Run multi-agent LLM financial trading analysis
A framework of multiple LLM agents that collaborate on financial trading decisions.
What they did
You install the repo, set an API key for an LLM provider, and pick a ticker and date in the CLI or through the Python TradingAgentsGraph call. Analyst agents cover fundamentals, sentiment, news and technicals. Bullish and bearish researchers then debate, a trader proposes a decision, and risk managers review it. The Portfolio Manager approves or rejects, and approved orders go to a simulated exchange.
How it went
The source reports no benchmark results. It says outputs vary between runs and backtest returns may not match published figures, so the framework is a research scaffold and not a strategy with a fixed return.
Worth knowing
Runs aren't reproducible, because LLM sampling and live news and social data change. Reasoning models ignore the temperature setting, so use a non-reasoning model for steadier output.
Labels
Runs aren't reproducible, because LLM sampling and live news and social data change.
Analyst agents cover fundamentals, sentiment, news and technicals. Bullish and bearish researchers then debate, a trader proposes a decision, and risk managers review it.
The “Material” label failed its quality check and is not used for selection.
Details
- Agents
- custom-built or unnamed agent
- Source
- Hacker News
- Source date
- Sep 8, 2026
- Accounts
- 1
- In registry since
- Oct 12, 2026