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Quant finance tutorials with Python and QuantLib
What it is
A collection of tutorials on quantitative finance written in Python and QuantLib. Topics include derivatives pricing, xVA, hedging, portfolio optimisation, and machine learning and deep learning applications, all presented as Jupyter notebooks. Quant researchers, financial engineering students and developers applying Python to pricing and risk work are the intended readers. The notebooks are tutorials, so they show techniques rather than offering a packaged library.
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 | Steep 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 2026-09-28 (up to 30 days): 0 over the period, now 179. Gaps mean no snapshot was taken that day.
Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.
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