asset-allocation

Portfolio allocation applies modern portfolio theory and Black-Litterman frameworks to generate diversified asset weights. It combines these theories with built-in optimizers to construct portfolios with explicit risk controls and clear allocation targets.

Portfolio rebalancing with risk budgeting balances risk contributions across assets using built-in optimizer configurations. It generates target weights for periodic rebalances by applying lookback-based risk estimates and optional constraints across asset classes.

Risk parity and equal volatility are supported optimizers for all-weather portfolio construction. These frameworks balance risk contributions across assets and scenarios, allowing you to build robust portfolios with predefined risk targets.

Mean variance and max diversification optimizers are configured through a config.json file. You define the optimizer type and lookback window in the configuration, then run the allocation to output target weights and risk metrics.

Lookback-based risk estimates are required for generating target weights in multi-asset portfolios. The optimization process uses these estimates alongside your configured optimizer to calculate asset weights and output risk metrics.

The asset allocation optimizer outputs target weights and risk metrics after processing. Results are generated based on your config.json settings, applying frameworks like risk budgeting and modern portfolio theory to your multi-asset portfolio.