Quantfolio Simulation Hub

A professional-grade portfolio risk analysis platform that helps investors understand their investment risk using advanced Monte Carlo simulations, statistical modeling, and real-time market data.

🎯 Features

• 4 Simulation Methods

  • Monte Carlo Simulation
  • Historical Simulation
  • Bootstrap Simulation
  • Stress Testing

• Real-time Market Data 📈

  • Live NSE/BSE stock prices
  • 1-year historical data
  • Automatic data alignment & cleaning

• Professional Risk Metrics 📊

  • Value at Risk (VaR) calculation
  • Expected Shortfall (ES)
  • Maximum Loss/Gain analysis
  • Statistical summaries

• Interactive Dashboard 🎨

  • Drag-and-drop portfolio builder
  • Real-time portfolio summary
  • Dynamic loss distribution charts
  • Portfolio composition breakdown

• Professional PDF Reports 📄

  • Embedded loss distribution charts
  • Financial explanations & insights
  • Risk recommendations
  • Compliance-ready disclaimers

• Educational Content 🎓

  • VaR/ES explanations
  • Chart interpretation guides
  • Risk management recommendations

🛠️ Tech Stack

Component	Technology
Frontend	Streamlit
Backend	Python, NumPy, Pandas
Visualization	Plotly, Matplotlib
Data	yfinance
PDF Generation	ReportLab
Simulations	SciPy, NumPy

📸 Screenshots

Main Window

Risk Analysis Results

PDF Report

🚀 Quick Start

Prerequisites

• Python 3.9+
• pip package manager

Installation

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/quantfolio-simulation-hub.git
cd quantfolio-simulation-hub

2. Create virtual environment

python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

streamlit run app.py

5. Open in browser

http://localhost:8501

📖 How to Use

1. Upload Portfolio

   • Click "Upload Excel" or "Build Portfolio"
   • Add stock symbols and quantities
   • File format: Stock, Quantity columns

2. Configure Simulation

   • Choose simulation method (Monte Carlo, Historical, Bootstrap, Stress)
   • Set number of iterations (1,000 - 50,000)
   • Select confidence level (90% - 99%)

3. Run Simulation

   • Click "🚀 Run Simulation"
   • Wait for calculations to complete

4. Analyze Results

   • View risk metrics (VaR, ES)
   • Explore loss distribution chart
   • Read educational explanations
   • Download PDF report or CSV data

📊 Key Concepts Explained

Value at Risk (VaR)

The maximum amount you could lose on a single day with a given confidence level (e.g., 95%).

Example: VaR of ₹50,000 at 95% confidence means:

• 95% of days, you won't lose more than ₹50,000
• 5% of days, you might lose more than ₹50,000

Expected Shortfall (ES)

The average loss on days when the loss exceeds the VaR threshold. Represents the worst-case scenario losses.

Monte Carlo Simulation

Generates thousands of random market scenarios based on historical volatility and correlation to estimate potential portfolio outcomes.

🔧 Project Structure

quantfolio-simulation-hub/
├── app.py                          # Main Streamlit application
├── requirements.txt                # Python dependencies
├── README.md                       # This file
│
├── simulations/
│   ├── monte_carlo.py             # Monte Carlo implementation
│   ├── historical.py              # Historical simulation
│   ├── bootstrap.py               # Bootstrap resampling
│   └── stress_test.py             # Stress test simulation
│
├── utils/
│   ├── data_fetcher.py            # yfinance data integration
│   ├── pdf_generator.py           # PDF report generation
│   ├── explanations.py            # Financial education content
│   ├── styling.py                 # CSS styling
│   └── ...
│
└── ui/
    ├── portfolio_builder.py       # Portfolio input interface
    ├── modern_components.py       # Streamlit components
    └── charts.py                  # Visualization

🐛 Key Features & Bug Fixes

• ✅ Intelligent data cleaning: Handles missing values using forward/backward fill
• ✅ Stock alignment: Validates portfolio stocks against downloaded data
• ✅ Error handling: Comprehensive error messages for users
• ✅ Performance optimization: 10K simulations run in <5 seconds
• ✅ Professional output: PDFs with embedded charts and explanations

💡 Learning Points

This project demonstrates:

1. Financial Computing

   • Monte Carlo simulations
   • Statistical risk modeling
   • Time-series analysis

2. Data Engineering

   • Real-time data fetching
   • Data cleaning & alignment
   • Missing value handling

3. Software Design

   • Modular architecture
   • Error handling
   • Code organization

4. Product Development

   • UI/UX design
   • Professional reporting
   • User education

🚧 Future Enhancements

• Portfolio optimization suggestions
• Tax-loss harvesting recommendations
• Correlation analysis between stocks
• Multi-currency support
• Mobile app version
• API for integration
• Historical report tracking
• Risk alerts & notifications

📝 Notes

• Data is fetched in real-time from Yahoo Finance (yfinance)
• Simulations are based on 1-year historical data
• Past performance does not guarantee future results
• See disclaimer in PDF reports for important legal information

⚠️ Disclaimer

This tool is for educational and analytical purposes only. It is not a substitute for professional financial advice. Past performance does not guarantee future results. Consult with a qualified financial advisor before making investment decisions.

📄 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

👨‍💻 Author

[Your Name]

• GitHub: @purnankgogarkar
• LinkedIn: Purnank Gogarkar
• Email: purnank18@gmail.com

📧 Questions?

Feel free to open an issue or reach out directly!

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