Jupyter-based quantitative finance analyses and tools from Winter 2024 internship projects, including return-hold performance assessment and risk parity portfolio construction.
This repository contains two core analyses completed during a Winter 2024 quantitative finance internship:
- Return-Hold Assessment: Evaluating performance persistence of assets under various holding periods.
- Risk Parity Calculation: Building and backtesting a risk-parity portfolio, balancing asset volatilities.
Each analysis is delivered as a self-contained Jupyter Notebook with narrative explanations, charts, and code.
| Notebook | Description |
|---|---|
return-hold-assessment.ipynb |
Assess performance metrics across different hold periods. |
risk-parity.ipynb |
Construct and backtest a volatility-weighted portfolio. |
- Python 3.8+
- Jupyter Notebook or JupyterLab
- Core libraries:
pandasnumpymatplotlibscipyyfinance(or other data-fetch library)
-
Clone this repository:
git clone https://github.com/SuYirouCrystal/QuantFund.git cd QuantFund -
(Optional) Create and activate a virtual environment:
python3 -m venv env source env/bin/activate -
Install dependencies:
pip install -r requirements.txt
-
(Optional) If requirements.txt is not present, install manually:
pip install pandas numpy matplotlib scipy yfinance
- Launch Jupyter Notebook:
jupyter notebook
- Open and run each notebook cell by cell.
- Modify parameters (tickers, dates, weights) at the top of each notebook to explore different scenarios.
QuantFund/
├── return-hold-assessment.ipynb
├── risk-parity.ipynb
├── LICENSE.md
├── README.md
└── requirements.txt
Contributions and suggestions are welcome! Please open an issue or submit a pull request with improvements, bug fixes, or additional analyses.
This project is licensed under the MIT License.