🚀 Momentum Portfolio Manager

An automated momentum-based portfolio management system for NSE stocks using Python.

📋 Features

• Automated Stock Screening: Filters stocks based on momentum criteria
• Sharpe Ratio Calculation: Multi-period Sharpe ratio analysis (3/6/9/12 months)
• Technical Filters: 200-day moving average and 52-week high proximity checks
• Portfolio Construction: Automatically builds portfolios with up to 30 stocks
• Daily Monitoring: Tracks portfolio stocks for 200 DMA breaks
• Monthly Rebalancing: Automated monthly portfolio rebalancing
• JSON Configuration: Easily configurable parameters

🔧 Setup Instructions

1. Prerequisites

• Python 3.7 or higher
• Internet connection for fetching stock data

2. Installation

1. Download all the files to a folder:

   • momentum_portfolio.py (main script)
   • setup_and_run.py (setup helper)
   • requirements.txt (dependencies)
   • Batch files for automation

2. Install dependencies:

   pip install -r requirements.txt

3. Or use the setup script:

   python setup_and_run.py

3. Configuration

The system uses a portfolio_config.json file for configuration:

{
  "max_stocks": 30,
  "exit_rank": 60,
  "dma_period": 200,
  "lookback_periods": [3, 6, 9, 12],
  "high_percentage_threshold": 30,
  "use_all_time_high": false,
  "portfolio_file": "current_portfolio.json",
  "data_cache_file": "stock_data_cache.json"
}

Configuration Parameters:

• max_stocks: Maximum number of stocks in portfolio (default: 30)
• exit_rank: Exit rank threshold (default: 60)
• dma_period: Moving average period (default: 200)
• lookback_periods: Sharpe ratio calculation periods in months
• high_percentage_threshold: Maximum distance from 52-week high (%)
• use_all_time_high: Use all-time high instead of 52-week high
• portfolio_file: File to store current portfolio
• data_cache_file: File to cache stock data

🎯 Usage

Method 1: Command Line

# Monthly rebalancing
python momentum_portfolio.py rebalance

# Daily monitoring
python momentum_portfolio.py monitor

# Show configuration
python momentum_portfolio.py config

Method 2: Interactive Menu

python setup_and_run.py

Method 3: Batch Files (Windows)

• Double-click run_rebalance.bat for monthly rebalancing
• Double-click run_monitor.bat for daily monitoring

Method 4: Shell Scripts (Linux/Mac)

# Make executable
chmod +x run_rebalance.sh run_monitor.sh

# Run scripts
./run_rebalance.sh    # Monthly rebalancing
./run_monitor.sh      # Daily monitoring

📊 Portfolio Strategy

Screening Criteria

1. Technical Filter: Stock price > 200-day moving average
2. Momentum Filter: Stock within 30% of 52-week high (configurable)
3. Sharpe Ratio: Ranked by 12-month Sharpe ratio
4. Universe: NSE stocks (expandable to NSE 750)

Portfolio Rules

• Maximum 30 stocks in portfolio
• Monthly rebalancing on predetermined dates
• Daily exit monitoring for 200 DMA breaks
• Immediate exit when stock breaks below 200 DMA
• New additions only on rebalance days

Sharpe Ratio Calculation

1. Fetch daily price data for specified lookback period
2. Calculate daily returns
3. Compute annualized return and volatility
4. Sharpe Ratio = Annualized Return / Annualized Volatility

📁 File Structure

momentum_portfolio/
├── momentum_portfolio.py       # Main portfolio manager
├── setup_and_run.py           # Setup and interactive runner
├── requirements.txt           # Python dependencies
├── portfolio_config.json     # Configuration file
├── current_portfolio.json    # Current portfolio data
├── run_rebalance.bat         # Windows rebalance script
├── run_monitor.bat           # Windows monitoring script
├── run_rebalance.sh          # Linux/Mac rebalance script
├── run_monitor.sh            # Linux/Mac monitoring script
├── rebalance_results_*.json  # Rebalancing results
└── monitoring_results_*.json # Daily monitoring results

🔄 Automation Setup

Windows Task Scheduler

1. Open Task Scheduler
2. Create Basic Task
3. Set trigger for monthly rebalancing
4. Set action to run run_rebalance.bat
5. Repeat for daily monitoring with run_monitor.bat

Linux/Mac Cron Jobs

# Edit crontab
crontab -e

# Add these lines:
# Daily monitoring at 9:30 AM
30 9 * * 1-5 /path/to/your/script/run_monitor.sh

# Monthly rebalancing on 1st of each month at 10:00 AM
0 10 1 * * /path/to/your/script/run_rebalance.sh

📈 Output Examples

Rebalancing Output

==============================================================
MOMENTUM PORTFOLIO REBALANCING
==============================================================
Fetching data for 50 stocks...
✓ RELIANCE.NS (1/50)
✓ TCS.NS (2/50)
...
Screening stocks...

