Random portfolios are a sample from the population of portfolios that obey some given set of constraints. The constraints are the key ingredient. Random portfolios have a wide range of applications.
Portfolio optimization is the process of using predictions about the asset universe to find a suitable trade to perform. The term “portfolio optimization” is really a misnomer — it is actually the trade that is being optimized.
Utility-free optimization finds the trade that moves as close as possible to an ideal target portfolio while still obeying the specified constraints.
There are a number of constraints that are implemented in Portfolio Probe.
One use of costs is to alleviate the problem in optimization that the input predictions are not known exactly. The transaction costs can be increased which tends to reduce the size of the trade which is the right thing to do when there is uncertainty in the inputs.
The computing engines in Portfolio Probe for random portfolios and for portfolio optimization mostly share the same basic elements. The algorithm is described as “genetic” but it really consists of three types of algorithms…