Geometric Brownian Motion Stock Simulator 📈

A quantitative finance tool that uses Monte Carlo simulations to predict future stock price paths based on historical volatility and drift. Built with Python.

🚀 Overview

This project applies the Geometric Brownian Motion (GBM) model—the mathematical foundation of the Black-Scholes equation—to real-time financial data. It allows users to:

• Fetch live data for any stock/index (default: NIFTY 50).
• Simulate 100+ potential future price paths over 1 year.
• Calculate key risk metrics like Value at Risk (VaR) and expected returns.

🛠️ Technology Stack

• Python: Core logic.
• yfinance: Real-time market data API.
• NumPy & Pandas: Vectorized financial calculations.
• Matplotlib: Visualization of simulation paths.

📊 How to Run

1. Clone the repository:

   git clone [https://github.com/YOUR_USERNAME/gbm-stock-simulator.git](https://github.com/YOUR_USERNAME/gbm-stock-simulator.git)
   

Disclaimer: This tool is for educational purposes only to demonstrate the mathematics of volatility. It does not constitute investment advice or a recommendation to buy/sell any securities.