Stochastic Processes Simulation and Visualisation
Project description
aleatory
- Homepage: https://github.com/quantgirluk/aleatory
- Pip Repository: aleatory
Overview
The aleatory (/ˈeɪliətəri/) Python library provides functionality for simulating and visualising stochastic processes. More precisely, it introduces objects representing a number of continuous-time stochastic processes $X = (X_t : t\geq 0)$ and provides methods to:
- generate realizations/trajectories from each process —over discrete time sets
- create visualisations to illustrate the processes properties and behaviour
Currently, aleatory
supports the following processes:
- Brownian Motion
- Geometric Brownian Motion
- Ornstein–Uhlenbeck
- Vasicek
- Cox–Ingersoll–Ross
- Constant Elasticity
Installation
Aleatory is available on pypi and can be installed as follows
pip install aleatory
Dependencies
Aleatory relies heavily on
-
numpy
scipy
for random number generation, as well as support for a number of one-dimensional distributions. -
matplotlib
for creating visualisations
Compatibility
Aleatory is tested on Python versions 3.7, 3.8, and 3.9
Project details
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