Fundamental data agent
      News sentiment agent
      Risk management layer
    Tech stack
      Vue.js frontend
      FastAPI backend
      MongoDB and Redis
    Supported markets
      China A-shares
      Hong Kong stocks
      US stocks
    AI providers
      OpenAI
      Google Gemini
      Chinese LLMs
    Use cases
      Learn multi-agent AI
      Stock research
      Educational analysis

Things people build with this

USE CASE 1

Build a stock research assistant that automatically gathers technical, fundamental, and sentiment data for any ticker.

USE CASE 2

Learn how to architect multi-agent AI systems where specialized agents collaborate to solve complex problems.

USE CASE 3

Generate comprehensive stock analysis reports in Markdown, Word, or PDF format for personal investment research.

USE CASE 4

Study how to integrate multiple LLM providers (OpenAI, Gemini, Chinese models) into a single application.

Tech stack

PythonFastAPIVue.jsMongoDBRedisDockerOpenAIGoogle Gemini

Getting it running

Difficulty · hard Time to first run · 1day+

Requires Docker, MongoDB, Redis, multiple API keys (OpenAI, Google Gemini), and multi-service orchestration to run end-to-end.

Core AI analysis code is Apache 2.0 (free for any use); web application requires commercial license for business use but is free for personal and research use.

In plain English

TradingAgents-CN is a Chinese-language enhanced version of an AI-powered stock analysis platform that uses multiple AI agents working together to research and analyze stocks, specifically optimized for China's A-share market (Shanghai and Shenzhen stock exchanges), Hong Kong stocks, and US stocks. The system deploys a team of specialized AI analyst agents: one for technical chart analysis, one for fundamental financial data, one for news sentiment, and a risk management layer that synthesizes their work. Users ask questions or request analysis on a stock ticker, and the agents collectively produce a comprehensive research report, much like having a virtual team of analysts. The platform is built as a full web application with a Vue.js front-end (the visual interface) and a FastAPI back-end (the server logic), connected to MongoDB and Redis databases for caching and storing results. It supports multiple AI providers including OpenAI, Google Gemini, and Chinese LLM providers, with Docker-based deployment for easy setup. Reports can be exported as Markdown, Word, or PDF. Important licensing note: the core AI analysis code is open-source (Apache 2.0), but the web application front-end and back-end are proprietary and require commercial licensing for business use. Personal and research use is free. For a non-technical founder: this is a research and learning platform for studying how to apply multi-agent AI systems to stock analysis. It is not intended for live trading execution, and its README emphasizes it is for educational purposes only, not investment advice.

Copy-paste prompts

Prompt 1
How do I set up TradingAgents-CN locally with Docker to analyze stocks on the China A-share market?
Prompt 2
Show me how the technical analysis agent and fundamental data agent work together in this codebase to produce a stock report.
Prompt 3
How can I add a new AI provider or customize the analysis agents to focus on specific metrics I care about?
Prompt 4
What's the best way to export stock analysis reports from this system as PDF or Word documents?
Prompt 5
How do I configure this to work with my own OpenAI API key or switch to a Chinese LLM provider?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.