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How to use Trading Agents: what you can do with it

What is Trading Agents? TradingAgents is an open-source multi-agent trading research framework that mirrors a trading firm. LLM agents play fundamentals, sentiment, news and technical analysts, bullish and bearish researchers who debate, a trader, a risk management team and a portfolio manager who approves or rejects each proposal, which then runs on a simulated exchange. The README says it is built for research and is not financial, investment or trading advice. Not financial advice: paper trade first, and remember you can lose money.

How do I use it? Clone the repo, create a Python 3.11 or later environment and run pip install; then run cp .env.example .env and add the API key for your chosen LLM provider; launch the interactive CLI with tradingagents. Full steps below.

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What do you want to do?

3 use cases

  • Run a multi-agent research debate on one stock

    Pick a ticker and date in the CLI and watch analysts, researchers, a trader and a risk team argue before a decision.

    View prompt
    Install TauricResearch/TradingAgents, run the tradingagents CLI for <ticker> on <date> with <provider>, and give me the final decision plus the two strongest bull and bear arguments from the debate. Confirm the run only used data available on that date.

  • Run Trading Agents on local models with Ollama

    Use the Docker Ollama profile or any OpenAI-compatible local server so no API key is needed.

    View prompt
    Set up TauricResearch/TradingAgents to use <local model> through Ollama (or an OpenAI-compatible server at <url>), run one analysis of <ticker>, and tell me how long it took and whether any agent step failed.

  • Run Trading Agents on your ChatGPT Plus or Pro plan

    Sign in once with your ChatGPT plan instead of paying for a separate API key.

    View prompt
    Configure TauricResearch/TradingAgents to use my ChatGPT plan with tradingagents login chatgpt, run one analysis of <ticker> on <date>, and tell me where the sign-in file is stored so I can revoke it later.

Quick start with Trading Agents

  1. Check you have what it needs: Python 3.11 or later, or Docker; An API key for one supported LLM provider, a ChatGPT Plus or Pro plan, or local models through Ollama; Optional data keys such as Alpha Vantage and FRED (free).
  2. Open TauricResearch/TradingAgents on GitHub and read the README and license.
  3. Clone the repo, create a Python 3.11 or later environment and run pip install .
  4. Run cp .env.example .env and add the API key for your chosen LLM provider
  5. Launch the interactive CLI with tradingagents
  6. Select your tickers, analysis date, LLM provider and research depth on the setup screen

These steps come from our top use case, Run a multi-agent research debate on one stock, which also has a copy-paste agent prompt.

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Trading Agents alternatives · Best AI repos for Money

About this repo

  • Who it is forDevelopers, students and quant hobbyists who want to study how a team of LLM agents reasons about a stock before a trade, on a simulated exchange.
  • Why it is hot114k stars, talked about in 2 AI sources we track
  • LanguagePython
  • Sourcegithub.com/TauricResearch/TradingAgents · Back to Hot repos

Requirements

  • Python 3.11 or later, or Docker
  • An API key for one supported LLM provider, a ChatGPT Plus or Pro plan, or local models through Ollama
  • Optional data keys such as Alpha Vantage and FRED (free)

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Questions about Trading Agents

What is Trading Agents?

TradingAgents is an open-source multi-agent trading research framework that mirrors a trading firm. LLM agents play fundamentals, sentiment, news and technical analysts, bullish and bearish researchers who debate, a trader, a risk management team and a portfolio manager who approves or rejects each proposal, which then runs on a simulated exchange. The README says it is built for research and is not financial, investment or trading advice. Not financial advice: paper trade first, and remember you can lose money. It is an open-source project on GitHub: https://github.com/TauricResearch/TradingAgents.

How do I use Trading Agents?

Clone the repo, create a Python 3.11 or later environment and run pip install; then run cp .env.example .env and add the API key for your chosen LLM provider; launch the interactive CLI with tradingagents. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Trading Agents for?

Developers, students and quant hobbyists who want to study how a team of LLM agents reasons about a stock before a trade, on a simulated exchange.

What can I do with Trading Agents?

3 use cases so far, for example: Run a multi-agent research debate on one stock; Run Trading Agents on local models with Ollama; Run Trading Agents on your ChatGPT Plus or Pro plan. Each has steps and a copy-paste agent prompt.

What do I need to run Trading Agents?

Python 3.11 or later, or Docker; An API key for one supported LLM provider, a ChatGPT Plus or Pro plan, or local models through Ollama; Optional data keys such as Alpha Vantage and FRED (free).

What are alternatives to Trading Agents?

Other hot AI repos in Money: Fincept Terminal, Freqtrade, Vibe Trading, Day Trader Skill, Tick Stock Panel.

Is Trading Agents safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.