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https://github.com/luphord/nelson_siegel_svensson
Implementation of the Nelson-Siegel-Svensson interest rate curve model.
https://github.com/luphord/nelson_siegel_svensson
curve-fitting finance interest-rates python3 yield-curve
Last synced: 3 days ago
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Implementation of the Nelson-Siegel-Svensson interest rate curve model.
- Host: GitHub
- URL: https://github.com/luphord/nelson_siegel_svensson
- Owner: luphord
- License: mit
- Created: 2019-02-05T16:29:03.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-11-02T21:42:15.000Z (about 1 year ago)
- Last Synced: 2024-12-23T17:12:12.266Z (11 days ago)
- Topics: curve-fitting, finance, interest-rates, python3, yield-curve
- Language: Python
- Size: 229 KB
- Stars: 115
- Watchers: 1
- Forks: 41
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Authors: AUTHORS.rst
Awesome Lists containing this project
README
============================
Nelson-Siegel-Svensson Model
============================
.. image:: https://img.shields.io/pypi/v/nelson_siegel_svensson.svg
:target: https://pypi.python.org/pypi/nelson_siegel_svensson
.. image:: https://github.com/luphord/nelson_siegel_svensson/actions/workflows/python-package.yml/badge.svg
:target: https://github.com/luphord/nelson_siegel_svensson/actions
.. image:: https://readthedocs.org/projects/nelson-siegel-svensson/badge/?version=latest
:target: https://nelson-siegel-svensson.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
Implementation of the Nelson-Siegel-Svensson interest rate curve model in Python.
.. code-block:: python
from nelson_siegel_svensson import NelsonSiegelSvenssonCurve
import numpy as np
from matplotlib.pyplot import plot
y = NelsonSiegelSvenssonCurve(0.028, -0.03, -0.04, -0.015, 1.1, 4.0)
t = np.linspace(0, 20, 100)
plot(t, y(t))
.. image:: docs/_static/an_example_nelson-siegel-svensson-curve.png
* Free software: MIT license
* Python 3.7 or later supported
* Documentation: https://nelson-siegel-svensson.readthedocs.io.
Features
--------
* Python implementation of the Nelson-Siegel curve (three factors)
* Python implementation of the Nelson-Siegel-Svensson curve (four factors)
* Methods for zero and forward rates (as vectorized functions of time points)
* Methods for the factors (as vectorized function of time points)
* Calibration based on ordinary least squares (OLS) for betas and nonlinear optimization for taus
* Simple command line interface (CLI) for evaluating, calibrating and plotting curves
Calibration
-----------
In order to calibrate a curve to given data you can use the `calibrate_ns_ols` and
`calibrate_nss_ols` functions in the `calibrate` module:
.. code-block:: python
import numpy as np
from nelson_siegel_svensson.calibrate import calibrate_ns_ols
t = np.array([0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0])
y = np.array([0.01, 0.011, 0.013, 0.016, 0.019, 0.021, 0.026, 0.03, 0.035, 0.037, 0.038, 0.04])
curve, status = calibrate_ns_ols(t, y, tau0=1.0) # starting value of 1.0 for the optimization of tau
assert status.success
print(curve)
which gives the following output:
.. code-block:: python
NelsonSiegelCurve(beta0=0.04201739383636799, beta1=-0.031829031569430594, beta2=-0.026797319779108236, tau=1.7170972656534174)
.. image:: docs/_static/calibrated_nelson-siegel-curve.png
Command Line interface
----------------------
`nelson_siegel_svensson` provides basic functionality using a command line interface (CLI):
.. code-block:: console
Usage: nelson_siegel_svensson [OPTIONS] COMMAND [ARGS]...
Commandline interface for nelson_siegel_svensson.
Options:
--help Show this message and exit.
Commands:
calibrate Calibrate a curve to the given data points.
evaluate Evaluate a curve at given points.
plot Plot a curve at given points.
In order to calibrate a curve to given data points on the command line, try
.. code-block:: console
nelson_siegel_svensson calibrate -t '[0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0]' -y '[0.01, 0.011, 0.013, 0.016, 0.019, 0.021, 0.026, 0.03, 0.035, 0.037, 0.038, 0.04]' --nelson-siegel --initial-tau1 1.0
which gives
.. code-block:: console
{"beta0": 0.042017393764903765, "beta1": -0.03182903146166806, "beta2": -0.026797320316066128, "tau": 1.717097232403383}
This curve can then be evaluated on the command line using
.. code-block:: console
nelson_siegel_svensson evaluate -c '{"beta0": 0.042017393764903765, "beta1": -0.03182903146166806, "beta2": -0.026797320316066128, "tau": 1.717097232403383}' -t '[0, 1, 2, 3]'
resulting in
.. code-block:: console
[0.010188362303235707, 0.012547870204470839, 0.01574855552855885, 0.01897955804146046]
And finally, the curve can be plotted with
.. code-block:: console
nelson_siegel_svensson plot -o cli_plot_example.png -c '{"beta0": 0.042017393764903765, "beta1": -0.03182903146166806, "beta2": -0.026797320316066128, "tau": 1.717097232403383}'
.. image:: docs/_static/cli_plot_example.png
Note that the quoting in the above commands prevents `bash` from evalutating the JSON-based parameters. Depending on your shell, you may require a different quoting mechanism.
Credits
-------
Main developer is luphord_.
.. _luphord: https://github.com/luphord
This package was prepared with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage