A comprehensive full-stack algorithmic trading platform with advanced AI strategies, real-time market data, and professional-grade automation workflows. Built for Indian stock markets with support for multiple brokers.
- π― Features
- π οΈ Tech Stack
- β‘ Quick Start
- π€ Solo-Developer Workflow
- π§ Configuration
- π Deployment
- π Monitoring
- π Documentation
- π€ Contributing
- Multi-Broker Support: Angel One, Dhan, Upstox, Zerodha, Fyers
- Real-Time Market Data: Live prices, indices, and market analytics
- AI-Powered Strategies: Fibonacci retracements, SuperTrend + EMA, sentiment analysis
- Advanced Order Management: Stop-loss, target orders, position sizing
- Options Trading: Comprehensive options strategies and ATM selection
- Risk Management: Portfolio tracking, P&L analysis, drawdown monitoring
- User Authentication: Google OAuth, JWT tokens, role-based access
- WebSocket Architecture: Centralized high-speed admin feed for multiple users
- Responsive UI: Material-UI v6 with dark theme, mobile-optimized
- System Health: Parallelized real-time telemetry and operational integrity checks
- Backtesting Engine: Strategy validation with historical data
- β‘ Zero-Downtime Deployment: Automated rolling updates via Railway and Render
- π§ Automated Releases: Push-to-Tag triggers for GitHub Releases & Changelogs
- π Security Scanning: Automatic secret, dependency, and code analysis
- π Performance Monitoring: Integrated system resource tracking
- π Auto-Built Docs: MkDocs Material site automatically deployed to
/documentation
This platform is optimized for a solo developer to manage high-stakes trading with zero manual maintenance.
- Develop: Commit changes using prefixes like
feat:,fix:, orperf:. - Tag: At the end of the day, create a version tag:
git tag v1.0.1
- Push: Push the tag to trigger the automated release and deployment:
git push origin v1.0.1
- Result: Your bot is deployed, health checks run, and a formal GitHub Release with an auto-generated changelog is created.
- Framework: Python 3.10+ with FastAPI
- Database: PostgreSQL with SQLAlchemy ORM
- Real-Time: WebSocket + Socket.IO for live data
- Caching: Redis with connection failure caching
- AI/ML: Technical indicators and sentiment analysis engine
- Framework: React 19 with TypeScript
- UI Library: Material-UI v6 + Tailwind CSS
- Documentation: MkDocs Material (hosted at
/documentation) - Charts: Recharts & Lightweight Charts
- Live App: https://tradingbot-ttys.onrender.com
- Full Documentation: https://tradingbot-ttys.onrender.com/documentation
- API Reference: /docs (Swagger)
# Clone repository
git clone https://github.com/growthquantix/tradingbot.git
cd tradingbot
# Install requirements
pip install -r requirements.txt
# Start backend
python app.pyConfigure these in your .env or cloud dashboard (Render/Railway):
DATABASE_URL: Your PostgreSQL connection stringJWT_SECRET: 32+ character random stringUPSTOX_API_KEY: For broker connectivityENVIRONMENT: Set toproductionfor cloud deployment
- Backend: Python FastAPI hosted on Render
- Database: PostgreSQL hosted on Render (Managed)
- Frontend: React SPA hosted on Netlify
- Documentation: Static MkDocs site hosted on Netlify under
/documentation
Access the System Health page in the UI to monitor:
- Operational Integrity: Real-time status of Token Refresh, Stock Selection, and Options Enhancement.
- Resource Usage: CPU, RAM, and Disk telemetry.
- Latency: Database, Redis, and API response times.
- β Secret Protection: Automatic secret scanning in CI/CD.
- β SQL Injection Prevention: Using SQLAlchemy ORM.
- β Production Logging: Console-only logging in production to prevent disk bloat.
- β Secure Auth: JWT with refresh tokens and Google OAuth.
Built with β€οΈ for Indian Stock Market Traders Empowering retail traders with institutional-grade technology