Welcome to the Quantitative Finance repository, a comprehensive collection of projects exploring various topics in quantitative finance. This repository consolidates multiple standalone projects into a single organized structure for ease of access and better collaboration.
The repository is organized into folders based on categories of quantitative finance. Each folder contains the following:
- Code File: The main implementation in Python (or other relevant languages).
- PDF File: A detailed report or presentation of the project.
Quantitative-Finance/
├── Options_Pricing/
│ ├── american_options_pricing.ipynb
│ ├── american_options_pricing.pdf
├── Portfolio_Optimization/
│ ├── mean_variance_optimization.ipynb
│ ├── mean_variance_optimization.pdf
└── README.md
This repository currently includes the following projects:
- Options Pricing
- American Options Pricing using the Binomial Method
- European Options Pricing with Monte Carlo Simulations
- Portfolio Optimization
- Mean-Variance Optimization
- Efficient Frontier Visualization
- Time Series Analysis
- ARIMA Modeling for Stock Price Prediction
- GARCH Models for Volatility Forecasting
More projects will be added over time as part of the ongoing clean-up and migration process.
To explore the projects, you can:
- Clone this repository:
git clone https://github.com/yourusername/Quantitative-Finance.git cd Quantitative-Finance - Navigate to the project folder of interest.
- Open the provided Jupyter notebooks (
.ipynb) for step-by-step explanations and implementations.
We welcome contributions to improve or expand this collection. To contribute:
- Fork the repository.
- Create a new branch for your feature or fix.
- Submit a pull request with a detailed explanation of your changes.
This repository is licensed under the MIT License. You are free to use, modify, and distribute the content with proper attribution.
Special thanks to everyone who provided feedback and suggestions to improve the quality of this repository. For any questions or collaboration inquiries, feel free to reach out.
Author: Usama Buttar
Contact: LinkedIn