Quick Portfolio Optimizer

The Macroaxis Quick Portfolio Optimization module is built on classical mean variance optimization techniques introduced by Harry Markowitz in his paper titled 'Portfolio Selection' published in 1952 in The Journal of Finance. This module is a subset of Advanced Portfolio Optimizer which provides additional input into the optimization algorithm and uses investing ideas as possible models for portfolio origination.


Portfolio Optimizer picks the optimal portfolio from the efficient frontier based on your investment objectives and risk preferences. It evaluates a One-Day Value at Risk (VaR) for the optimal portfolio, along with its total risk, expected return, and sharpe ratio. Your main objective, as a rational investor, is to outperform your existing portfolio in the four main categories.

The main assumption of this model is that a rational investor will not select a portfolio if another portfolio exists with superior risk-return tradeoff.

Achieving perfect optimization

If any measures in a resulted optimized portfolio do not outperform your existing portfolio, you can change the inputs and run the model again until all 4 measures in the resulted portfolio outperform your existing portfolio.

The easiest way to determine if your portfolio is optimal is to run Portfolio Optimizer several times replacing your current portfolio with the resulted optimal portfolio after each iteration. You should stop this process when all relative scores of your portfolio are identical (or almost identical) to relative scores of the optimal portfolio.
Please note that changing model inputs can significantly alter your desired optimal asset allocation. Make sure you carefully select your inputs before running the model !


Please note, the New York Stock Exchange (NYSE) and American Stock Exchange (AMEX) have recently merged. Although Macroaxis has implemented solutions to handle this transition gracefully, you may still find some securities that may not be fully transferred from one exchange to another.