modelx


Namemodelx JSON
Version 0.28.0 PyPI version JSON
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home_pagehttps://github.com/fumitoh/modelx
SummaryBuild and run complex models composed of formulas and data
upload_time2024-12-08 09:20:55
maintainerNone
docs_urlNone
authorFumito Hamamura
requires_python>=3.7
licenseLGPLv3
keywords model development
VCS
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requirements No requirements were recorded.
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coveralls test coverage No coveralls.
            modelx
======
*Use Python like a spreadsheet!*

.. image:: https://github.com/fumitoh/modelx/actions/workflows/python-package.yml/badge.svg
    :target: https://github.com/fumitoh/modelx/actions/workflows/python-package.yml

.. image:: https://img.shields.io/pypi/pyversions/modelx
    :target: https://pypi.org/project/modelx/

.. image:: https://img.shields.io/pypi/v/modelx
    :target: https://pypi.org/project/modelx/

.. image:: https://img.shields.io/pypi/l/modelx
    :target: https://github.com/fumitoh/modelx/blob/master/LICENSE.LESSER.txt


.. Overview Begin

What is modelx?
---------------
**modelx** is a numerical computing tool that enables you to
use Python like a spreadsheet by quickly defining cached functions.
modelx is best suited for implementing mathematical models expressed
in a large system of recursive formulas,
in such fields as actuarial science, quantitative finance and risk management.

Feature highlights
------------------
**modelx** enables you to interactively
develop, run and debug complex models in smart ways.
modelx allows you to:

- Define cached functions as *Cells* objects by writing Python functions
- Quickly build object-oriented models, utilizing prototype-based inheritance and composition
- Quickly parameterize a set of formulas and get results for different parameters
- Trace formula dependency
- Import and use any Python modules, such as `Numpy`_, `pandas`_, `SciPy`_, `scikit-learn`_, etc..
- See formula traceback upon error and inspect local variables
- Save models to text files and version-control with `Git`_
- Save data such as pandas DataFrames in Excel or CSV files within models
- Auto-document saved models by Python documentation generators, such as `Sphinx`_
- Use Spyder with a plugin for modelx (spyder-modelx) to interface with modelx through GUI

.. _Numpy: https://numpy.org/
.. _pandas: https://pandas.pydata.org/
.. _SciPy: https://scipy.org/
.. _scikit-learn: https://scikit-learn.org/
.. _Git: https://git-scm.com/
.. _Sphinx: https://www.sphinx-doc.org


modelx sites
-------------

========================== ===============================================
Home page                  https://modelx.io
Blog                       https://modelx.io/allposts
Documentation site         https://docs.modelx.io
Development                https://github.com/fumitoh/modelx
Discussion Forum           https://github.com/fumitoh/modelx/discussions
modelx on PyPI             https://pypi.org/project/modelx/
========================== ===============================================


Who is modelx for?
------------------
**modelx** is designed to be domain agnostic, 
so it's useful for anyone in any field.
Especially, modelx is suited for modeling in such fields such as:

- Quantitative finance
- Risk management
- Actuarial science

**lifelib** (https://lifelib.io) is a library of actuarial and
financial models that are built on top of modelx.

How modelx works
----------------

Below is an example showing how to build a simple model using modelx.
The model performs a Monte Carlo simulation to generate 10,000
stochastic paths of a stock price that follow a geometric Brownian motion
and to price an European call option on the stock.

.. code-block:: python

    import modelx as mx
    import numpy as np

    model = mx.new_model()                  # Create a new Model named "Model1"
    space = model.new_space("MonteCarlo")   # Create a UserSpace named "MonteCralo"

    # Define names in MonteCarlo
    space.np = np
    space.M = 10000     # Number of scenarios
    space.T = 3         # Time to maturity in years
    space.N = 36        # Number of time steps
    space.S0 = 100      # S(0): Stock price at t=0
    space.r = 0.05      # Risk Free Rate
    space.sigma = 0.2   # Volatility
    space.K = 110       # Option Strike


