VYNN AI Financial Analyst

What does this agent do, and when should you use it?

This is the personal financial analysis agent behind VYNN AI, with a Python 3.11 command-line entry point at main.py. src/agents/generalist_agent.py runs a reasoning loop that selects tools for the question; when full research is needed, it gathers financials, builds a ten-tab Excel model while screening news in parallel, then writes a cited report. Code computes the model figures, a deterministic calculator sets the rating and target, and a validator checks numbers and requires at least 95% of material sentences to cite evidence from the run. Outputs include a chat answer, research report, workbook, and run data; Docker is also documented. The README documents OpenAI and Anthropic model interfaces, but written permission from VYNN AI is required to use, copy, modify, or distribute the source.

Accepts natural-language questions through --pipeline chat, with generalist_agent.py selecting tools. Quick questions can query prices, technicals, options, portfolio risk, macro data, funds, crypto, or prediction markets. Full research calls get_financials, build_model, analyze_news, and write_report to produce a ten-tab Excel model and cited report; the deterministic calculator and validator in src/recommendation_* set the rating and target and check figures, while src/confidence_alert.py flags large gaps between the model value and analyst targets. Runs write models, reports, answers, and logs under data/<email>/<TICKER>/<timestamp>/. An OpenAI API key is required by default, with Anthropic available as an alternative provider.

1. An individual investor considering whether to research a company further can run a full financial analysis and inspect its valuation model and cited report.
2. A user seeking company research in Chinese can ask in Chinese and receive a report in Chinese.
3. An investor assessing how interest-rate changes may affect US banks can ask about macro data for a quick answer.
4. A technical trader seeking levels for NVDA or AAPL can request technical analysis.
5. An options or portfolio researcher can request Black-Scholes pricing, risk metrics, or max-Sharpe portfolio weights.
6. A user tracking market-implied Fed-cut odds or Bitcoin can query prediction markets or a crypto snapshot.

How do you install or deploy this agent?

Requires Python 3.11 and an OpenAI API key. Docker deployment is also documented. Written permission from VYNN AI is required to use, copy, modify, or distribute the source.

git clone https://github.com/Agentic-Analyst/stock-analyst.git
cd stock-analyst
python3.11 -m venv .venv && source .venv/bin/activate
pip install -r requirements.lock
cp .env.example .env          # set OPENAI_API_KEY

Docker deployment:

docker build --target production -t stock-analyst .
docker run --rm --env-file .env -v "$(pwd)/data:/data" stock-analyst \
  --pipeline chat --email [email protected] --timestamp 20260101_120000 --user-prompt "Analyze NVDA"

How do you use this agent?

Set OPENAI_API_KEY in .env, then submit a question through the chat pipeline:

python main.py --pipeline chat --email [email protected] --timestamp 20260101_120000 \
  --user-prompt "Analyze NVDA. Should I buy?"

Continue a conversation with --session-id. For scripted runs, use --ticker with a pipeline such as comprehensive, financial-model, or search-news. Set ANTHROPIC_API_KEY to use Claude models; FRED_API_KEY enables get_macro, while SERPAPI_API_KEY can improve news discovery.

What are this agent's strengths and limitations?

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