Skip to main content

TensorFlow-Python financial library

Project description

TensorQuant

TensorQuant Logo Python Build Status License

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. For detailed API references and comprehensive documentation, visit the ReadTheDocs page.

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.


📑 Table of Contents


🌟 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:

pip install tensorquant

Alternatively, clone the repository and install manually:

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 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.

📝 License

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


📧 Contact

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


Happy computing!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tensorquant-0.0.6.tar.gz (52.2 kB view details)

Uploaded Source

Built Distribution

tensorquant-0.0.6-py3-none-any.whl (70.0 kB view details)

Uploaded Python 3

File details

Details for the file tensorquant-0.0.6.tar.gz.

File metadata

  • Download URL: tensorquant-0.0.6.tar.gz
  • Upload date:
  • Size: 52.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.7

File hashes

Hashes for tensorquant-0.0.6.tar.gz
Algorithm Hash digest
SHA256 9f82c1d02af0925fddf3e30b9e1956167115b2511968c700e69a34ca04d6ed99
MD5 7828d4f3c242de715851c78991f4f5f5
BLAKE2b-256 fd571dcc7993ab179d489c484902e6f24782801a40a35de0a2eeba8da45c6732

See more details on using hashes here.

File details

Details for the file tensorquant-0.0.6-py3-none-any.whl.

File metadata

  • Download URL: tensorquant-0.0.6-py3-none-any.whl
  • Upload date:
  • Size: 70.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.7

File hashes

Hashes for tensorquant-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 4da92daffe643c2b9b27eba628a80f55a6888dc99fb4610eb383cca1149ddaaf
MD5 5c3a516262c81ce9af0c2942b567e3a4
BLAKE2b-256 0bc975c40145dce5bbcf7e8cca04e4f004d11387ee47567e3befb2885f1dd3a0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page