    # Define Cells objects in MonteCarlo from function definitions
    @mx.defcells
    def std_norm_rand():
        gen = np.random.default_rng(1234)
        return gen.standard_normal(size=(N, M))


    @mx.defcells
    def stock(i):
        """Stock price at time t_i"""
        dt = T/N; t = dt * i
        if i == 0:
            return np.full(shape=M, fill_value=S0)
        else:
            epsilon = std_norm_rand()[i-1]
            return stock(i-1) * np.exp((r - 0.5 * sigma**2) * dt + sigma * epsilon * dt**0.5)


    @mx.defcells
    def call_opt():
        """Call option price by Monte Carlo"""
        return np.average(np.maximum(stock(N) - K, 0)) * np.exp(-r*T)

Running the model from IPython is as simple as calling a function:

.. code-block:: pycon

    >>> stock(space.N)      # Stock price at i=N i.e. t=T
    array([ 78.58406132,  59.01504804, 115.148291  , ..., 155.39335662,
            74.7907511 , 137.82730703])

    >>> call_opt()
    16.26919556999345

Changing a parameter is as simple as assigning a value to a name:

.. code-block:: pycon

    >>> space.K = 100   # Cache is cleared by this assignment

    >>> call_opt()    # New option price for the updated strike
    20.96156962064

You can even dynamically create multiple copies of *MonteCarlo*
with different combinations of ``r`` and ``sigma``,
by parameterizing *MonteCarlo* with ``r`` and ``sigma``:

.. code-block:: pycon

    >>> space.parameters = ("r", "sigma")   # Parameterize MonteCarlo with r and sigma

    >>> space[0.03, 0.15].call_opt()  # Dynamically create a copy of MonteCarlo with r=3% and sigma=15%
    14.812014828333284

    >>> space[0.06, 0.4].call_opt()   # Dynamically create another copy with r=6% and sigma=40%
    33.90481014639403


License
-------
Copyright 2017-2024, Fumito Hamamura

modelx is free software; you can redistribute it and/or
modify it under the terms of
`GNU Lesser General Public License v3 (LGPLv3)
<https://github.com/fumitoh/modelx/blob/master/LICENSE.LESSER.txt>`_.

Contributions, productive comments, requests and feedback from the community
are always welcome. Information on modelx development is found at Github
https://github.com/fumitoh/modelx


.. Overview End


Requirements
------------
* Python 3.7+
* NetwrkX 2.0+
* asttokens
* LibCST
* Pandas
* OpenPyXL

            

