hsliuping/tradingagents-cn

Analysis updated 2026-06-21

★ 25,772PythonAudience · researcherComplexity · 4/5LicenseSetup · moderate

Mindmap

mindmap
  root((TradingAgents-CN))
    What it does
      AI stock analysis
      Multi-agent research
      Report generation
    Agent types
      Technical analysis
      Fundamental data
      News sentiment
      Risk management
    Tech stack
      Python and FastAPI
      Vue.js frontend
      MongoDB and Redis
    Markets
      China A-shares
      Hong Kong stocks
      US stocks

mindmap root((TradingAgents-CN)) What it does AI stock analysis Multi-agent research Report generation Agent types Technical analysis Fundamental data News sentiment Risk management Tech stack Python and FastAPI Vue.js frontend MongoDB and Redis Markets China A-shares Hong Kong stocks US stocks

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What do people build with it?

USE CASE 1

Research how to build a multi-agent AI system where specialized agents collaborate to analyze financial data and produce a report.

USE CASE 2

Study the architecture for combining technical, fundamental, and sentiment analysis using AI agents in a single pipeline.

USE CASE 3

Use the platform to generate comprehensive AI-written research reports on Chinese A-share or US stocks for educational purposes.

USE CASE 4

Learn how to integrate multiple LLM providers (OpenAI, Gemini, Chinese LLMs) into a single AI application with a web front-end.

What is it built with?

PythonVue.jsFastAPIMongoDBRedisDocker

How does it compare?

	hsliuping/tradingagents-cn	mlflow/mlflow	getzep/graphiti
Stars	25,772	25,771	25,764
Language	Python	Python	Python
Setup difficulty	moderate	easy	moderate
Complexity	4/5	3/5	3/5
Audience	researcher	developer	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 1h+

Requires Docker, API keys for at least one LLM provider, and MongoDB/Redis setup, Chinese market data sources may need additional configuration.

Core AI code is Apache 2.0 (free for personal and research use), but the web app front-end and back-end require a commercial license for business 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

Using the TradingAgents-CN architecture as a reference, show me how to set up multiple AI agents in Python where one does technical analysis and another does sentiment analysis, then combines their outputs.

Prompt 2

How does TradingAgents-CN coordinate its analyst agents using FastAPI? Show me the multi-agent orchestration pattern it uses.

Prompt 3

Help me set up a Docker environment for TradingAgents-CN so I can run the full platform locally for research purposes.

Prompt 4

Show me how TradingAgents-CN integrates with OpenAI and Chinese LLM providers so I can switch between them in my own multi-agent app.

Frequently asked questions

What is tradingagents-cn?

TradingAgents-CN is an AI-powered stock analysis platform where multiple AI agents, covering technical charts, fundamentals, and news sentiment, collaborate to produce research reports on Chinese, Hong Kong, and US stocks.

What language is tradingagents-cn written in?

Mainly Python. The stack also includes Python, Vue.js, FastAPI.

What license does tradingagents-cn use?

Core AI code is Apache 2.0 (free for personal and research use), but the web app front-end and back-end require a commercial license for business use.

How hard is tradingagents-cn to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is tradingagents-cn for?

Mainly researcher.

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