Money, Bills & Benefits
Summarize an insurance denial letter for an appeal
The poster used an AI assistant to summarize a denial PDF to draft an appeal, but it invented an appeal deadline.
Other or custom
Run multi-agent LLM financial trading analysis
A framework of multiple LLM agents that collaborate on financial trading decisions.
Time saved
Not reported
Setup time
Not reported
Running cost
Not reported
Cost range
Not reported
Use case overview
A framework of multiple LLM agents that collaborate on financial trading decisions.
Reported experience
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.
Example prompts
Set up a small multi-agent research project in this folder where separate LLM agents act as a fundamentals analyst, a news analyst, a technical analyst and a risk manager for [ticker or list of tickers]. Have them debate and produce one written recommendation per ticker with reasoning, using only [free data source, e.g. yfinance]. This is paper analysis only, so never connect to a brokerage or place trades, and stop once you have run it on one ticker and saved the report.
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