portfolio-optimization

To optimize multi-asset portfolio allocation, provide historical returns and covariance matrices to generate optimized weights using mean-variance, risk parity, HRP, or Black-Litterman models. The toolkit handles diversified asset allocation and outputs optimized weights alongside risk metrics.

The best way to apply Black-Litterman portfolio construction is to supply historical returns, covariance matrices, and optional investor views as inputs. The model integrates these custom views with market equilibrium to output customized optimized weights for your multi-asset portfolio.

Yes, you can use hierarchical risk parity (HRP) for diversified portfolio risk budgeting by inputting historical returns. HRP constructs optimized portfolios using robust covariance estimation without requiring matrix inversion, yielding optimized weights and tail risk metrics like VaR and CVaR.

To calculate VaR and CVaR tail risk for an optimized portfolio, input historical returns and covariance data into the toolkit. It generates performance attribution and robust covariance estimation, outputting specific tail risk metrics alongside your optimized portfolio weights.

To start building an optimized portfolio with Markowitz MVO, you need historical returns, covariance matrices, and a risk-free rate. Providing these inputs allows the toolkit to construct your portfolio and evaluate risk-return trade-offs based on your risk tolerance.

Yes, Kelly criterion sizing works alongside traditional mean-variance optimization as one of multiple supported allocation models. You can construct portfolios using Kelly sizing and compare the resulting optimized weights and risk metrics against Markowitz MVO, HRP, and Black-Litterman outputs.