Interactive Black-Scholes Option Pricing App

An interactive Streamlit app that provides a comprehensive tool for analyzing options pricing, profit & loss (P&L), and mispricing using the Black-Scholes model. The app integrates theoretical pricing with real market data to help users make informed decisions about options trading.

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Features

1. Pricing Heatmaps

• Visualize theoretical call and put option prices across a range of:
  • Spot Prices
  • Volatilities
• Understand how option values behave under different market conditions.

2. P&L Analysis

• Calculate the profit & loss (P&L) for options based on user-defined purchase prices.
• Heatmaps color-coded:
  • Green: Positive P&L.
  • Red: Negative P&L.

3. Mispricing Visualization

• Compare theoretical Black-Scholes prices with real market data:
  • Blue: Undervalued options (buying opportunities).
  • Red: Overvalued options (selling or avoid opportunities).
• Analyze mispricing across a range of spot prices and volatilities.

4. Real Market Data Integration

• Fetch live market data, including:
  • Spot Price
  • Strike Price
  • Implied Volatility
  • Option Prices
• Ticker selection powered by Yahoo Finance.

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Installation

To run this app locally, follow these steps:

Prerequisites

Ensure you have Python 3.8 or later installed.

Clone the Repository

git clone https://github.com/your-repo-name/black-scholes-app.git
cd black-scholes-app

Install Dependencies

Install the required libraries using the provided requirements.txt file:

pip install -r requirements.txt

Usage

Run the App

Start the Streamlit app using:

streamlit run app.py

Features Walkthrough

1. Select Analysis Mode:

• Pricing Heatmaps
• P&L Analysis
• Mispricing Heatmaps

2. Configure Inputs:

• Ticker symbol (e.g., SPY, AAPL).
• Spot price and volatility range.
• Purchase price for P&L mode.

3. Interactive Results:

• Explore heatmaps to gain insights into options pricing dynamics.

Requirements

The app depends on the following Python libraries:

• yfinance: For fetching live market data.
• numpy: For numerical computations.
• scipy: To calculate the Black-Scholes formula.
• pandas: For data manipulation.
• seaborn and matplotlib: For visualizing heatmaps.
• streamlit: For building the interactive web app.

Project Structure

black-scholes-app/
├── main.py               # Main Streamlit app script
├── functions.py          # Helper functions for calculations and data fetching
├── screenshots/          # Screenshots of the app 
│   ├── pricing_heatmap.jpg 
│   ├── pnl_heatmap.jpg
│   ├── mispricing_heatmap.jpg
├── requirements.txt      # Dependencies for the project
├──  LICENSE              # Project license (MIT)
├── README.md             # Documentation (this file)

Screenshots

1. Pricing Heatmaps

Visualize call and put option prices for different spot prices and volatilities.

2. P&L Heatmaps

Analyze profits and losses for various scenarios.

3. Mispricing Visualization

Identify undervalued and overvalued options.

Future Enhancements

• Add 3D visualization for pricing and P&L surfaces.
• Include support for multiple tickers in one analysis.
• Extend functionality to analyze options strategies (e.g., spreads).

License

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