Use Python like a spreadsheet!
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.. Overview Begin
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.
modelx enables you to interactively develop, run and debug complex models in smart ways. modelx allows you to:
Numpy
, pandas
, SciPy
, scikit-learn
, etc..Git
_Sphinx
_.. _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
========================== =============================================== 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/ ========================== ===============================================
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:
lifelib (https://lifelib.io) is a library of actuarial and financial models that are built on top of modelx.
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
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