Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/quantgirluk/aleatory

📦 Python library for Stochastic Processes Simulation and Visualisation
https://github.com/quantgirluk/aleatory

data-visualization data-viz diffusion-models financial-mathematics monte-carlo probability statistics stochastic-differential-equations stochastic-processes

Last synced: 3 months ago
JSON representation

📦 Python library for Stochastic Processes Simulation and Visualisation

Host: GitHub
URL: https://github.com/quantgirluk/aleatory
Owner: quantgirluk
License: mit
Created: 2022-09-07T20:39:16.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-01-14T17:22:37.000Z (5 months ago)
Last Synced: 2024-02-27T22:22:08.722Z (4 months ago)
Topics: data-visualization, data-viz, diffusion-models, financial-mathematics, monte-carlo, probability, statistics, stochastic-differential-equations, stochastic-processes
Language: Python
Homepage:
Size: 77.2 MB
Stars: 87
Watchers: 5
Forks: 6
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - aleatory

README

        # *aleatory*

[![PyPI version fury.io](https://badge.fury.io/py/aleatory.svg)](https://pypi.org/project/aleatory/) [![Downloads](https://static.pepy.tech/personalized-badge/aleatory?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads)](https://pepy.tech/project/aleatory)

![example workflow](https://github.com/quantgirluk/aleatory/actions/workflows/python-package.yml/badge.svg) [![Documentation Status](https://readthedocs.org/projects/aleatory/badge/?version=latest)](https://aleatory.readthedocs.io/en/latest/?badge=latest)

- [Git Homepage](https://github.com/quantgirluk/aleatory)

- [Pip Repository](https://pypi.org/project/aleatory/)

- [Documentation](https://aleatory.readthedocs.io/en/latest/)

## 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

- Bessel Process

- Squared Bessel Processs

## Installation

Aleatory is available on [pypi](https://pypi.python.org/pypi) and can be

installed as follows

```

pip install aleatory

```

## Dependencies

Aleatory relies heavily on

- ``numpy``  for random number generation

- ``scipy`` and ``statsmodels`` for support for a number of one-dimensional distributions.

- ``matplotlib`` for creating visualisations

## Compatibility

Aleatory is tested on Python versions 3.8, 3.9, and 3.10

## Quick-Start

Aleatory allows you to create fancy visualisations from different stochastic processes in an easy and concise way.

For example, the following code

```python

from aleatory.processes import BrownianMotion

brownian = BrownianMotion()

brownian.draw(n=100, N=100, colormap="cool", figsize=(12,9))

```

generates a chart like this:

  




For more example visit the [Quick-Start Guide](https://aleatory.readthedocs.io/en/latest/general.html).

## Thanks for Visiting! ✨

Connect with me via:

- 🦜 [Twitter](https://twitter.com/Quant_Girl)

- 👩🏽‍💼 [Linkedin](https://www.linkedin.com/in/dialidsantiago/)

- 📸 [Instagram](https://www.instagram.com/quant_girl/)

- 👾 [Personal Website](https://quantgirl.blog)

⭐️ **If you like this projet, please give it a star!** ⭐️