Advanced Options Research Platform - AI-powered research agent that provides comprehensive options trading insights backed by real-time market data and authoritative web sources.
OptiScout-Web is a sophisticated financial research platform designed for options traders, analysts, and finance professionals. It combines AI-powered analysis with real-time market data and comprehensive web research to deliver accurate, cited, and actionable insights on options trading strategies, market conditions, and risk management.
- 🔍 Intelligent Research: Advanced web search with content extraction from authoritative financial sources
- 📊 Live Market Data: Real-time price feeds and volatility indicators via Polygon.io integration
- 🤖 AI-Powered Analysis: GPT-4 powered insights with beginner-friendly explanations
- 📝 Structured Responses: Professional markdown format with citations and data tables
- ⚡ Real-Time Processing: Fast, concurrent data fetching and analysis
- 🎨 Modern UI: Responsive design with Fidelity-inspired professional styling
- Framework: Next.js 14 with App Router
- Language: TypeScript 5.0+
- Styling: TailwindCSS with custom Fidelity design system
- UI Components: Custom component library with professional styling
- State Management: React hooks with optimistic updates
- Framework: FastAPI with async/await patterns
- Language: Python 3.11+
- AI Integration: OpenAI GPT-4 with optimized prompts
- Web Search: Tavily API for authoritative source discovery
- Market Data: Polygon.io integration for live quotes and historical data
- Content Processing: Advanced extraction with trafilatura and custom parsers
User Query → Frontend → FastAPI Backend → AI Processing → Web Search + Live Data → Structured Response
- Python 3.11 or higher
- Node.js 18 or higher
- OpenAI API key
- Tavily API key
- (Optional) Polygon.io API key for live market data
# Navigate to backend directory
cd backend
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Configure environment
cp .env.example .env
# Edit .env with your API keys
# Start the server
uvicorn main:app --reload --host 0.0.0.0 --port 8000# Navigate to frontend directory
cd frontend
# Install dependencies
npm install
# Start development server
npm run dev- Frontend: http://localhost:3000
- Backend API: http://localhost:8000
- API Documentation: http://localhost:8000/docs
Create backend/.env with the following:
# Core AI & Search APIs (Required)
OPENAI_API_KEY=your_openai_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here
# Application Settings
ENVIRONMENT=development
# Live Market Data (Optional)
POLYGON_API_KEY=your_polygon_api_key_here
SUPABASE_FUNCTION_URL=your_supabase_function_url_hereBy default, the app uses OpenAI models. You can point to any OpenAI-compatible endpoint (e.g., xAI Grok) by setting these variables in backend/.env:
# Use an OpenAI-compatible endpoint
OPENAI_BASE_URL=https://api.x.ai/v1
# Choose the model served by that endpoint
OPENAI_MODEL=grok-code-fast-1The backend reads these at startup and will report the active model in GET /status.
- Polygon.io: For real-time stock and options data
- Custom Data Sources: Extend with your own market data APIs
Query: "Explain iron condor strategy for beginners"
Response: Comprehensive explanation with visual diagrams, risk profiles, and current market examples
Query: "What's the current implied volatility for SPY options?"
Response: Live IV data, historical context, and trading implications
Query: "Covered call risks for dividend stocks"
Response: Detailed analysis with real examples and risk mitigation strategies
- Options Traders: Professional and retail traders seeking comprehensive research
- Financial Analysts: Research professionals requiring cited, authoritative sources
- Educators: Finance instructors teaching options trading concepts
- Students: Finance students learning about derivatives and risk management
optiscout-web/
├── backend/ # FastAPI application
│ ├── main.py # API entry point
│ ├── agent.py # Core research agent
│ ├── web_search.py # Web search and content extraction
│ ├── live_data.py # Market data integration
│ └── schemas.py # Data models and validation
├── frontend/ # Next.js application
│ ├── app/ # App router pages
│ ├── components/ # React components
│ └── lib/ # Utility functions
└── docs/ # Documentation
# Backend tests
cd backend
pytest
# Frontend tests
cd frontend
npm test# Backend
cd backend
pip install -r requirements.txt
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker
# Frontend
cd frontend
npm run build
npm start- API Key Security: Environment variable management with secure defaults
- Rate Limiting: Built-in request throttling for external APIs
- CORS Configuration: Secure cross-origin resource sharing
- Input Validation: Comprehensive request validation and sanitization
- Response Time: < 3 seconds for complex research queries
- Concurrent Users: Supports 100+ simultaneous users
- Caching: Intelligent caching for frequently requested data
- Scalability: Horizontal scaling with Docker containers
We welcome contributions! Please see our Contributing Guidelines for details.
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
Educational Use Only: This platform is designed for research and educational purposes only. All content and analysis provided should not be construed as financial advice, investment recommendations, or trading signals.
No Investment Advice: The information generated by OptiScout-Web does not constitute professional financial advice. Users should conduct their own research and consult with qualified financial advisors before making investment decisions.
Market Risk: Options trading involves substantial risk and is not suitable for all investors. Past performance does not guarantee future results.
Built with ❤️ for the trading community