📊 REBALANCING RESULTS
📈 New Portfolio Size: 30 stocks
➕ Added Stocks: 5
➖ Removed Stocks: 3

🆕 ADDED STOCKS:
   • TITAN.NS
   • MARUTI.NS
   • SUNPHARMA.NS

🗑️  REMOVED STOCKS:
   • ONGC.NS
   • COALINDIA.NS

📋 FINAL PORTFOLIO:
    1. RELIANCE.NS
    2. TCS.NS
    3. HDFCBANK.NS
    ...

Daily Monitoring Output

==============================================================
DAILY PORTFOLIO MONITORING
==============================================================
Checking DMA breaks...
🟢 RELIANCE.NS - Above 200 DMA (Price: 2450.00, DMA: 2380.50)
🟢 TCS.NS - Above 200 DMA (Price: 3890.00, DMA: 3750.25)
🔴 ONGC.NS - Below 200 DMA (Price: 185.50, DMA: 195.75)

📊 MONITORING RESULTS
📈 Portfolio Size: 30 stocks
🔴 Stocks Below 200 DMA: 1
🟢 Healthy Stocks: 29

⚠️  STOCKS TO EXIT (Below 200 DMA):
   • ONGC.NS

🛠️ Customization

Adding More Stocks

To expand beyond the sample tickers, modify the get_nse_tickers() method in momentum_portfolio.py:

def get_nse_tickers(self) -> List[str]:
    # Add your NSE 750 tickers here
    your_tickers = [
        'RELIANCE', 'TCS', 'HDFCBANK', 
        # ... add all 750 tickers
    ]
    return [ticker + '.NS' for ticker in your_tickers]

Adjusting Parameters

Modify portfolio_config.json:

• Change max_stocks for different portfolio sizes
• Adjust high_percentage_threshold for different momentum criteria
• Modify lookback_periods for different Sharpe calculation windows
• Set use_all_time_high: true to use all-time highs instead of 52-week highs

Adding New Filters

Extend the screen_stocks() method to add custom filters:

# Example: Add volume filter
avg_volume = data['Volume'].tail(20).mean()
if avg_volume < 100000:  # Minimum volume threshold
    continue

🚨 Important Notes

Data Limitations

• Uses Yahoo Finance data (free but may have delays)
• Sample includes ~50 stocks (expand to NSE 750 for production)
• Historical data availability may vary by stock

Risk Considerations

• Backtesting: Always backtest before live trading
• Market Conditions: Strategy performance varies with market conditions
• Slippage: Consider transaction costs and slippage
• Position Sizing: Implement proper position sizing rules

Error Handling

• System handles missing data gracefully
• Logs errors for individual stock processing
• Continues processing even if some stocks fail

📞 Troubleshooting

Common Issues

1. Import Errors

   pip install --upgrade yfinance pandas numpy requests

2. Data Fetch Failures

   • Check internet connection
   • Verify ticker symbols are correct
   • Some stocks may be delisted or suspended

3. Configuration Errors

   • Ensure portfolio_config.json is valid JSON
   • Check file permissions for writing results

4. Empty Portfolio

   • Relax screening criteria
   • Check if stocks meet all filters
   • Verify data availability for selected time periods

Debug Mode

Add debugging by modifying the script:

import logging
logging.basicConfig(level=logging.DEBUG)

🔄 Updating the System

Adding New Features

1. Fork the code
2. Add new methods to the MomentumPortfolioManager class
3. Update configuration schema if needed
4. Test thoroughly before production use

Version Control

Consider using Git for tracking changes:

git init
git add .
git commit -m "Initial momentum portfolio setup"

📚 Additional Resources

• NSE Website - Official NSE data
• Yahoo Finance API - Data source documentation
• Pandas Documentation - Data manipulation
• NumPy Documentation - Numerical computing

⚖️ Disclaimer

This software is for educational and research purposes only. It is not investment advice. Always:

• Consult with financial advisors
• Understand the risks involved
• Test thoroughly before real trading
• Consider your risk tolerance
• Past performance doesn't guarantee future results

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Happy Trading! 🚀📈