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@@ -320,6 +320,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
 - [riskparity.py](https://github.com/dppalomar/riskparity.py) - `Python` - fast and scalable design of risk parity portfolios with TensorFlow 2.0.
 - [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) - `Python` - Implementations regarding "Advances in Financial Machine Learning" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling).
 - [DeepDow](https://github.com/jankrepl/deepdow) - `Python` - Portfolio optimization with deep learning.
+- [goal-based-allocation](https://github.com/ArturSepp/GoalBasedAllocation) - `Python` - Dynamic mean-variance portfolio allocation under regime-switching jump-diffusions with wealth floors, solved analytically via Laplace transforms.
 - [QuantLibRisks](https://github.com/auto-differentiation/QuantLib-Risks-Py) - `Python` - Fast risks with QuantLib.
 - [XAD](https://github.com/auto-differentiation/xad-py) - `Python` - Automatic Differentation (AAD) Library.
 - [pyfolio](https://github.com/quantopian/pyfolio) - `Python` - Portfolio and risk analytics in Python.