Stochastic Differential Equations

Package	Documentation	Code Coverage
sde

So you say you are interested in stochastic differential equations? And you also subscribe to the idea of learning by example? Well, you are in luck! sde provides basic tools for simulating brownian motions which is the basic ingredients to lots of SDE models, performing different types of stochastic integrations, Eular-Maruyama methods and so much more (to come...).

We have also built a detailed online documentation where we guide you step-by-step on how to use our package.

The origin of this project is this wonderful introductory paper by Desmond J. Higham.

Dependencies

• numpy==1.22.4
• tqdm==4.64.1
• matplotlib==3.7.1
• pandas==2.0.0
• seaborn==0.12.2

Enviroment Setup

We highly recommend creating a virtual environment before proceeding to installing the package. For how to manage virtual environment via conda, check out their tutorial.

pip install -r requirements.txt

Installation

pip install sde