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XAD
C++ automatic differentiation library for derivatives and risk
What it is
An automatic differentiation library, often called AAD in finance, implemented in C++. It supports forward and reverse modes, so derivatives and gradients of complex calculations can be computed exactly and efficiently. Quant developers working on risk sensitivities and financial engineering code that needs fast derivatives are the main audience. Integrating it means adapting your numerical code to its types, which takes effort in a large existing codebase.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Developers |
|---|---|
| Used for | Strategy research, Data analysis |
| Markets | Other markets |
| Stack | C++ |
| Learning curve | Steep 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): +6 over the period, now 436. Gaps mean no snapshot was taken that day.
Automatic Differentation (AAD) Library.