mirkovicdev/CRASH-LANDSCAPES
Topological data analysis of market crashes: persistent homology of four US indices, 1992-2026. The signal measures decoupling, not crash size, and detects rather than predicts.
Why it is useful
CRASH-LANDSCAPES is a recent open-source project in Mathematics. It is included because it has recent repository activity and can support mathematical learning, research, modelling, software development, or AI workflows.
Repository at a glance
- Owner: mirkovicdev
- Primary language: Jupyter Notebook
- Latest update: 2026-07-19
- Stars / forks: 11 / 3
- License: MIT
- Links: No separate project site was listed.
Good starting points
- Read the project README and installation instructions.
- Try the smallest example before changing parameters or datasets.
- Check tests, issues, and release notes before relying on results in teaching or research.
- Cite the repository and its license when reusing code or figures.
README snapshot
A reproduction and out-of-sample extension of Gidea and Katz (2017), Topological Data Analysis of Financial Time Series: Landscapes of Crashes (arXiv:1703.04385), run on four US equity indices from 1992 to 2026. The headline result is a single number per day that spikes at every crash. The point of the project is to ask what that number actually measures, and whether it can predict anything. The honest answers are: it measures decoupling, not volatility or crash size, and no, it does not forecast crashes. It describes, precisely and after the fact, how a market came apart. Take four US indices (