Options Strategy & Volatility Surface Visualizer

A sophisticated quantitative finance tool built with Python to model and visualize two critical aspects of options trading: strategy payoffs and market-implied volatility.

Payoff Diagram Screenshot

Volatility Surface Screenshot

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Key Features

1. Options Strategy Payoff Visualizer:

   • Dynamically build multi-leg options strategies (e.g., Straddles, Iron Condors, Spreads).
   • Generates an interactive 2D Profit/Loss diagram.
   • Automatically calculates and displays key metrics: Maximum Profit, Maximum Loss, and Break-Even Point(s).

2. Implied Volatility (IV) Surface Visualizer:

   • Fetches the complete options chain for any user-specified stock ticker from live market data.
   • Calculates the implied volatility for every contract using the Black-Scholes-Merton model and a numerical root-finding algorithm.
   • Renders an interactive 3D surface plot, visualizing the volatility smile/skew across all strikes and expirations.

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Core Concepts & Skills Demonstrated

This project showcases a deep understanding of concepts central to quantitative finance and data science:

• Quantitative Finance: Derivatives pricing (Black-Scholes-Merton), multi-leg P/L calculation, options greeks (implied volatility/vega).
• Numerical Methods: Implementation of a numerical solver (scipy.optimize.brentq) to find the root of a complex, non-linear equation.
• Data Engineering: Fetching, parsing, cleaning, and structuring complex, nested financial data (options chains) into a usable format.
• Advanced Data Visualization: Conveying complex, multi-dimensional financial data in an intuitive and interactive format using Plotly.

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Tech Stack

• Language: Python
• Data Analysis & Computation: Pandas, NumPy, SciPy
• Data Acquisition: yfinance
• Visualization & UI: Plotly, Streamlit

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Setup and Installation

1. Clone the repository:

   git clone https://github.com/dsarkar10/YOUR-REPO-NAME.git
   cd YOUR-REPO-NAME

2. Create and activate a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

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How to Run

From the project's root directory, run the following command in your terminal:

streamlit run app.py