tensorquant 0.0.4

TensorFlow-Python financial library

TensorQuant

TensorQuant is a Python financial library designed to provide a practical, Python-based implementations. Leveraging Tensor arrays, TensorQuant supports pricing, intensive risk management computations, and algorithmic differentiation. You can explore examples and use cases in the playground repository with Jupyter notebooks.

It is particularly valuable in academic settings, such as the Finance Master courses at the University of Siena, where students gain hands-on experience with financial libraries and object-oriented programming.

Many of TensorQuant's components draw inspiration from the renowned QuantLib library. Our thanks go to the QuantLib community for their contributions to financial modeling. While simplified for ease of use, TensorQuant aims to strike a balance between ease of understanding and professional architecture.

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📑 Table of Contents

• 🌟 Features
• 🛠️ Installation
• 🚀 Usage
• 📝 License
• 📧 Contact

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🌟 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.

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🛠️ Installation

To install TensorQuant, use pip:

pip install tensorquant

Alternatively, clone the repository and install manually:

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

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🚀 Usage

To get started using TensorQuant, here are some resources:

Examples

• Visit the playground for Jupyter notebooks containing examples and use cases.

Documentation

• The ReadTheDocs page provides API references and comprehensive documentation.

GitHub Repository

• Check out the open-source code on GitHub.

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📝 License

TensorQuant is licensed under the GPL-3.0 License. See the LICENSE file for more information.

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📧 Contact

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

• Email: [e-mail]

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Happy computing!