google/tfquantfinance
Highperformance TensorFlow library for quantitative finance.
repo name  google/tfquantfinance 
repo link  https://github.com/google/tfquantfinance 
homepage  
language  Python 
size (curr.)  5958 kB 
stars (curr.)  929 
created  20190724 
license  Apache License 2.0 
TF Quant Finance: TensorFlow based Quant Finance Library
Table of contents
 Introduction
 Installation
 TensorFlow training
 Development roadmap
 Examples
 Contributing
 Development
 Community
 Disclaimers
 License
Introduction
This library provides highperformance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, midlevel methods, and specific pricing models. The coverage is being expanded over the next few months.
The library is structured along three tiers:

Foundational methods. Core mathematical methods  optimisation, interpolation, root finders, linear algebra, random and quasirandom number generation, etc.

Midlevel methods. ODE & PDE solvers, Ito process framework, Diffusion Path Generators, Copula samplers etc.

Pricing methods and other quant finance specific utilities. Specific Pricing models (e.g Local Vol (LV), Stochastic Vol (SV), Stochastic Local Vol (SLV), HullWhite (HW)) and their calibration. Rate curve building, payoff descriptions and schedule generation.
We aim for the library components to be easily accessible at each level. Each layer will be accompanied by many examples which can be run independently of higher level components.
Installation
The easiest way to get started with the library is via the pip package.
Note that library requires Python 3 and Tensorflow >= 2.1.
First please install the most recent version of TensorFlow by following the TensorFlow installation instructions. For example, you could install TensorFlow using
pip3 install upgrade tensorflow
Then run
pip3 install upgrade tfquantfinance
You maybe also have to use the option user
.
TensorFlow training
If you are not familiar with TensorFlow, a good place to get started is with the following selfstudy introduction to TensorFlow notebooks:
 Introduction to TensorFlow Part 1  Basics.
 Introduction to TensorFlow Part 2  Debugging and Control Flow.
 Introduction to TensorFlow Part 3  Advanced Tensor Manipulation.
Development roadmap
We are working on expanding the coverage of the library. Areas under active development are:
 Ito Processes: Framework for defining Ito processes. Includes methods for sampling paths from a process and for solving the associated backward Kolmogorov equation.
 Implementation of the following specific processes/models:
 Brownian Motion
 Geometric Brownian Motion
 OrnsteinUhlenbeck
 Single factor Hull White model
 Heston model
 Local volatility model.
 Quadratic Local Vol model.
 SABR model
 Copulas: Support for defining and sampling from copulas.
 Model Calibration:
 Dupire local vol calibration.
 SABR model calibration.
 Rate curve fitting: HaganWest algorithm for yield curve bootstrapping and the Monotone Convex interpolation scheme.
 Support for dates, daycount conventions, holidays, etc.
Examples
See tf_quant_finance/examples/
for endtoend examples. It includes tutorial notebooks such as:
 American Option pricing under the BlackScholes model
 Monte Carlo via Euler Scheme
 Black Scholes: Price and Implied Vol.
 Root search using Brent’s method
 Optimization
The above links will open Jupyter Notebooks in Colab.
Contributing
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.
Development
This section is meant for developers who want to contribute code to the library. If you are only interested in using the library, please follow the instructions in the Installation section.
Development dependencies
This library has the following dependencies:
 Bazel
 Python 3 (Bazel uses Python 3 by default)
 TensorFlow nightly build (most functions should work with TensorFLow 2.1)
 TensorFlow Probability nightly build
 Numpy version 1.16 or higher
 Attrs
This library requires the Bazel build system. Please follow the Bazel installation instructions for your platform.
You can install TensorFlow and related dependencies using the pip3 install
command:
pip3 install upgrade tfnightly tfpnightly numpy==1.16.0 attrs
Commonly used commands
Clone the GitHub repository:
git clone https://github.com/google/tfquantfinance.git
After you run
cd tf_quant_finance
you can execute tests using the bazel test
command. For example,
bazel test tf_quant_finance/math/random_ops/sobol:sobol_test
will run tests in sobol_test.py .
Tests will be run using the Python version 3. Please make sure that you can
run import tensorflow
in the Python 3 shell, otherwise tests might fail.
Docker images
The official TF Quant Finance Docker images are located in the gcr.io container repository.
Images are tagged using the GitHub release version. Images contain all development dependencies. See Dockerfile for details.
You can pull the latest Docker image using
sudo docker pull gcr.io/tfquantfinanceimages/tfquantfinance
To start a TF Quant Finance container, use the following command:
sudo docker run it gcr.io/tfquantfinanceimages/tfquantfinance
Building a custom pip package
The following commands will build custom pip package from source and install it:
# sudo aptget install bazel git python pythonpip rsync # For Ubuntu.
git clone https://github.com/google/tfquantfinance.git
cd tfquantfinance
bazel build :build_pip_pkg
./bazelbin/build_pip_pkg artifacts
pip install user upgrade artifacts/*.whl
Community

GitHub repository: Report bugs or make feature requests.

TensorFlow Blog: Stay up to date on content from the TensorFlow team and best articles from the community.

tfquantfinance@google.com: Open mailing list for discussion and questions of this library.

TensorFlow Probability: This library will leverage methods from TensorFlow Probability (TFP).
Disclaimers
This is not an officially supported Google product. This library is under active development. Interfaces may change at any time.
License
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.