Most financial tools give you data.
But investors don't make decisions using raw numbers alone — they interpret them through a framework.
Warren Buffett thinks about moat and intrinsic value.
Ray Dalio thinks about macro cycles.
Charlie Munger thinks about avoiding mistakes.
So I asked a simple question:
What if an AI could apply these investment philosophies automatically?
That idea became Wallstreet-AI, an open-source agentic financial analysis system that combines structured data pipelines with LLM reasoning.
GitHub:
https://github.com/davidkim205/wallstreet-ai
What is Wallstreet-AI?
Wallstreet-AI is an LLM-powered financial analysis assistant that converts natural language questions into structured investment reports.
Instead of manually gathering:
- financial statements
- technical indicators
- earnings summaries
- market news
you simply ask:
"What would Warren Buffett think about Apple in a high interest rate environment?"
The system builds a full reasoning pipeline and generates a structured report.
Key Idea: Persona-driven financial reasoning
Different investors interpret the same data differently.
Example:
Buffett focuses on:
- business quality
- durable moat
- long-term cash flow
Dalio focuses on:
- macro regime changes
- interest rate cycles
- portfolio diversification
Wallstreet-AI lets the same dataset produce multiple interpretations depending on the persona applied.
System Architecture
The project is designed as an agent workflow:
- Natural language intent parsing
- Tool routing based on analysis type
- Data collection via APIs
- News enrichment using RSS scraping
- LLM synthesis
- Streaming output via SSE
- Structured logging for reproducibility
Pipeline:
User Query
→ Intent Parser
→ Tool Router
→ Data Collection
→ LLM Generation
→ Structured Report
Example Question
"What would Warren Buffett think about Microsoft today?"
The pipeline automatically:
- detects ticker
- determines analysis type
- gathers market data
- generates structured reasoning
- streams the output in real time
Live Demo
You can try it instantly:
HuggingFace Spaces:
https://huggingface.co/spaces/davidkim205/wallstreet-ai
Google Colab:
https://colab.research.google.com/drive/13rWqKpAgJMytsztSt_spsws4HPWQZPnc
Future ideas
Possible extensions:
- portfolio optimization personas
- backtesting integration
- vector database memory
- evaluation benchmarks for financial reasoning
- multi-agent debate between investor personas
Feedback welcome
I would appreciate feedback on:
- agent architecture design
- persona prompting approaches
- evaluation methodology for financial reasoning
- additional financial datasets
GitHub:
https://github.com/davidkim205/wallstreet-ai
Contributions are welcome.
