Metadata-Version: 2.4
Name: tensorquant
Version: 1.0.0
Summary: TensorFlow-Python financial library
Home-page: https://github.com/andrea220/tQuant
Author: Andrea Carapelli
Author-email: carapelliandrea@email.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: tensorflow>=2.0
Requires-Dist: python-dateutil==2.9.0.post0
Requires-Dist: pandas==2.2.2
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary


# TensorQuant

![TensorQuant Logo](https://img.shields.io/badge/TensorQuant-v0.0.3-blue.svg)
![Python](https://img.shields.io/badge/python-v3.10+-blue.svg)
![Build Status](https://img.shields.io/badge/build-passing-brightgreen.svg)
![License](https://img.shields.io/badge/license-MIT-green.svg)

**TensorQuant** is a Python financial library that uses TensorFlow as its computational engine. Leveraging tensor arrays, TensorQuant supports pricing, intensive risk management computations, and algorithmic differentiation. You can explore examples and use cases in the [**playground repository**](https://github.com/andrea220/tqPlayground) with Jupyter notebooks. For detailed API references and comprehensive documentation, visit the [**ReadTheDocs**](https://tquant.readthedocs.io/en/latest/index.html) page. 

It is particularly valuable in academic settings, such as the [Finance Master courses at the University of Siena](https://finance.unisi.it/it), where students gain hands-on experience with financial libraries and object-oriented programming. At the same time, TensorQuant is designed to support fast prototyping, replication of academic and industry results, and professional-grade applications.

TensorQuant aims to strike a balance between ease of understanding and professional architecture: it is easy to use and extend, while remaining reliable, fast, and robust for financial modeling and risk management.

---

## 📑 Table of Contents

- [🌟 Features](#-features)
- [🛠️ Installation](#%EF%B8%8F-installation)
- [🚀 Usage](#-usage)
- [📝 License](#-license)
- [📧 Contact](#-contact)

---

## 🌟 Features

- **Tensor Array Operations**: Efficient handling and manipulation of tensor arrays for financial data.
- **Derivative Pricing**: Pricing financial derivatives.
- **Algorithmic Differentiation**: Automatic differentiation for optimization and sensitivity analysis.
- **Stochastic Models**: Simulations and solver tools for financial modeling.
- **Extensibility**: Easy to extend and customize for a wide range of financial applications.

---

## 🛠️ Installation

To install `TensorQuant`, use pip:

```bash
pip install tensorquant
```

Alternatively, clone the repository and install manually:

```bash
git clone https://github.com/andrea220/tQuant.git
cd tQuant
pip install .
```

---

## 🚀 Usage

To get started using `TensorQuant`, here are some resources:

### Examples
- Visit the [**`Playground`**](https://github.com/andrea220/tqPlayground) for Jupyter notebooks containing examples and use cases.

### Documentation
- The [**`ReadTheDocs`**](https://tquant.readthedocs.io/en/latest/index.html) page provides API references and comprehensive documentation.

### GitHub Repository
- Check out the open-source code on [**GitHub**](https://github.com/andrea220/tQuant).

---

## 📝 License

`TensorQuant` is licensed under the GPL-3.0 License. See the [LICENSE](LICENSE) file for more information.

---

## 📧 Contact

For any questions or suggestions, feel free to reach out:

- **Email**: [carapelliandrea@gmail.com](mailto:carapelliandrea@gmail.com)

---

Happy computing!
