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    "info": {
        "author": "luphord",
        "author_email": "luphord@protonmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.7"
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        "description": "============================\nNelson-Siegel-Svensson Model\n============================\n\n\n.. image:: https://img.shields.io/pypi/v/nelson_siegel_svensson.svg\n        :target: https://pypi.python.org/pypi/nelson_siegel_svensson\n\n.. image:: https://img.shields.io/travis/luphord/nelson_siegel_svensson.svg\n        :target: https://travis-ci.org/luphord/nelson_siegel_svensson\n\n.. image:: https://readthedocs.org/projects/nelson-siegel-svensson/badge/?version=latest\n        :target: https://nelson-siegel-svensson.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\n\n\nImplementation of the Nelson-Siegel-Svensson interest rate curve model in Python.\n\n.. code-block:: python\n\n        from nelson_siegel_svensson import NelsonSiegelSvenssonCurve\n        import numpy as np\n        from matplotlib.pyplot import plot\n\n        y = NelsonSiegelSvenssonCurve(0.028, -0.03, -0.04, -0.015, 1.1, 4.0)\n        t = np.linspace(0, 20, 100)\n        plot(t, y(t))\n\n.. image:: docs/_static/an_example_nelson-siegel-svensson-curve.png\n\n* Free software: MIT license\n* Documentation: https://nelson-siegel-svensson.readthedocs.io.\n\n\nFeatures\n--------\n\n* Python implementation of the Nelson-Siegel curve (three factors)\n* Python implementation of the Nelson-Siegel-Svensson curve (four factors)\n* Methods for zero and forward rates (as vectorized functions of time points)\n* Methods for the factors (as vectorized function of time points)\n* Calibration based on ordinary least squares (OLS) for betas and nonlinear optimization for taus\n* Simple command line interface (CLI) for evaluating, calibrating and plotting curves\n\n\nCalibration\n-----------\n\nIn order to calibrate a curve to given data you can use the `calibrate_ns_ols` and\n`calibrate_nss_ols` functions in the `calibrate` module:\n\n.. code-block:: python\n\n        import numpy as np\n        from nelson_siegel_svensson.calibrate import calibrate_ns_ols\n\n        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])\n        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])\n\n        curve, status = calibrate_ns_ols(t, y, tau0=1.0)  # starting value of 1.0 for the optimization of tau\n        assert status.success\n        print(curve)\n\nwhich gives the following output:\n\n.. code-block:: python\n\n        NelsonSiegelCurve(beta0=0.04201739383636799, beta1=-0.031829031569430594, beta2=-0.026797319779108236, tau=1.7170972656534174)\n\n.. image:: docs/_static/calibrated_nelson-siegel-curve.png\n\n\nCommand Line interface\n----------------------\n\n`nelson_siegel_svensson` provides basic functionality using a command line interface (CLI):\n\n.. code-block:: console\n\n        Usage: nelson_siegel_svensson [OPTIONS] COMMAND [ARGS]...\n\n        Commandline interface for nelson_siegel_svensson.\n\n        Options:\n        --help  Show this message and exit.\n\n        Commands:\n        calibrate  Calibrate a curve to the given data points.\n        evaluate   Evaluate a curve at given points.\n        plot       Plot a curve at given points.\n\n\nIn order to calibrate a curve to given data points on the command line, try\n\n.. code-block:: console\n\n        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\n\nwhich gives\n\n.. code-block:: console\n\n        {\"beta0\": 0.042017393764903765, \"beta1\": -0.03182903146166806, \"beta2\": -0.026797320316066128, \"tau\": 1.717097232403383}\n\nThis curve can then be evaluated on the command line using\n\n.. code-block:: console\n\n        nelson_siegel_svensson evaluate -c '{\"beta0\": 0.042017393764903765, \"beta1\": -0.03182903146166806, \"beta2\": -0.026797320316066128, \"tau\": 1.717097232403383}' -t '[0, 1, 2, 3]'\n\nresulting in\n\n.. code-block:: console\n\n        [0.010188362303235707, 0.012547870204470839, 0.01574855552855885, 0.01897955804146046]\n\nAnd finally, the curve can be plotted with\n\n.. code-block:: console\n\n        nelson_siegel_svensson plot -o cli_plot_example.png -c '{\"beta0\": 0.042017393764903765, \"beta1\": -0.03182903146166806, \"beta2\": -0.026797320316066128, \"tau\": 1.717097232403383}'\n\n.. image:: docs/_static/cli_plot_example.png\n\nNote 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.\n\nCredits\n-------\n\nMain developer is luphord_.\n\n.. _luphord: https://github.com/luphord\n\nThis package was prepared with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.4.0 (2019-07-08)\n------------------\n\n* Simple command line interface (CLI) supporting curve evaluation, calibration and plotting\n* Added more documentation\n\n0.3.0 (2019-03-17)\n------------------\n\n* Added type annotations\n\n0.2.0 (2019-02-20)\n------------------\n\n* Ordinary least squares based calibration of Nelson-Siegel-Svensson\n* Ordinary least squares based calibration of Nelson-Siegel\n* A little bit of usage documentation\n\n0.1.0 (2019-02-13)\n------------------\n\n* First release on PyPI.\n\n\n",
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