  The potential of machine learning to automate and control nonlinear, complex
systems is well established. These same techniques have always presented
potential for use in the investment arena, specifically for the managing of
equity portfolios. In this paper, the opportunity for such exploitation is
investigated through analysis of potential simple trading strategies that can
then be meshed together for the machine learning system to switch between. It
is the eligibility of these strategies that is being investigated in this
paper, rather than application. In order to accomplish this, the underlying
assumptions of each trading system are explored, and data is created in order
to evaluate the efficacy of these systems when trading on data with the
underlying patterns that they expect. The strategies are tested against a
buy-and-hold strategy to determine if the act of trading has actually produced
any worthwhile results, or are simply facets of the underlying prices. These
results are then used to produce targeted returns based upon either a desired
return or a desired risk, as both are required within the portfolio-management
industry. Results show a very viable opportunity for exploitation within the
aforementioned industry, with the Strategies performing well within their
narrow assumptions, and the intelligent system combining them to perform
without assumptions.
