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https://github.com/fumitoh/modelx

Use Python like a spreadsheet!
https://github.com/fumitoh/modelx

actuarial actuary cache finance memoization modeling monte-carlo python quantitative-finance recursion risk-management time-series

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Use Python like a spreadsheet!

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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)

`_.



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