Black-Scholes Option Pricing & P&L Simulator

This repository provides an interactive Black-Scholes Pricing Model dashboard built using Streamlit. It allows users to simulate and visualize both the option prices and the resulting P&L (Profit and Loss) under varying market conditions, using real-time market data from yfinance.

Live App: https://blackscholemodel.streamlit.app/

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Features

Options Pricing, P&L, and Risk Heatmaps

• Computes Call and Put option prices using the Black-Scholes formula.
• Visualizes P&L heatmaps across a grid of spot prices and volatilities.
• Compares model prices to real market option prices, showing pricing errors as a heatmap.
• Visualizes option Greeks (Delta and Gamma) as 2D surfaces.

Real-Time Market Data Integration

• Pulls the latest price data and option chain from yfinance.
• Allows users to simulate pricing using implied volatility and market strikes.

Interactive Dashboard

• Fully adjustable inputs:
  • Spot Price (St)
  • Strike Price (K)
  • Volatility (σ)
  • Time to Maturity (t)
  • Risk-Free Rate (r)
  • Option purchase price
• Choose whether to use real-time data or manual inputs.
• Dynamically update the spot/volatility ranges for grid-based simulations.

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How It Works

The app calculates:

• Black-Scholes option prices
• Option Greeks (Delta, Gamma)
• P&L surface as:

  P&L = Model Option Price – Purchase Price
  

• Pricing Error as:

  Pricing Error = Model Price – Market Price
  

These are plotted over a configurable grid of:

• Spot Prices (S)
• Volatility (σ)

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Preview

Black-Scholes theoretical pricing structure

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Installation

pip install streamlit numpy pandas matplotlib seaborn scipy plotly yfinance

Run the app with:

streamlit run streamlit_app.py

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File Structure

📦 blackscholes/
 ┣ 📄 streamlit_app.py       # Main Streamlit dashboard
 ┣ 📄 README.md              # This file

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Author

Pushkar Ambastha
LinkedIn Profile

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