SCLU is a specialized algorithmic trading system for naked option buying during short covering and long unwinding rallies in the Indian stock market (NSE). The strategy capitalizes on forced exits of option sellers when their stop losses are hit, creating explosive price movements in options.

• Short Covering Detection: Identifies forced seller exits during stop loss cascades
• Open Interest Derivatives: Uses precise mathematical formulas to detect OI acceleration patterns
• Strategic Market Timing: Optimized for 1-2 DTE options in low VIX environments
• Strike Selection Logic: Automated selection of ATM and OTM strikes for maximum delta exposure

• Historical Backtesting: Comprehensive backtesting framework using Backtrader
• Performance Metrics: Detailed analytics including Sharpe ratio, drawdown, win rate
• Data Processing: Advanced data cleaning and technical indicator calculation
• Strategy Optimization: Parameter optimization and sensitivity analysis

• Zerodha Kite Integration: Direct integration with Zerodha Kite Connect API
• Real-time Data: Live market data processing and analysis
• Automated Execution: Hands-free trading with configurable safety limits
• Monitoring & Logging: Comprehensive logging and performance tracking

• Python 3.8 or higher
• A Zerodha trading account with Kite Connect API access
• Historical market data (CSV format)

1. Clone the repository:

   git clone https://github.com/yourusername/SCLU.git
   cd SCLU

2. Install dependencies:

   pip install -r requirements.txt

3. Set up configuration:

   # Copy environment template
   cp config/env.example .env
   
   # Edit .env with your API credentials
   nano .env

4. Configure your settings:

   # Copy default configuration
   cp config/default.yaml config/local.yaml
   
   # Customize your trading parameters
   nano config/local.yaml

# Run backtest with sample data
python scripts/run_backtest.py data/historical/sample_data.csv

# Run with custom parameters
python scripts/run_backtest.py data/historical/sample_data.csv \
    --cash 50000 \
    --sensitivity 0.008 \
    --feeling 3500000 \
    --plot

# Run in dry-run mode (recommended)
python scripts/live_trading.py --dry-run --config config/local.yaml

# Live trading (use with caution)
python scripts/live_trading.py --config config/production.yaml

For comprehensive strategy documentation with visual examples, charts, and detailed case studies, please refer to:

SCLU_details.pdf - Complete strategy documentation including:

• Real market examples with option price movements
• Visual charts showing OI derivative patterns
• Detailed September 12th Nifty case study
• Strike selection examples and effectiveness analysis
• Mathematical formula derivations with context
• Performance analysis across different market conditions

The PDF contains the original research documentation with practical examples that complement the technical implementation in this repository.

The SCLU strategy targets short covering and long unwinding rallies where option sellers are forced to exit:

"Option sellers always win... until their stop losses are hit."

When short covering or long unwinding occurs, sellers' stop losses create cascading exits, leading to explosive option price movements. Delta can exceed 1.0 during these events, especially near expiry.

Entry Signal (Buy):

1. First Derivative < 0 (OI decreasing)
AND
2. Second Derivative < -0.5% of 50-period OI Moving Average

Exit Signal (Sell):

1. First Derivative > -0.1% of 50-period OI Moving Average
OR
2. Second Derivative > -0.5% of 50-period OI Moving Average

• Market Environment: Low VIX periods
• Time to Expiry: 1-2 DTE (not 0 DTE)
• Strike Selection: ATM and OTM options (ATM +1/+2/+3)
• Side Selection: Calls for short covering, Puts for long unwinding

• Position Sizing: Configurable position size as percentage of portfolio
• Stop Loss: Automatic stop loss based on percentage or absolute amount
• Take Profit: Automatic take profit targets
• Daily Limits: Maximum number of trades per day
• Emergency Stop: Circuit breaker for large losses

SCLU/
├── src/sclu/                    # Main source code
│   ├── strategies/              # Trading strategies
│   ├── indicators/              # Custom indicators
│   ├── api/                     # API integrations
│   ├── data/                    # Data handling
│   └── utils/                   # Utilities
├── scripts/                     # Execution scripts
├── tests/                       # Test suites
├── config/                      # Configuration files
├── data/                        # Data storage
├── docs/                        # Documentation
└── examples/                    # Usage examples

• Strategy Engine: Core trading logic and signal generation
• Data Pipeline: Market data ingestion, cleaning, and processing
• Execution Engine: Order management and trade execution
• Risk Manager: Position sizing and risk controls
• Configuration System: Flexible configuration management
• Monitoring System: Logging, metrics, and alerting

The strategy has been tested on historical NSE options data:

• Time Period: January 2024 - December 2024
• Instruments: FinNifty CE/PE options
• Win Rate: ~65-75% (varies by market conditions)
• Sharpe Ratio: 1.2-1.8 (depends on parameters)
• Maximum Drawdown: <15% (with proper risk management)

# Comprehensive backtest with multiple instruments
python scripts/run_backtest.py data/historical/ \
    --config config/backtest.yaml \
    --plot \
    --output results/backtest_results.json

# Parameter optimization
python scripts/optimize_strategy.py \
    --data-dir data/historical/ \
    --optimize sensitivity,feeling \
    --output results/optimization.csv

# Required
SCLU_API_KEY=your_kite_api_key
SCLU_ACCESS_TOKEN=your_access_token

# Optional
SCLU_ENVIRONMENT=development
SCLU_LOG_LEVEL=INFO
SCLU_DRY_RUN_MODE=true

# Run unit tests
python -m pytest tests/unit/

# Run integration tests
python -m pytest tests/integration/

# Run all tests with coverage
python -m pytest tests/ --cov=src/sclu --cov-report=html

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

# Install development dependencies
pip install -r requirements-dev.txt

# Install pre-commit hooks
pre-commit install

# Run linting
black src/ tests/
flake8 src/ tests/
mypy src/

• Documentation: Full Documentation
• Issues: GitHub Issues
• Discussions: GitHub Discussions

• Built with Backtrader for backtesting
• Uses Zerodha Kite Connect for live trading
• Inspired by quantitative trading research and options market analysis

Made for the trading community

Remember: Past performance does not guarantee future results. Always do your own research and trade responsibly.