Home / Backtesting & trading frameworks
Jupyter Quant
Dockerized Jupyter research environment with preloaded quant tools
What it is
A Docker-based Jupyter environment for quantitative research with many tools preinstalled, including statsmodels, pymc, arch, py_vollib, zipline-reloaded and PyPortfolioOpt. It removes much of the work of setting up a Python quant environment by hand. Analysts and developers who want a ready research workspace on any machine running Docker are the target users. Bundled versions are fixed per image, so you may need to rebuild it to get newer releases of a specific library.
At a glance
Worth watchingOur 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 | Commits today |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): 0 over the period, now 247. Gaps mean no snapshot was taken that day.
A dockerized Jupyter quant research environment with preloaded tools for quant analysis, statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, etc.
Similar tools
Jupyter notebooks companion to Machine Learning for Trading
scikit-learn compatible Python library for portfolio optimization
CVXPY-based portfolio optimization and strategic asset allocation
Goldman Sachs Python toolkit for derivatives and risk
Portfolio and risk analytics, a maintained fork of pyfolio
C++ financial research terminal with embedded Python analytics