This portfolio optimizer tool supports the following portfolio optimization strategies:
Mean Variance Optimization – Find the optimal risk adjusted portfolio that lies on the efficient frontier
Minimize Conditional Value-at-Risk – Optimize the portfolio to minimize the expected tail loss
Maximize Information Ratio – Find the portfolio that maximizes the information ratio against the selected benchmark
Risk Parity – Find the portfolio that equalizes the risk contribution of portfolio assets
Maximize Kelly Criterion – Finds the portfolio with the maximum expected geometric growth rate
Maximize Sortino Ratio – Find the portfolio that maximizes the Sortino ratio for the given minimum acceptable return
Maximize Omega Ratio – Find the portfolio that maximizes the Omega ratio for the given minimum acceptable return
Minimize Maximum Drawdown – Find the portfolio with the minimum worst case drawdown with optional minimum acceptable return
The optimization is based on the monthly return statistics of the selected portfolio assets for the given time period.
The optimization result does not predict what allocation would perform best outside the given time period, and the actual performance
of portfolios constructed using the optimized asset weights may vary from the given performance goal.
The required inputs for the optimization include the time range and the portfolio assets. Portfolio asset weights and constraints are optional.
You can also use the Black-Litterman model based portfolio optimization, which allows the benchmark portfolio asset weights to be optimized based on investor's views.
At least 12 months of data required for portfolio optimization. The time period was constrained by the available data for iPath B S&P 500 VIX S/T Futs ETN (VXX) [Feb 2018 - Nov 2019].