quantgirluk/aleatory
📦 Python library for Stochastic Processes Simulation and Visualisation
Implements 25+ stochastic process models (Brownian Motion, Geometric Brownian Motion, Vasicek, Hawkes, Poisson, and others) with support for both 1D and 2D trajectories. Built on NumPy for random number generation and SciPy/statsmodels for distributional support, with integrated Matplotlib visualization for trajectory plotting. Designed for quantitative finance and probability research applications requiring discrete-time Monte Carlo simulations.
357 stars and 264 monthly downloads. Available on PyPI.
Stars
357
Forks
39
Language
Python
License
MIT
Category
Last pushed
Mar 10, 2026
Monthly downloads
264
Commits (30d)
0
Dependencies
6
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