portfolio-optimizer

Mean-variance optimization automates asset allocation by processing expected returns, covariance, and constraints to output optimized weights, efficient frontier plots, and performance metrics for maximum risk-adjusted returns. You simply provide your asset inputs and constraints.

Tax-aware rebalancing manages wash-sale rules and lot-level taxation while maintaining target exposure. The optimizer applies drift-based rebalances to your investment baskets, ensuring efficient lot selection without triggering unnecessary tax penalties.

Factor exposure analysis evaluates your portfolio's sensitivity to value, momentum, quality, and low-volatility factors. It uses your asset weights and covariance data to assess how these exposures impact overall portfolio risk and expected returns.

Yes, you can calculate the efficient frontier by providing a set of assets, expected returns, and covariance inputs. The optimizer processes these constraints to generate frontier visualizations and optimal weights for your custom investment baskets.

Yes, expected returns and covariance matrices are required inputs. The optimizer also needs defined constraints and benchmarks to properly calculate the efficient frontier, optimized weights, and performance metrics for your funds.

The optimizer applies mean-variance analysis using your custom constraints and benchmarks to maximize the Sharpe ratio. It processes these inputs to output optimized asset weights and frontier plots tailored to your specific risk and return targets.