{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "tauricresearch-tradingagents--run-a-multi-agent-analysis-of-one-stock",
 "repo": "TauricResearch/TradingAgents",
 "repo_url": "https://github.com/TauricResearch/TradingAgents",
 "repo_slug": "tauricresearch-tradingagents",
 "title": "Run a multi-agent research debate on one stock",
 "page_title": "How to run a multi-agent research debate on one stock with Trading Agents",
 "summary": "Pick a ticker and date in the CLI and watch analysts, researchers, a trader and a risk team argue before a decision.",
 "who": "A developer or student who wants to see how LLM agents weigh fundamentals, news, sentiment and technicals for one stock.",
 "problem": "One chatbot answer about a stock hides its reasoning and never argues with itself.",
 "outcome": "A 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.",
 "steps": [
  "Clone the repo, create a Python 3.11 or later environment and run `pip install .`",
  "Run `cp .env.example .env` and 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"
 ],
 "agent_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.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "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.",
 "category": "Money",
 "tags": [
  "Money"
 ],
 "lang": "python",
 "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)"
 ],
 "risk": "sensitive",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "credentials",
   "notice": "You store LLM and data API keys in .env, so keep that file out of version control."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/tauricresearch-tradingagents/run-a-multi-agent-analysis-of-one-stock/",
 "md": "https://dedupedai.com/use-cases/tauricresearch-tradingagents/run-a-multi-agent-analysis-of-one-stock/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/tauricresearch-tradingagents--run-a-multi-agent-analysis-of-one-stock.json",
 "api_version": "v1"
}