💰 Options Pricing Dashboard

Welcome to the Options Pricing Dashboard, an interactive platform designed to help you visualize and analyze option prices using various financial models. Whether you're a trader, student, or finance enthusiast, this dashboard offers a comprehensive toolset to understand how different factors influence option valuations.

[]

📚 Table of Contents

• Overview
• 🚀 Features
• 🔍 Models Explained
  • 1. Black-Scholes Model
  • 2. Monte Carlo Simulation
  • 3. Binomial Model
• 📸 Screenshots
• 🛠️ Getting Started
  • Prerequisites
  • Installation
  • Running the Project
• 🤝 Contributing
• 📄 License
• 🆕 Version

Overview

The Options Pricing Dashboard provides an intuitive interface to explore and analyze option pricing using three prominent financial models:

• Black-Scholes Model
• Monte Carlo Simulation
• Binomial Model

Each model offers unique insights into how various parameters such as Spot Price, Volatility, Time to Maturity, and Risk-Free Interest Rate impact the pricing of Call and Put options.

🚀 Features

1. Options Pricing Visualization:

   • Display both Call and Put option prices using interactive heatmaps.
   • Real-time updates as you adjust parameters like Spot Price, Volatility, and Time to Maturity.

2. Interactive Dashboard:

   • Input different values for Spot Price, Volatility, Strike Price, Time to Maturity, and Risk-Free Interest Rate.
   • Immediate calculation and display of both Call and Put option prices for easy comparison.

3. Model-Specific Insights:

   • Black-Scholes: Understand the theoretical pricing mechanism.
   • Monte Carlo: Simulate a wide range of possible price paths.
   • Binomial Model: Explore discrete-time option pricing trees.

4. Customizable Parameters:

   • Set custom ranges for Spot Price and Volatility to generate comprehensive views under diverse market conditions.

5. Visualization Tools:

   • Heatmaps for P&L analysis.
   • Simulation paths and price convergence distributions.

🔍 Models Explained

1. Black-Scholes Model

The Black-Scholes Model is a mathematical model for pricing an options contract. It estimates the variation over time of financial instruments, specifically European-style options.

• Key Features:
  • Calculates theoretical option prices.
  • Provides insights into the Greeks (Delta, Gamma, etc.) for risk management.
  • Assumes constant volatility and interest rates.

2. Monte Carlo Simulation

Monte Carlo Simulation is a computational algorithm that relies on repeated random sampling to obtain numerical results. In option pricing, it simulates a large number of possible price paths for the underlying asset.

• Key Features:
  • Handles complex and path-dependent options.
  • Provides probabilistic distribution of option prices.
  • Useful for pricing options with multiple sources of uncertainty.

3. Binomial Model

The Binomial Model is a discrete-time model for the varying price over time of financial instruments, primarily used for pricing options.

• Key Features:
  • Builds a price tree to evaluate option prices at different nodes.
  • Flexible and can model American options.
  • Easier to implement for options with early exercise features.

📸 Screenshots

1. Black-Scholes Model

2. Monte Carlo Simulation

3. Binomial Model

🛠️ Getting Started

Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Ensure you have the following installed:

• Python 3.7 or higher
• Git

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/options-pricing-dashboard.git
   cd options-pricing-dashboard

2. Create a Virtual Environment

   It's good practice to use a virtual environment to manage dependencies.

   python -m venv venv

3. Activate the Virtual Environment

   • Windows:

     venv\Scripts\activate

   • macOS/Linux:

     source venv/bin/activate

4. Install Dependencies

   pip install -r requirements.txt

   or manually:

   pip install streamlit numpy pandas scipy matplotlib seaborn plotly

Running the Project

1. Navigate to the Project Directory

   Ensure you're in the project's root directory.

2. Run the Streamlit App with the Landing Page

   streamlit run Home.py

3. Access the Dashboard

   After running the above command, Streamlit will provide a local URL (e.g., http://localhost:8501). Open this URL in your web browser to interact with the dashboard.

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

1. Fork the Repository

2. Create a Feature Branch

   git checkout -b feature/YourFeatureName

3. Commit Your Changes

   git commit -m "Add some feature"

4. Push to the Branch

   git push origin feature/YourFeatureName

5. Open a Pull Request

   "Go to the repository on GitHub and open a pull request to main; we'll review and merge your changes or send a message if we need more changes. Any improvements are welcome!"

---

Feel free to reach out with any questions or feedback!