asset-allocation

Portfolio optimization using the Black-Litterman model incorporates investor views into equilibrium returns to construct risk-adjusted portfolios. This Skill guides the configuration of Black-Litterman parameters alongside built-in optimizers to generate recommended asset weights.

Risk parity allocates portfolio weights so each asset contributes equally to total risk, whereas mean variance optimization balances expected returns against variance. This Skill provides both as distinct built-in optimizers alongside equal_volatility and max_diversification strategies.

Risk budgeting strategies allocate specific risk contributions to assets, and dynamic rebalancing adjusts weights using periodic, threshold, or volatility triggers. This Skill applies risk budgeting parameters and outputs structured rebalancing rules for different asset classes.

Modern Portfolio Theory maximizes risk-adjusted returns using historical covariance, while All-Weather strategies balance risk across economic environments. This Skill provides comprehensive guides on both theories to inform your optimizer configuration and portfolio construction.

Historical price information from multiple data sources can be used to calculate expected returns and covariance for the optimizers. This Skill requires configuring risk parameters and data inputs to generate structured asset allocation recommendations with risk contributions.

Portfolio optimization recommendations are delivered in a structured format including asset weights, risk contributions, expected returns, and rebalancing rules. This output structure applies across all four built-in optimizers and asset allocation theories.