Building an AI that analyzes stocks like Warren Buffett

Source: Dev.to

Introduction

Most financial tools give you raw data, but investors don’t make decisions using numbers alone—they interpret them through a framework.

• Warren Buffett: focuses on moat and intrinsic value.
• Ray Dalio: looks at macro cycles.
• Charlie Munger: emphasizes avoiding mistakes.

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:

How Wallstreet‑AI Works

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, for example:

“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.

Persona‑Based Interpretation

Different investors interpret the same data differently:

Wallstreet‑AI lets the same dataset produce multiple interpretations depending on the persona applied.

Architecture & Pipeline

The project is designed as an agent workflow:

1. Natural language intent parsing
2. Tool routing based on analysis type
3. Data collection via APIs
4. News enrichment using RSS scraping
5. LLM synthesis
6. Streaming output via Server‑Sent Events (SSE)
7. Structured logging for reproducibility

Detailed Pipeline Flow

User Query
   → Intent Parser
   → Tool Router
   → Data Collection
   → LLM Generation
   → Structured Report

Example query: “What would Warren Buffett think about Microsoft today?”

The pipeline automatically:

• Detects the ticker
• Determines the analysis type (e.g., Buffett persona)
• Gathers market data and relevant news
• Generates structured reasoning
• Streams the output in real time

Try It Out

• HuggingFace Spaces:
• Google Colab notebook:

Possible Extensions

• Portfolio‑optimization personas
• Backtesting integration
• Vector‑database memory for long‑term context
• Evaluation benchmarks for financial reasoning
• Multi‑agent debate between investor personas

Feedback & Contributions

I would appreciate feedback on:

• Agent architecture design
• Persona‑prompting approaches
• Evaluation methodology for financial reasoning
• Additional financial datasets

Contributions are welcome:

• GitHub repository: