📈 Equity-intelligence-Agent

A modular financial intelligence agent that performs grounded analysis using deterministic tools, supports extensible reasoning states, and is designed to scale across models and interfaces.

🎯 Overview

EQ-Agent is an AI-powered financial analyst that provides real-time stock analysis, fundamental metrics, and industry-contextualized insights. Built with LangChain and OpenBB, it combines the power of large language models with reliable financial data sources.

✨ Features

• 🔍 Real-time Stock Data: Fetch current and historical stock prices
• 📊 Fundamental Analysis: Deep dive into company financials with industry benchmarks
• 🏭 Industry Context: Compare metrics against sector-specific standards
• ⚖️ Comparative Analysis: Side-by-side comparison of multiple stocks
• 💰 Capital Allocation Insights: Understand buyback strategies and ROE drivers
• 🎯 Smart Symbol Resolution: Convert company names to ticker symbols automatically

🛠️ Tools Available

Tool	Description
stock_name	Converts company names to ticker symbols
get_stock_price	Fetches historical price data (last 5 days)
get_company_profile	Retrieves company description and overview
fundamental_analysis	Comprehensive financial metrics with industry benchmarks
compare_stocks	Multi-stock comparison with ROE-based ranking
get_capital_allocation	Analyzes buyback activity and capital strategy

📸 Screenshots

Agent in Action

Real-time fundamental analysis with industry-contextualized insights

🚀 Quick Start

Prerequisites

Python 3.8+
pip install langchain langchain-core openbb

Installation

# Clone the repository
git clone https://github.com/sanjayy0612/Equity-intelligence-Agent.git
cd Equity-intelligence-Agent

# Install dependencies
pip install -r requirements.txt

# Set up environment variables (if needed)
cp .env.example .env

Usage

from tools import fundamental_analysis, compare_stocks

# Analyze a single stock
result = fundamental_analysis("AAPL")
print(result)

# Compare multiple stocks
comparison = compare_stocks("TSLA,F,GM")
print(comparison)

📋 Industry Benchmarks

The agent includes pre-configured benchmarks for major sectors:

• Technology: High margins (20% avg), high P/E (30 avg)
• Automotive: Moderate margins (6% avg), lower P/E (15 avg)
• Healthcare: Strong margins (15% avg), moderate P/E (22 avg)
• Financial Services: High margins (25% avg), leverage-heavy
• Energy: Variable margins (8% avg), cyclical patterns
• Consumer Cyclical: Tight margins (5% avg), inventory-intensive

🏗️ Architecture

Equity-intelligence-Agent/
│
├── ai_engine.py          # LangChain agent orchestration
├── app.py                # User interface / API layer
├── context_schema.py     # Data models and schemas
├── exa.py                # Example usage and demos
├── main.py               # Entry point
├── states.py             # Agent reasoning states
├── tools.py              # Financial analysis tools
└── README.md             # This file

🧠 How It Works

1. Query Processing: User asks a financial question
2. Tool Selection: Agent intelligently selects appropriate tools
3. Data Retrieval: OpenBB fetches real-time financial data
4. Contextualization: Metrics are compared against industry benchmarks
5. Response Generation: LLM synthesizes insights in natural language

🎓 Example Queries

✅ "Analyze AMD's profitability"
✅ "Compare Tesla, Ford, and GM"
✅ "What's Apple's current ROE and how does it compare to the tech industry?"
✅ "Why is Tesla's ROE so high?"
✅ "Get me the latest stock price for Microsoft"

🔧 Configuration

Adding New Companies

Edit the COMPANY_TICKERS dictionary in tools.py:

COMPANY_TICKERS = {
    "your_company": "TICK",
    # ... existing entries
}

Customizing Benchmarks

Modify INDUSTRY_BENCHMARKS in tools.py to adjust sector standards.

📊 Supported Metrics

• Profitability: Profit Margin, ROE, EPS
• Valuation: P/E Ratio, Price-to-Book
• Leverage: Debt-to-Equity
• Liquidity: Current Ratio
• Growth: Revenue trends, margin expansion

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• OpenBB for providing robust financial data APIs
• LangChain for the agent orchestration framework
• Yahoo Finance for real-time market data

📧 Contact

Sanjay - @sanjayy0612

Project Link: https://github.com/sanjayy0612/Equity-intelligence-Agent

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