This library provides high-performance methods leveraging the hardware acceleration support and automatic differentation of TensorFlow. The initial library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. These will be significantly expanded over next months.
Our TensorFlow-based methods will be structured along three tiers:
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Foundational methods. Core mathematics methods - optimisation, root finders, linear algebra, random number generation, etc.
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Mid-level methods. ODE & PDE solvers, Diffusion Path Generators, Copula samplers etc.
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Pricing methods. Specific Pricing models (e.g Local Vol (LV), Stochastic Vol (SV), Stochastic Local Vol (SLV), Hull-White (HW))
Our existing examples will be made available here soon.
We're eager to collaborate with you! See CONTRIBUTING.md for a guide on how to contribute. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
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GitHub repository: Report bugs or make feature requests.
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TensorFlow Blog: Stay up to date on content from the TensorFlow team and best articles from the community.
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tf-quant-finance@google.com: Open mailing list for discussion and questions of this library.
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TensorFlow Probability: This library will leverage methods from TensorFlow Probability (TFP).
This is not an officially supported Google product. This library is under active development. Interfaces may change at any time.
This library is licensed under the Apache 2 license (see LICENSE). This library uses Sobol primitive polynomials and initial direction numbers which are licensed under the BSD license.