This project simulates stock market prices using data simulation techniques. It is built as part of a data simulation course project and aims to provide an understanding of how stock prices can be modeled using various mathematical and statistical methods.
- Simulates stock price movements over time
- Implements different models like:
- Random Walk
- Geometric Brownian Motion (GBM)
- Visualizes simulated data using charts
- Configurable parameters like volatility, drift, and time horizon
- Python
- NumPy
- Pandas
- Matplotlib / Seaborn
- Jupyter Notebook (for visualization and experimentation)