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Overview Begin\n\nWhat is modelx?\n---------------\n**modelx** is a numerical computing tool that enables you to\nuse Python like a spreadsheet by quickly defining cached functions.\nmodelx is best suited for implementing mathematical models expressed\nin a large system of recursive formulas,\nin such fields as actuarial science, quantitative finance and risk management.\n\nFeature highlights\n------------------\n**modelx** enables you to interactively\ndevelop, run and debug complex models in smart ways.\nmodelx allows you to:\n\n- Define cached functions as *Cells* objects by writing Python functions\n- Quickly build object-oriented models, utilizing prototype-based inheritance and composition\n- Quickly parameterize a set of formulas and get results for different parameters\n- Trace formula dependency\n- Import and use any Python modules, such as `Numpy`_, `pandas`_, `SciPy`_, `scikit-learn`_, etc..\n- See formula traceback upon error and inspect local variables\n- Save models to text files and version-control with `Git`_\n- Save data such as pandas DataFrames in Excel or CSV files within models\n- Auto-document saved models by Python documentation generators, such as `Sphinx`_\n- Use Spyder with a plugin for modelx (spyder-modelx) to interface with modelx through GUI\n\n.. _Numpy: https://numpy.org/\n.. _pandas: https://pandas.pydata.org/\n.. _SciPy: https://scipy.org/\n.. _scikit-learn: https://scikit-learn.org/\n.. _Git: https://git-scm.com/\n.. _Sphinx: https://www.sphinx-doc.org\n\n\nmodelx sites\n-------------\n\n========================== ===============================================\nHome page                  https://modelx.io\nBlog                       https://modelx.io/allposts\nDocumentation site         https://docs.modelx.io\nDevelopment                https://github.com/fumitoh/modelx\nDiscussion Forum           https://github.com/fumitoh/modelx/discussions\nmodelx on PyPI             https://pypi.org/project/modelx/\n========================== ===============================================\n\n\nWho is modelx for?\n------------------\n**modelx** is designed to be domain agnostic, \nso it's useful for anyone in any field.\nEspecially, modelx is suited for modeling in such fields such as:\n\n- Quantitative finance\n- Risk management\n- Actuarial science\n\n**lifelib** (https://lifelib.io) is a library of actuarial and\nfinancial models that are built on top of modelx.\n\nHow modelx works\n----------------\n\nBelow is an example showing how to build a simple model using modelx.\nThe model performs a Monte Carlo simulation to generate 10,000\nstochastic paths of a stock price that follow a geometric Brownian motion\nand to price an European call option on the stock.\n\n.. code-block:: python\n\n    import modelx as mx\n    import numpy as np\n\n    model = mx.new_model()                  # Create a new Model named \"Model1\"\n    space = model.new_space(\"MonteCarlo\")   # Create a UserSpace named \"MonteCralo\"\n\n    # Define names in MonteCarlo\n    space.np = np\n    space.M = 10000     # Number of scenarios\n    space.T = 3         # Time to maturity in years\n    space.N = 36        # Number of time steps\n    space.S0 = 100      # S(0): Stock price at t=0\n    space.r = 0.05      # Risk Free Rate\n    space.sigma = 0.2   # Volatility\n    space.K = 110       # Option Strike\n\n\n    # Define Cells objects in MonteCarlo from function definitions\n    @mx.defcells\n    def std_norm_rand():\n        gen = np.random.default_rng(1234)\n        return gen.standard_normal(size=(N, M))\n\n\n    @mx.defcells\n    def stock(i):\n        \"\"\"Stock price at time t_i\"\"\"\n        dt = T/N; t = dt * i\n        if i == 0:\n            return np.full(shape=M, fill_value=S0)\n        else:\n            epsilon = std_norm_rand()[i-1]\n            return stock(i-1) * np.exp((r - 0.5 * sigma**2) * dt + sigma * epsilon * dt**0.5)\n\n\n    @mx.defcells\n    def call_opt():\n        \"\"\"Call option price by Monte Carlo\"\"\"\n        return np.average(np.maximum(stock(N) - K, 0)) * np.exp(-r*T)\n\nRunning the model from IPython is as simple as calling a function:\n\n.. code-block:: pycon\n\n    >>> stock(space.N)      # Stock price at i=N i.e. t=T\n    array([ 78.58406132,  59.01504804, 115.148291  , ..., 155.39335662,\n            74.7907511 , 137.82730703])\n\n    >>> call_opt()\n    16.26919556999345\n\nChanging a parameter is as simple as assigning a value to a name:\n\n.. code-block:: pycon\n\n    >>> space.K = 100   # Cache is cleared by this assignment\n\n    >>> call_opt()    # New option price for the updated strike\n    20.96156962064\n\nYou can even dynamically create multiple copies of *MonteCarlo*\nwith different combinations of ``r`` and ``sigma``,\nby parameterizing *MonteCarlo* with ``r`` and ``sigma``:\n\n.. code-block:: pycon\n\n    >>> space.parameters = (\"r\", \"sigma\")   # Parameterize MonteCarlo with r and sigma\n\n    >>> space[0.03, 0.15].call_opt()  # Dynamically create a copy of MonteCarlo with r=3% and sigma=15%\n    14.812014828333284\n\n    >>> space[0.06, 0.4].call_opt()   # Dynamically create another copy with r=6% and sigma=40%\n    33.90481014639403\n\n\nLicense\n-------\nCopyright 2017-2024, Fumito Hamamura\n\nmodelx is free software; 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