Home / Other tools
PyFlux
Python time series modeling with frequentist and Bayesian inference
What it is
An open source Python library for time series modeling and inference, offering a probabilistic approach to forecasting and analysis. It includes a broad set of models such as ARIMA, GARCH, EGARCH variants, GAS, state space, and VAR models, combined with inference options spanning maximum likelihood, Metropolis-Hastings, Laplace approximation, and black box variational inference. Quant researchers and developers who want flexible, probabilistic time series work in Python may find it useful. The library is alpha software with limited test coverage, and the author has paused updates for the immediate future.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Data analysis, Strategy research |
| 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 | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +3 over the period, now 2,138. Gaps mean no snapshot was taken that day.
Python library for timeseries modelling and inference (frequentist and Bayesian) on models.
Similar tools
Jupyter notebooks companion to Machine Learning for Trading
Goldman Sachs Python toolkit for derivatives and risk
scikit-learn compatible Python library for portfolio optimization
CVXPY-based portfolio optimization and strategic asset allocation
Python financial econometrics with ARCH, GARCH and volatility models
C++ financial research terminal with embedded Python analytics