Multi-Agent Investment Research Platform

AI-powered equity research platform that generates comprehensive investment reports using multi-agent orchestration, RAG, and real-time financial data.

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Screenshot

📎 More Screenshots(Interactive Charts)

🚀 What It Does

Ask any investment question in natural language:

• English: "What is the investment outlook for Microsoft?"
• 中文: "微软的投资前景如何？"

Get a comprehensive report with:

• Real-time market data & 52-week trends
• Sentiment analysis from news
• Analyst consensus & price targets
• Peer valuation comparison (11 sectors)
• EDGAR SEC filings context

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⚡ Quick Start

1. Prerequisites

• Python 3.11+
• Node.js 18+
• OpenAI API key (Get one here)
• MongoDB

2. Setup

# Clone repository
git clone <your-repo-url>
cd multi-agent-investment

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install Python dependencies
pip install -r requirements.txt

# Install frontend dependencies
cd frontend
npm install
cd ..

3. Configure Environment Variables

# Copy template
cp .env.template .env

# Edit .env and add your credentials:
# - OPENAI_API_KEY: Your OpenAI API key
# - MONGODB_URI: Your MongoDB connection string

Required Environment Variables:

• OPENAI_API_KEY - OpenAI API key for GPT-4o and embeddings
• MONGODB_URI - MongoDB connection string
• MONGODB_DB_NAME - Database name (default: investment_research)
• CHROMA_PERSIST_DIR - Local directory for vector store (default: ./data/chroma)

4. Initialize Databases

python -m backend.scripts.init_db
python -m backend.scripts.init_ticker_cache

5. Run the Application

# Backend (Terminal 1)
uvicorn backend.main:app --reload --port 8000

# Frontend (Terminal 2)
cd frontend && npm install && npm run dev

Access:

• Frontend: http://localhost:3000
• API: http://localhost:8000/docs

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💡 Core Features

✅ Multi-Agent Workflow - LangGraph orchestration (router, market data, sentiment, report)
✅ Real-time Data - Yahoo Finance, SEC EDGAR, financial news
✅ Smart Analysis - 52-week trends, peer valuation, analyst consensus
✅ RAG Pipeline - ChromaDB vector search for EDGAR filings
✅ Bilingual - Auto-detects language (EN/CN)
✅ Conversation Memory - MongoDB session history (24h TTL)

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🛠️ Technology Stack

Component	Technology
Backend	FastAPI, Python 3.11+
Multi-Agent	LangGraph, LangChain
LLM	OpenAI GPT
Databases	MongoDB, ChromaDB
Frontend	React, TypeScript, Tailwind CSS
Data Sources	Yahoo Finance, SEC EDGAR

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📝 Example Usage

Via Frontend

1. Open http://localhost:3000
2. Type: "Analyze Apple's recent performance"
3. Get comprehensive report in seconds

Via API

curl -X POST http://localhost:8000/api/research/query \
  -H "Content-Type: application/json" \
  -d '{"query": "What is the investment outlook for NVDA?"}'

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📁 Project Structure

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├── backend/
│   ├── agents/          # LangGraph multi-agent system
│   ├── api/             # FastAPI REST endpoints
│   ├── memory/          # MongoDB conversation memory
│   ├── rag/             # RAG pipeline (EDGAR, news)
│   ├── services/        # Yahoo Finance, ChromaDB
│   └── config/          # Settings & environment config
├── frontend/
│   └── src/
│       ├── components/  # React UI components
│       ├── api/         # API client
│       └── types/       # TypeScript definitions
├── data/
│   ├── chroma/          # Vector store (local)
│   ├── edgar_filings/   # Downloaded SEC filings (not in git)
│   └── ticker_cache.json # Ticker resolution cache
└── tests/               # Test suite

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🎨 Features Showcase

Deep Analysis Mode

• On-demand SEC 10-K filing analysis
• Automatic download and vector embedding
• Comprehensive business insights and risk analysis

Multi-Language Support

• Automatic language detection (English/Chinese)
• Bilingual UI and reports
• Natural query understanding

Real-time Market Data

• Yahoo Finance integration
• 52-week price trends
• Peer sector comparison
• Analyst consensus ratings

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🤝 Contributing

This is a personal portfolio project demonstrating:

• Multi-agent AI systems with LangGraph
• RAG pipeline implementation
• Full-stack development (FastAPI + React)
• Financial data integration

For questions or suggestions, please open an issue or contact me at [email protected].

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• LangGraph & LangChain - Multi-agent orchestration framework
• OpenAI - GPT for analysis and embeddings
• Yahoo Finance - Real-time market data
• SEC EDGAR - Official company filings

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