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59 changes: 59 additions & 0 deletions README.md
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# PorQua


**PorQua** is an advanced **Python** library for **portfolio optimization** and **index replication**, designed as part of the [GeomScale project](https://github.com/GeomScale). It provides **efficient tools for financial data analysis, portfolio management, and asset selection**.

![License: LGPL-3.0](https://img.shields.io/badge/license-LGPL--3.0-blue.svg)
![Docs](https://img.shields.io/badge/docs-passing-brightgreen)
![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)
![Chat on Gitter](https://badges.gitter.im/GeomScale/PorQua.svg)
<!-- ![Build Status](https://img.shields.io/github/actions/workflow/status/GeomScale/PorQua/build.yml?branch=main) -->

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## 📦 Installation

Clone the repository:

```sh
git clone https://github.com/GeomScale/PorQua.git
cd PorQua
```

### 🧪 Run a Quick Test

```sh
python -m unittest test/tests_quadratic_program.py
```

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<!-- ## 📘 Documentation & Tutorials

- 📄 **[Package Documentation](https://github.com/GeomScale/PorQua/wiki)** -->


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## 📊 Examples

💡 Here are some notebooks showing potential use cases of the PorQua library.

| Feature | Example |
|---------|---------|
| **Backtesting a portfolio strategy using historical data** | [🔗 Backtesting](example/backtest.ipynb) |
| **Evaluating different quadratic programming (QP) solvers** | [🔗 Compare solver](example/compare_solver.ipynb) |
| **Index Replication (Using LSTM model)** | [🔗 Index replication](example/index_replication.ipynb) |
| **Time series forecasting using LSTM model** | [🔗 LSTM for prediction](example/lstm.ipynb) |
| **Time series forecasting using linear regression and XGBoost** | [🔗 ML forecasting](example/ml.ipynb) |
| **Probit and Logit ordinal regression models** | [🔗 Ordinal regression](example/ordinal_regression.ipynb) |
---



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## 📜 License

You may redistribute or modify the software under the [GNU Lesser General Public License](LICENSE) as published by Free Software Foundation, either version 3 of the License, or (at your option) any later version. It is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY.
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