📈 Options Pricing & Risk Analysis Tool

A comprehensive web application for options pricing and risk assessment using mathematical models and Monte Carlo simulations.

🚀 Features

Core Pricing Models

• Black-Scholes Model - Theoretical European options pricing
• Monte Carlo Simulation - Stochastic price path modeling
• Real-time Calculations - Interactive parameter adjustments

Advanced Analytics

• Option Greeks - Delta, Gamma, Theta, Vega sensitivity analysis
• Payoff Diagrams - Visual profit/loss at expiration
• Risk Metrics - Value at Risk (VaR) and Expected Shortfall (ES)

Strategy Builder

• Covered Call Strategy - Long stock + Short call position
• Protective Put Strategy - Long stock + Long put position
• Interactive Visualization - Real-time P&L charts

Enhanced UI/UX

• Dark Theme - Professional financial dashboard aesthetic
• Interactive Charts - Plotly-powered visualizations
• Responsive Design - Clean, intuitive interface
• Educational Tooltips - Mathematical explanations and formulas

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Quick Start

1. Clone the repository

   git clone https://github.com/dafahentra/options-pricing-tool.git
   cd options-pricing-tool

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   streamlit run main.py

4. Open your browser and navigate to http://localhost:8501

📊 Usage

Basic Options Pricing

1. Set option parameters in the sidebar:

   • Current stock price (S)
   • Strike price (K)
   • Time to maturity (T)
   • Risk-free rate (r)
   • Volatility (σ)
   • Option type (Call/Put)

2. View calculated prices using both Black-Scholes and Monte Carlo methods

Greeks Analysis

Monitor option sensitivities:

• Delta: Price sensitivity to underlying asset
• Gamma: Delta sensitivity to underlying asset
• Theta: Time decay effect
• Vega: Volatility sensitivity

Strategy Analysis

1. Select a strategy (Covered Call or Protective Put)
2. Set strike prices and contract quantities
3. Analyze profit/loss scenarios across different stock prices

Risk Assessment

• VaR (95%): Maximum expected loss at 95% confidence
• Expected Shortfall: Average loss beyond VaR threshold
• Price Distribution: Histogram of simulated future prices

🏗️ Project Structure

options-pricing-tool/
├── main.py              # Main Streamlit application
├── models.py            # Mathematical models and calculations
├── chart_utils.py       # Chart creation and styling utilities
├── requirements.txt     # Python dependencies
├── .streamlit/
│   └── config.toml     # Streamlit configuration and theming
├── README.md           # Project documentation
└── LICENSE             # MIT License

🧮 Mathematical Models

Black-Scholes Formula

Call Option:

C = S₀ × N(d₁) - K × e^(-rT) × N(d₂)

Put Option:

P = K × e^(-rT) × N(-d₂) - S₀ × N(-d₁)

Where:

• d₁ = [ln(S₀/K) + (r + σ²/2)T] / (σ√T)
• d₂ = d₁ - σ√T

Monte Carlo Simulation

S_T = S₀ × exp[(r - σ²/2)T + σ√T × Z]

Where Z ~ N(0,1) is a standard normal random variable.

🎨 Customization

Theme Configuration

Modify .streamlit/config.toml to customize colors and appearance:

[theme]
base = "dark"
primaryColor = "#c584f7"
backgroundColor = "#1a1a1a"
secondaryBackgroundColor = "#2d2d2d"
textColor = "#ffffff"
font = "serif"

Chart Styling

Update chart_utils.py to modify chart colors and layouts.

📈 Performance Features

• Caching: Expensive calculations cached with @st.cache_data
• Optimized Computing: Vectorized NumPy operations
• Efficient UI: Minimal re-renders and smart state management

🤝 Contributing

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

📝 License

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

🔗 Resources

• Black-Scholes Model
• Monte Carlo Methods in Finance
• Option Greeks
• Streamlit Documentation

📧 Contact

For questions, suggestions, or collaboration opportunities, please open an issue or reach out via GitHub.

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