https://github.com/nabilshadman/monte-carlo-simulation-equity-trading
Monte Carlo simulation toolkit for equity trading, utilizing GBM and Pareto distributions to model price movements and trading volumes
https://github.com/nabilshadman/monte-carlo-simulation-equity-trading
applied-mathematics applied-probability computational-finance computational-science equity-trading geometric-brownian-motion lognormal-distribution mathematical-finance monte-carlo pareto-distributions quantitative-finance scientific-computing
Last synced: 24 days ago
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Monte Carlo simulation toolkit for equity trading, utilizing GBM and Pareto distributions to model price movements and trading volumes
- Host: GitHub
- URL: https://github.com/nabilshadman/monte-carlo-simulation-equity-trading
- Owner: nabilshadman
- License: mit
- Created: 2023-08-21T08:27:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-05T09:39:07.000Z (5 months ago)
- Last Synced: 2025-02-09T13:13:00.577Z (3 months ago)
- Topics: applied-mathematics, applied-probability, computational-finance, computational-science, equity-trading, geometric-brownian-motion, lognormal-distribution, mathematical-finance, monte-carlo, pareto-distributions, quantitative-finance, scientific-computing
- Language: Jupyter Notebook
- Homepage:
- Size: 650 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Monte Carlo Simulation in Equity Trading
[](https://opensource.org/licenses/MIT)
## Overview
This project explores the application of [**Monte Carlo**](https://en.wikipedia.org/wiki/Monte_Carlo_method) simulations in equity trading, leveraging statistical distributions to model financial behaviors.
The methodologies implemented include:
- **Geometric Brownian Motion (GBM)**: Simulating equity price paths
- **Pareto Distribution**: Simulating equity trading volumes
Geometric Brownian Motion (GBM)
Pareto Distribution
Simulating equity price paths
Simulating equity trading volumes
### Tech Stack
- **Python Libraries**:
- scipy
- numpy
- pandas
- matplotlib
- **Development Environment**: Jupyter Notebook
- **Version Control**: GitHub
## Notebooks
### 1. [**Lognormal Distribution**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/lognormal_distribution.ipynb)
Provides a Python implementation of the **lognormal distribution**, a key component in modeling financial price movements.
- Visualizes histogram, Probability Density Function (PDF), and Cumulative Distribution Function (CDF).
- Foundation for simulating equity prices.
### 2. [**Pareto Distribution**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/pareto_distribution.ipynb)
Implements the **Pareto distribution**, often used for modeling trading volumes.
- Visualizes histogram and PDF.
- Forms the basis for simulating trading volume.
### 3. [**Simulating Equity Prices**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/simulating_equity_prices.ipynb)
Simulates **equity price paths** using the GBM process.
- Explains the relationship between periodic returns and lognormal price distributions.
- Uses Python’s NumPy for efficient computation.
### 4. [**Simulating Trading Volume**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/simulating_trading_volume.ipynb)
Simulates **trading volumes** with the Pareto distribution.
- Generates realistic equity volumes lacking autocorrelation and price dependency.
---
## Environment
### Recommended Setup
For seamless execution, use the [**Anaconda Distribution**](https://docs.anaconda.com/free/anaconda/index.html), which simplifies dependency management and ensures compatibility.
1. Download and install Anaconda from [here](https://www.anaconda.com/download).
2. Open **Anaconda Navigator** and launch Jupyter Notebook.
3. Navigate to the project directory to begin.
---
## Execution
### Running the Notebooks
1. Open a notebook in Jupyter.
2. In the toolbar, select **Run** → **Run All Cells** to execute sequentially.
### Suggested Order
For those new to Monte Carlo simulations in finance, follow this order:
1. [**lognormal_distribution.ipynb**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/lognormal_distribution.ipynb)
2. [**pareto_distribution.ipynb**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/pareto_distribution.ipynb)
3. [**simulating_equity_prices.ipynb**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/simulating_equity_prices.ipynb)
4. [**simulating_trading_volume.ipynb**](https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/blob/main/simulating_trading_volume.ipynb)
For detailed instructions, refer to the [Jupyter documentation](https://docs.jupyter.org/en/latest/).
## Contributing
We welcome contributions! Please feel free to submit pull requests or open issues for any improvements.
## License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE.txt) file for details.
## Citation
If you use this work in your research, please cite:
```bibtex
@misc{monte-carlo-equity-trading,
author = {Shadman, Nabil},
title = {Monte Carlo Simulation in Equity Trading},
year = {2023},
publisher = {GitHub},
url = {https://github.com/nabilshadman/monte-carlo-simulation-equity-trading/}
}
```