asset-allocation

Portfolio allocation is optimized by applying MPT, Black-Litterman, risk budgeting, and all-weather strategies to a multi-asset universe. The system requires return forecasts, covariance data, and a valid optimizer configuration to calculate actionable target weights.

The best way to generate target weights is using built-in optimizers like mean_variance, risk_parity, equal_volatility, and max_diversification. You provide an instrument universe, return forecasts, and covariance data to produce the final allocation.

Yes, risk parity and max diversification frameworks support custom rebalancing rules and constraints. Applied to multi-asset investments, they process covariance data and return forecasts to output target weights suitable for backtesting scenarios.

Portfolio optimization integrates by generating output directly into a config.json file. This format allows seamless integration with downstream portfolio systems, enabling automated execution of the calculated target weights and rebalancing rules.

Risk budgeting and portfolio reconstruction require an instrument universe, return forecasts, covariance data, and a valid optimizer configuration. These inputs allow the framework to process constraints and produce actionable target weights.

Use the Black-Litterman model versus standard mean variance optimization when you need to incorporate subjective return forecasts into market equilibrium returns. Both frameworks process covariance data and constraints to output target weights via config.json.