asset-allocation

Portfolio optimization using risk parity and mean-variance strategies requires configuring optimizer parameters and a risk-free rate to generate detailed allocation plans with expected risk and return metrics.

The Black-Litterman model is an asset allocation strategy that enhances Modern Portfolio Theory by incorporating investor views, providing more stable and diversified portfolio weights than traditional mean-variance optimization.

Portfolio rebalancing using threshold and volatility triggers involves setting specific deviation limits or volatility bands that automatically signal when to adjust asset weights back to target allocations.

Cross-asset correlation analysis for portfolio diversification is fully supported using numpy and pandas dependencies, computing typical correlation matrices to identify key diversification patterns across asset classes.

Maximum diversification is preferred over equal volatility when the goal is to minimize overall portfolio risk by selecting assets with low correlations, rather than simply weighting assets by their individual volatilities.

Setting up the asset-allocation optimizer requires configuring the optimizer type, lookback period for historical data, and the risk-free rate to compute expected risk and return metrics accurately.