📈 Mutual Fund Returns Tracker

A modern web application to track and analyze mutual fund performance with real-time data fetching, beautiful UI, and automated daily updates.

✨ Features

• Real-time Data Fetching: Automatically fetches latest NAV and returns data from mutual fund APIs
• Clean Web Interface: Responsive UI built with Flask and modern web technologies
• Smart Caching: Redis-backed caching with in-memory fallback for optimal performance
• Advanced Filtering: Search and filter funds by name or category
• Export Functionality: Download fund data as CSV for further analysis
• Auto-refresh: Scheduled daily updates at 11 AM (configurable)
• Performance Tracking: Track returns across multiple timeframes (1D, 1W, 1M, 3M, 6M, 1Y, 3Y, 5Y)

🚀 Quick Start

Prerequisites

• Python 3.8+
• Redis (optional, for caching)

One-Click Setup & Run

Simply double-click the startup script:

start.bat: Checks dependencies and starts the Flask server

Or run from command line:

.\start.bat

This will:

• Create virtual environment (if needed)
• Install Python dependencies
• Start the Flask server at http://localhost:5000
• Automatically open your browser

📦 Manual Installation

Backend Setup

1. Create virtual environment:

python -m venv venv
venv\Scripts\activate  # On Windows

2. Install dependencies:

pip install -r requirements.txt

3. Run Flask server:

python app.py

The application will be available at http://localhost:5000

📁 Project Structure

mutual_funds/
├── app.py                    # Flask backend server
├── fetch_mf_returns.py       # Data fetching logic
├── config.py                 # Configuration settings
├── requirements.txt          # Python dependencies
├── start.bat                 # One-click launcher
├── templates/                # Flask HTML templates
├── static/                   # Static assets (CSS, JS)
└── logs/                     # Application logs

🔧 Configuration

Adding/Modifying Funds

Edit the funds list in fetch_mf_returns.py:

funds = [
    {"name": "Fund Name", "code": "FUND_CODE"},
    # Add more funds here
]

Setting up Daily Auto-refresh (11 AM)

Windows Task Scheduler

1. Open Task Scheduler
2. Create Basic Task
3. Set trigger: Daily at 11:00 AM
4. Set action: Start start.bat (full path: D:\Github clones\mutual_funds\start.bat)
5. Enable "Run whether user is logged on or not"
6. Set "Start in" directory to: D:\Github clones\mutual_funds

Or use PowerShell to create scheduled task:

$action = New-ScheduledTaskAction -Execute "D:\Github clones\mutual_funds\start.bat" -WorkingDirectory "D:\Github clones\mutual_funds"
$trigger = New-ScheduledTaskTrigger -Daily -At 11:00AM
$settings = New-ScheduledTaskSettingsSet -AllowStartIfOnBatteries -DontStopIfGoingOnBatteries
Register-ScheduledTask -TaskName "MutualFundTracker" -Action $action -Trigger $trigger -Settings $settings -Description "Daily mutual fund data refresh at 11 AM"

🔍 API Endpoints

• GET / - Main dashboard (web UI)
• GET /api/funds - Get all fund data (JSON)
• POST /api/refresh - Force data refresh
• GET /health - Health check endpoint

💡 Usage Tips

1. Quick Search: Use the search bar to find specific funds instantly
2. Category Filter: Filter by Large Cap, Mid Cap, Small Cap, Liquid, or Debt funds
3. Sort Columns: Click any column header to sort by that metric
4. Export Data: Click the download button to export current view as CSV
5. View Details: Click any fund row to see detailed performance metrics

🛠️ Troubleshooting

Server won't start

• Ensure Python 3.8+ is installed: python --version
• Check if port 5000 is free: netstat -an | findstr :5000
• Verify all dependencies: pip install -r requirements.txt

Data not updating

• Check internet connection
• Verify mutual fund API is accessible
• Clear Redis cache (if using): redis-cli FLUSHALL
• Check logs in logs/app.log

🔐 Environment Variables

Create a .env file for custom configuration:

FLASK_ENV=development
REDIS_URL=redis://localhost:6379
LOG_LEVEL=INFO

📊 Performance

• Caching: 10-minute TTL for fund data
• Async fetching: Parallel API calls for faster data retrieval
• Optimized rendering: Efficient data handling for large datasets

🤝 Contributing

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

📝 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Built with Flask and modern web technologies
• Uses public mutual fund APIs for data
• Redis for high-performance caching

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Note: This application is for educational and personal use. Always verify financial data from official sources before making investment decisions.