Professional Algorithmic Trading Platform powered by QuantConnect LEAN Engine with integrated Smart Money Trading (SMT) concepts, AI/ML models, and comprehensive web dashboard.

• 🔧 Official QuantConnect LEAN Engine - Local execution, no API dependency
• 🤖 AI/ML Integration - Import/Export models (ONNX, TensorFlow, PyTorch, Scikit-learn)
• 📊 Smart Money Trading (SMT) - Order Blocks, Fair Value Gaps, Break of Structure
• 🌐 Web Dashboard - Complete broker management, portfolio analytics
• 🔗 Multi-Broker Support - Binance, Interactive Brokers, Paper Trading
• 🎛️ Advanced Risk Management - Position sizing, drawdown protection
• 📈 Real-time Monitoring - Live trading dashboard with P&L tracking
• 🔒 Security First - Encrypted credentials, rate limiting, SSL/TLS support
• ⚡ High Performance - Optimized Docker containers, caching, load balancing
• 🔄 CI/CD Ready - Automated testing, security scanning, deployment

graph TB
    A[Nginx Proxy] --> B[React Frontend]
    A --> C[Flask Backend]
    A --> D[Jupyter Research]
    A --> E[SMT Integration]
    
    C --> F[LEAN Engine]
    C --> G[PostgreSQL]
    C --> H[Redis]
    
    E --> I[ML Models]
    E --> J[SMT Indicators]
    
    F --> K[Algorithms]
    F --> L[Data]
    F --> M[Results]
    
    style A fill:#3b82f6
    style F fill:#10b981
    style G fill:#8b5cf6
    style H fill:#ef4444

• Docker and Docker Compose installed
• Git for repository cloning
• 4GB+ RAM recommended
• 10GB+ disk space for data and models

# Clone repository
git clone https://github.com/szarastrefa/lean-trading-bot-stack.git
cd lean-trading-bot-stack

# Make installer executable
chmod +x install.sh

# Run automated installation
./install.sh

After installation completes:

• 🌐 Dashboard: Your tunnel URL (displayed after install)
• 🔬 Research: Your_URL/research (Jupyter Lab)
• 🔗 API: Your_URL/api
• 📊 SMT API: Your_URL/smt

• 👤 Username: admin
• 🔑 Password: (randomly generated during install - displayed in terminal)
• 💾 Backup: Credentials saved to ~/.lean-bot-credentials-TIMESTAMP

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

# Restart single service
docker-compose restart webui-backend

# Update images
docker-compose pull && docker-compose up -d

# Development mode with hot reload
docker-compose -f docker-compose.yml up -d

# Build specific service
docker-compose build webui-backend

# Run tests
docker-compose run --rm webui-backend python -m pytest

1. Place Python files in algorithms/ directory
2. Follow LEAN algorithm structure:

from AlgorithmImports import *

class MyStrategy(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetCash(100000)
        self.AddEquity("SPY", Resolution.Hour)
        
    def OnData(self, data):
        if not self.Portfolio.Invested:
            self.SetHoldings("SPY", 1)

1. Upload models via SMT API:

curl -X POST "http://localhost:8000/models/upload" \
  -F "file=@model.pkl" \
  -F 'metadata={"type":"classification","accuracy":0.85}'

2. Use in algorithms:

# In your LEAN algorithm
model_predictions = self.smt_api.predict("my_model", features)

Key configuration options in .env:

# Core Settings
ENVIRONMENT=production
LEAN_MODE=BACKTESTING

# Security
ADMIN_PASSWORD=your_secure_password
FLASK_SECRET_KEY=your_secret_key

# Database
POSTGRES_PASSWORD=secure_db_password
REDIS_PASSWORD=secure_cache_password

# ML/AI
PERPLEXITY_API_KEY=your_ai_api_key
SMT_ENABLED=true

# Tunneling
TUNNEL_TYPE=localtunnel
TUNNEL_SUBDOMAIN=your-custom-name

# Interactive Brokers
IB_ACCOUNT=your_ib_account
IB_USERNAME=your_username
IB_PASSWORD=your_password
IB_TRADING_MODE=paper

# Binance
BINANCE_API_KEY=your_api_key
BINANCE_API_SECRET=your_secret
BINANCE_TESTNET=true

• Order Blocks: Institutional demand/supply zones
• Fair Value Gaps: Price imbalances for entries
• Break of Structure: Trend change identification
• Liquidity Zones: High-volume reversal areas

• Model Formats: ONNX, TensorFlow, PyTorch, Scikit-learn
• Real-time Predictions: Live trading integration
• Model Management: Upload, versioning, metadata
• Performance Tracking: Accuracy, confidence scores

• Portfolio Analytics: Real-time P&L, drawdown
• Algorithm Management: Start, stop, configure strategies
• Broker Integration: Multi-broker account management
• Risk Controls: Position sizing, stop losses
• Performance Reports: Backtests, live results

• 🔐 Encrypted Credentials: All secrets encrypted at rest
• 🛡️ Rate Limiting: API abuse prevention
• 🔍 Security Scanning: Automated vulnerability checks
• 🚦 Access Control: Role-based permissions
• 📝 Audit Logging: Complete action history
• 🔒 SSL/TLS: End-to-end encryption

1. AWS/GCP/Azure: Use provided Terraform scripts
2. DigitalOcean: One-click deployment available
3. VPS: Manual Docker installation

• ✅ Automated Testing: Unit, integration, E2E tests
• 🔍 Security Scanning: Vulnerability assessments
• 📦 Docker Builds: Multi-arch container images
• 🚀 Auto Deployment: Zero-downtime updates

• ⚡ Response Time: < 100ms API responses
• 📊 Throughput: 1000+ requests/second
• 💾 Memory Usage: < 2GB typical operation
• 🔄 Uptime: 99.9% availability target

Port Conflicts:

# Check port usage
sudo netstat -tulpn | grep :5000

# Stop conflicting services
sudo systemctl stop apache2

Docker Issues:

# Reset Docker
docker system prune -a
docker volume prune

# Restart services
docker-compose down && docker-compose up -d

Permission Errors:

# Fix permissions
sudo chown -R $USER:$USER .
chmod +x install.sh

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push branch: git push origin feature/amazing-feature
5. Open Pull Request

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

• QuantConnect for the LEAN engine
• Smart Money Concepts community
• All contributors and beta testers

• 📧 Email: support@lean-trading-bot.com
• 💬 Discord: Join Community
• 📖 Documentation: Wiki
• 🐛 Issues: GitHub Issues

⭐ If this project helped you, please give it a star!

🚀 Happy Trading! 📈