asset-allocation

Portfolio optimization using Modern Portfolio Theory constructs an efficient frontier by analyzing asset returns and covariance to generate optimal weights for your config.json. This Skill calculates risk controls and target allocations to maximize expected returns for a given risk level.

The Black-Litterman framework blends market equilibrium with custom investor views to produce adjusted asset allocation weights. This approach prevents extreme allocations often caused by naive optimization, yielding a more robust multi-asset portfolio configuration.

Risk budgeting allocates risk across various economic environments to build an all-weather portfolio. This Skill configures optimizers that distribute risk contributions equally or according to specific targets, outputting a robust configuration for multi-asset investing.

Configuring portfolio rebalancing involves setting target allocations and constraints within config.json using the built-in optimizers. The Skill processes asset returns and covariance to generate an optimizer configuration that includes pragmatic rebalancing guidance for your portfolio.

Asset allocation models like MPT and Black-Litterman apply directly to retirement planning and endowment-style investing. The Skill generates robust multi-asset portfolio weights and rebalancing rules suitable for long-term investment horizons and institutional constraints.

Portfolio optimization requires asset returns, covariance matrices, and specific investment constraints as inputs. The Skill processes these inputs through built-in optimizers to produce target allocations and risk budgeting configurations for your config.json file.