# Portfolio optimization (/tutorials/portfolio-optimization)

[Members-only tutorial](https://members.vectorbt.pro/tutorials/portfolio-optimization/)

Are you looking to build a portfolio that achieves the highest possible return while keeping the risk at a
level you are comfortable with?

✅ Learn how to design and implement your own portfolio optimization models 🎂

✅ Discover how VBT integrates with third-party libraries such as
[PyPortfolioOpt](https://pyportfolioopt.readthedocs.io/en/latest/),
[Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib), and
[Universal Portfolios](https://github.com/Marigold/universal-portfolios)
to enable rebalancing with just a few lines of code!

✅ Learn how to rebalance dynamically using Numba. We will implement a threshold rebalancing template
that can be used with any optimization function. As a bonus, we will build a mean-variance optimizer
(MVO) from scratch for a significant performance boost 💨

*   [Models](https://members.vectorbt.pro/tutorials/portfolio-optimization/models): Design and implement portfolio optimization models
*   [Integrations](https://members.vectorbt.pro/tutorials/portfolio-optimization/integrations): Learn about integrations with third-party portfolio optimization libraries in VectorBT PRO
*   [Dynamic](https://members.vectorbt.pro/tutorials/portfolio-optimization/dynamic): Learn about dynamic portfolio optimization in VectorBT PRO