This package implements specialized autoencoders and related classical methods for performing dimension reduction in quant models of financial markets. Potential uses include investment strategy research, portfolio valuation, and risk management.
Install using:
pip install aencNamespace aenc.core implements autoencoders and related
classical methods, including generic (such as PCA) and specialized
(such as Nelson-Siegel).
The implementation uses PyTorch and can be easily ported to TensorFlow 2 and other machine learning frameworks that support dynamic computational graphs.
Namespace aenc.dummy includes dummy objects and generators for dummy market
data for testing purposes. To perform testing or training on real
market data, provide your own historical market data files in the same
format as the dummy data files, or use pretrained components.
Namespace aenc.pretrained includes pretrained components to avoid lengthy
test execution time. Use flags to ignore pretrained parameters
and perform training from scratch (calculation time will increase).
The code in this project is licensed under Apache 2.0 license. See LICENSE for more information.
Each individual contributor holds copyright over their contributions to the project. The project versioning is the sole means of recording all such contributions and copyright details. Specifying corporate affiliation or work email along with the commit shall have no bearing on copyright ownership and does not constitute copyright assignment to the employer. Submitting a contribution to this project constitutes your acceptance of these terms.
Because individual contributions are often changes to the existing code, copyright notices in project files must specify The Project Contributors and never an individual copyright holder.
- Alexander Sokol, Autoencoder Market Models for Interest Rates, SSRN Working Paper https://ssrn.com/abstract=4300756
- GitHub repository: https://github.com/compatibl/aenc