Credentials. You store LLM and data API keys in .env, so keep that file out of version control.
Who it is forA developer or student who wants to see how LLM agents weigh fundamentals, news, sentiment and technicals for one stock.
The problemOne chatbot answer about a stock hides its reasoning and never argues with itself.
The outcomeA saved report with each analyst's findings, the bull and bear debate, the trader's proposal and the portfolio manager's approve or reject call.
What you need first
- 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)
Steps
- Clone the repo, create a Python 3.11 or later environment and run
pip install . - Run
cp .env.example .envand add the API key for your chosen LLM provider - Launch the interactive CLI with
tradingagents - Select your tickers, analysis date, LLM provider and research depth on the setup screen
- Read the analyst reports, the researcher debate and the final decision; reports are saved as one HTML page
Prompt for your agent
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.
Caveat. Results vary with the model, temperature, period and data quality, and orders only go to a simulated exchange. Not financial advice: paper trade first, and remember you can lose money.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for TauricResearch/TradingAgents and "Run a multi-agent research debate on one stock"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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