# Known Use Cases (KUC)

Total: **14**

## `KUC-101`
**Source**: `finrobot_equity/core/src/Run.ipynb`

Investors need comprehensive equity research reports that combine financial statement analysis, peer comparisons, and recent news to make informed investment decisions on specific companies.

## `KUC-102`
**Source**: `tutorials_advanced/agent_annual_report.ipynb`

Financial analysts require automated generation of customized financial analysis reports that can interact with clients, gather requirements, and produce professional-grade documents.

## `KUC-103`
**Source**: `tutorials_advanced/agent_fingpt_forecaster.ipynb`

Traders and investors need AI-powered market analysis that combines company profiles, financial data, and news to generate short-term price movement predictions.

## `KUC-104`
**Source**: `tutorials_advanced/agent_openbb.ipynb`

Users need an intelligent agent interface to access OpenBB's comprehensive financial data capabilities including market data, fundamentals, and technical analysis.

## `KUC-105`
**Source**: `tutorials_advanced/agent_trade_strategist.ipynb`

Algorithmic traders need automated assistance to develop, code, and backtest custom trading strategies using the BackTrader framework with logging for further analysis.

## `KUC-106`
**Source**: `tutorials_advanced/lmm_agent_mplfinance.ipynb`

Analysts need vision-enabled AI agents that can analyze financial charts and market visualizations alongside textual market news for comprehensive analysis.

## `KUC-107`
**Source**: `tutorials_advanced/lmm_agent_opt_smacross.ipynb`

Quantitative traders need AI-assisted optimization of Simple Moving Average crossover strategies by visually inspecting charts and iteratively refining parameters.

## `KUC-108`
**Source**: `tutorials_beginner/agent_annual_report.ipynb`

Beginners need a simple way to generate formatted PDF annual reports from SEC 10-K filings with appropriate length and professional presentation.

## `KUC-109`
**Source**: `tutorials_beginner/agent_fingpt_forecaster.ipynb`

Beginners need easy stock price movement predictions based on company news and available financial information for basic investment decision support.

## `KUC-110`
**Source**: `tutorials_beginner/agent_rag_earnings_call_sec_filings.ipynb`

Analysts need to query and analyze large collections of earnings call transcripts and SEC filings using retrieval augmented generation for insights.

## `KUC-111`
**Source**: `tutorials_beginner/agent_rag_qa.ipynb`

Users need to ask questions and get answers from annual report documents using RAG technology to quickly extract specific financial information.

## `KUC-112`
**Source**: `tutorials_beginner/agent_rag_qa_up.ipynb`

Analysts need to query across multiple financial document sources including earnings calls and SEC filings simultaneously to get comprehensive answers.

## `KUC-113`
**Source**: `tutorials_beginner/ollama function call.ipynb`

Users with privacy requirements or local infrastructure need to use local LLMs (Ollama) to call financial data functions for stock information retrieval.

## `KUC-114`
**Source**: `tutorials_beginner/ollama stock chart.ipynb`

Users need to generate stock price charts using local LLM infrastructure with yfinance data, avoiding cloud dependencies for visualization.