modelx

Use Python like a spreadsheet!

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.. 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.

See also the GitHub Copilot instructions for modelx <https://github.com/fumitoh/modelx/blob/main/.github/copilot-instructions.md>_.

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
• Export models as Python modules independent of modelx
• Translate exported models to Cython optimized code and compile them for performance improvement using Cython through modelx-cython_

.. _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 .. _spyder-modelx: https://github.com/fumitoh/spyder-modelx .. _modelx-cython: https://github.com/fumitoh/modelx-cython

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 2017-2026, 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