asset-allocation

To calculate optimal portfolio weights, input your current asset list and desired risk level. The skill computes covariance matrices and volatility metrics from historical market data to generate mathematically balanced allocation weights using quantitative models.

Available quantitative models for asset allocation include Modern Portfolio Theory (MPT), Black-Litterman, Risk Budgeting, and All-Weather strategies. These frameworks manage risk and return balance using mathematical optimizers like equal_volatility and max_diversification.

Yes, you can rebalance your portfolio to minimize volatility. By providing your current holdings and selecting the mean_variance or risk_parity optimizer, the skill calculates new allocation weights that target your specific risk profile and diversification goals.

Yes, historical market data is required to compute portfolio covariance matrices and volatility metrics. The optimization frameworks use this historical data as input to mathematically calculate risk contributions and optimal asset weights for your investments.

The mean_variance optimizer focuses on balancing expected returns against portfolio volatility, while the max_diversification optimizer calculates weights to maximize the spread of risk across different assets. Both use covariance matrices to determine optimal allocation strategies.

Use the Black-Litterman model when you have specific expected return views to incorporate into the baseline market equilibrium, and use risk parity when you want to allocate weights strictly based on balancing risk contributions across assets without relying on return forecasts.