Options Pricing Engine

A simple and interactive Options Pricing Engine built with Python. Supports European, American, Asian, and Barrier options using mathematical models like Black-Scholes, Binomial Tree, and Monte Carlo simulation.

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🚀 Features

• Fetches spot price and 1-year historical volatility automatically from Yahoo Finance using the stock ticker.
• Supports:
  • European (Call/Put) via Black-Scholes
  • American (Call/Put) via Binomial Tree
  • Asian options (Call/Put) via Monte Carlo simulation
  • Barrier options (Call/Put – up-out/down-out) via Monte Carlo
• Takes strike price, expiry date, and risk-free rate as user inputs.
• Auto-calculates time to expiry in years.
• Interactive and clean Streamlit dashboard with input sidebar and visualization section.
• Visualizes option price sensitivity:
  • Price vs Strike Price
  • Price vs Volatility
• CLI fallback available for quick testing.

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🛠️ Project Structure

options-pricing-engine/
├── app/
│   ├── __init__.py
│   ├── pricing.py           # Black-Scholes, Binomial Tree, and Monte Carlo Simulation logic
│   ├── plot_helpers.py      # For visualization using matplotlib
│   ├── data_fetcher.py      # Spot price and volatility from yfinance
│   ├── dashboard.py         # Streamlit UI
│   └── utils.py             # Helpers (e.g. days to expiry)
├── notebooks/
│   └── analysis.ipynb       # Exploratory testing + plots
├── tests/
│   └── test_pricing.py      # Unit tests for formulae
├── main.py                  # Entry point for running CLI version
├── requirements.txt         # All required packages
├── directory_structure.txt  # Maintains directory structure in an organized way
├── .gitignore               # Standard .gitignore for python projects
└── README.md                # This file

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⚙️ How to Run

1. Clone the repository on your local machine

git clone https://github.com/Mozeel-V/options-pricing-engine.git

2. Install dependencies in your python environment

pip install -r requirements.txt

3. Run the Streamlit dashboard (recommended)

streamlit run app/dashboard.py

This opens a web UI for interactive option pricing and visualization.

4. Alternatively, you can run the CLI (Command-Line Interface) tool (basic)

python main.py

Follow the prompts to input ticker, strike, expiry, risk-free rate, and option type and get option prices.

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🧮 Pricing Models Used

• Black-Scholes Model (for European Options)
  Calculates theoretical prices for European call and put options using a closed-form formula.
  Inputs: Spot Price (S), Strike Price (K), Time to Expiry (T), Risk-Free Rate (r), Volatility (σ), Option Type (call/put)
  Volatility is computed from 1-year historical data as the annualized standard deviation of daily log returns.

• Binomial Tree Model (for American & European Options)
  Uses a discrete-time lattice to model option price evolution, suitable for options with early exercise features like American options.
  Customizable steps allow precision tuning.

• Monte Carlo Simulation (for Exotic Options)
  Supports pricing of Asian options (average price) and Barrier options (knock-in, knock-out) by simulating many possible stock price paths and averaging discounted payoffs.
  Flexible and powerful for options without closed-form solutions.

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📊 Visualization

Price vs Strike Price: Shows how option price changes when varying strike price, holding other parameters fixed.

Price vs Volatility: Shows sensitivity of option price to changes in volatility, keeping other inputs constant.

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✅ How to run the tests

Install pytest if not already:

pip install pytest

Run from the root directory:

pytest tests/

It’ll run all tests and show PASSED if our pricing logic is correct

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🔗 Notes

• Risk-free rate input is manual because real-time fetching requires paid APIs.

• Expiry date format must be YYYY-MM-DD.

• Monte Carlo options require longer run-time (use ~5000+ simulations).

• The project can be extended with Greeks calculation and visualizations, implied volatility, stochastic volatility modelling and more.

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🤝 Contributions

Feel free to fork, raise issues, or submit PRs to improve this project!

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📝 Author

Mozeel Vanwani | IIT Kharagpur CSE

Email: [vanwani.mozeel@gmail.com]