portfolio-optimizer

Mean-variance optimization requires return forecasts, while risk parity is a correlation-aware approach used when forecasts are unavailable. This Skill guides optimizer selection based on your data availability and diversification goals.

Black-Litterman asset allocation combines market equilibrium returns with your own forecasts to build stable portfolios. Use it when you want to incorporate specific views into mean-variance optimization without extreme weight concentrations.

Portfolio rebalancing rules use periodic, threshold, or volatility-based triggers. This Skill suggests appropriate rebalancing frequencies and thresholds by asset class to ensure realistic backtesting and configuration.

Yes. This Skill produces selection-ready recommendations and optimizer configurations for all-weather diversification approaches, outputting allocation plans expressed specifically for config.json workflows.

Risk budget targeting allocates risk contributions rather than capital across assets. This Skill provides configuration guidance for the risk_parity and equal_volatility optimizers to achieve your specific risk distribution.

No. The max_diversification optimizer focuses on correlation-aware diversification rather than return forecasts. This Skill recommends it when you lack reliable forecasts but want to minimize portfolio correlation risk.