aleatory 0.0.1

Stochastic Processes Simulation and Visualisation

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