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lppls
Fit the LPPLS bubble model to financial time series
What it is
A Python module for fitting the Log-Periodic Power Law Singularity model, an approach associated with Didier Sornette for describing speculative bubbles. It fits the model to price series and helps identify the accelerating, oscillating patterns the theory links to bubble phases. Researchers and developers interested in bubble modelling and crash-risk studies are the intended users. Fits can be sensitive to the chosen window and starting values, so outputs should be treated as one research signal rather than a timing tool.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Strategy research, Data analysis |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 5 months ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): 0 over the period, now 478. Gaps mean no snapshot was taken that day.
A Python module for fitting the [Log-Periodic Power Law Singularity (LPPLS)](https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.
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