Zerodha-MCP-Tradin

🚀 Zerodha Market Connect Pro

An advanced algorithmic trading system for Zerodha, featuring automated trading strategies, real-time market analysis, and LLM-powered decision making. Built with Python and integrated with Zerodha's Kite API.

📝 Description

Zerodha Market Connect Pro (MCP) is a comprehensive algorithmic trading platform designed specifically for Zerodha traders. This system combines cutting-edge technology with sophisticated trading strategies to provide a powerful automated trading solution. Here's what makes it special:

• Intelligent Trading: Leverages Large Language Models (LLMs) for market analysis and trading decisions
• Real-time Processing: Handles live market data with low-latency execution and websocket streaming
• Risk Management: Implements robust risk controls including position sizing, stop-losses, and exposure limits
• Strategy Flexibility: Supports multiple trading strategies with customizable parameters
• Professional Tools: Includes advanced technical analysis, volume profiling, and price action pattern recognition
• Developer Friendly: Well-documented API, extensive testing suite, and Docker support for easy deployment

Perfect for both professional traders looking to automate their strategies and developers interested in algorithmic trading.

🌟 Key Features

• 🤖 Automated Trading

  • Real-time order execution
  • Multiple strategy support
  • Customizable entry/exit rules
  • Risk management automation

• 📊 Advanced Market Analysis

  • Real-time market data processing
  • Technical indicator calculations
  • Volume profile analysis
  • Price action patterns

• 🧠 LLM Integration

  • Natural language trading commands
  • Market sentiment analysis
  • Strategy optimization
  • Trading journal analysis

• ⚡ High Performance

  • Asynchronous operations
  • Efficient data handling
  • Real-time websocket streaming
  • Low-latency execution

• 🛡️ Risk Management

  • Position sizing rules
  • Stop-loss automation
  • Exposure limits
  • Portfolio diversification

🔧 Technical Architecture

zerodha_mcp/
├── auth/           # Authentication and session management
├── trading/        # Core trading functionality
├── analysis/       # Market analysis and indicators
└── llm/           # Language model integration

📋 Prerequisites

• Python 3.8 or higher
• Zerodha Kite trading account
• API credentials from Zerodha Developer Console
• OpenAI API key (for LLM features)

🚀 Quick Start

1. Clone the Repository

   git clone https://github.com/SirCharan/zerodha-market-connect-pro.git
   cd zerodha-market-connect-pro
   

2. Set Up Environment

   # Create and activate virtual environment
   python -m venv .venv
   source .venv/bin/activate  # Linux/macOS
   .venv\Scripts\activate     # Windows
   
   # Install dependencies
   pip install -r requirements.txt
   

3. Configure Credentials

   # Create .env file
   cp .env.example .env
   
   # Edit .env with your credentials
   ZERODHA_API_KEY=your_api_key
   ZERODHA_API_SECRET=your_api_secret
   OPENAI_API_KEY=your_openai_api_key  # Optional
   

4. Start Trading System

   python main.py
   

📊 Trading Strategies

Built-in Strategies

1. Moving Average Crossover

   from zerodha_mcp.trading.strategies import MACrossStrategy
   
   strategy = MACrossStrategy(
       fast_period=10,
       slow_period=30,
       timeframe="5min"
   )
   

2. RSI Mean Reversion

   from zerodha_mcp.trading.strategies import RSIMeanReversionStrategy
   
   strategy = RSIMeanReversionStrategy(
       period=14,
       overbought=70,
       oversold=30
   )
   

Custom Strategy Development

Create your own strategy by inheriting from the base Strategy class:

from zerodha_mcp.trading.base import Strategy

class MyCustomStrategy(Strategy):
    def __init__(self, **params):
        super().__init__()
        self.params = params

    def generate_signals(self, data):
        # Implement your strategy logic here
        pass

    def on_trade(self, trade):
        # Handle trade events
        pass

🔧 Configuration

Trading Parameters

Edit config/default.yaml to customize trading behavior:

trading:
  default_quantity: 1
  max_position_size: 100000
  stop_loss_percent: 2.0
  target_profit_percent: 4.0

risk_management:
  max_daily_loss: 10000
  max_trades_per_day: 10
  max_open_positions: 5

strategies:
  moving_average_crossover:
    enabled: true
    timeframe: "5min"
    fast_period: 10
    slow_period: 30

🐳 Docker Deployment

1. Build Image

   docker build -t zerodha-market-connect-pro .
   

2. Run Container

   docker run -d \
     --name zerodha-market-connect-pro \
     -v $(pwd)/config:/app/config \
     -v $(pwd)/.env:/app/.env \
     zerodha-market-connect-pro
   

📈 Performance Monitoring

Real-time Monitoring

# View trading logs
tail -f mcp.log

# Check system status
python -m zerodha_mcp.status

# Generate performance report
python -m zerodha_mcp.report

Metrics Dashboard

Access the web dashboard at http://localhost:5000/dashboard for:

• P&L visualization
• Strategy performance
• Risk metrics
• Trade history

🧪 Development

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=zerodha_mcp tests/

# Run specific test category
pytest tests/test_trading.py

Code Quality

# Format code
black zerodha_mcp tests

# Check typing
mypy zerodha_mcp

# Run linter
flake8 zerodha_mcp tests

🔍 Troubleshooting

Common Issues

1. Authentication Errors

   • Verify API credentials in .env
   • Check token expiration
   • Ensure API access is enabled

2. Order Placement Failures

   • Verify account balance
   • Check trading hours
   • Review order parameters

3. Strategy Issues

   • Validate configuration
   • Check data availability
   • Review error logs

📚 API Documentation

Trading Operations

from zerodha_mcp import ZerodhaMCP

# Initialize client
client = ZerodhaMCP()

# Place order
order = client.place_order(
    symbol="RELIANCE",
    quantity=1,
    side="BUY",
    order_type="MARKET"
)

# Get positions
positions = client.get_positions()

# Get holdings
holdings = client.get_holdings()

Market Data

# Get historical data
data = client.get_historical_data(
    symbol="RELIANCE",
    interval="5minute",
    from_date="2024-01-01",
    to_date="2024-01-31"
)

# Stream live ticks
client.subscribe(["RELIANCE"], callback=on_tick)

📄 License

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

🤝 Contributing

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

📬 Support & Contact

• 📧 Email: charandeepkapoor3@gmail.com
• 💻 GitHub: @SirCharan
• 📝 Issues: GitHub Issues
• 📚 Wiki: Project Documentation

🙏 Acknowledgments

• Zerodha for their excellent trading platform
• KiteConnect for the robust API
• All contributors who have helped improve this project