  To promote economic stability, finance should be studied as a hard science,
where scientific methods apply. When a trading strategy is proposed, the
underlying model should be transparent and defined robustly to allow other
researchers to understand and examine it thoroughly. Like any hard sciences,
results must be repeatable to allow researchers to collaborate, and build upon
each other's results. Large-scale collaboration, when applying the steps of
scientific investigation, is an efficient way to leverage "crowd science" to
accelerate research in finance. In this paper, we demonstrate how a real world
problem in economics, an old problem still subject to a lot of debate, can be
solved by the application of a crowd-powered, collaborative scientific
computational framework, fully supporting the process of investigation dictated
by the modern scientific method. This paper provides a real end-to-end example
of investigation to illustrate the use of the framework. We intentionally
selected an example that is self-contained, complete, simple, accessible, and
of constant debate in both academia and the industry: the performance of a
trading strategy used commonly in technical analysis. Claims of efficiency in
technical analysis, referred derisively by some sources as "Black Magic", are
of widespread use in mainstream media and usually met with a lot of
controversy. In this paper we show that different researchers assess this
strategy differently, and the subsequent debate is due more to the lack of
method than purpose. Most results reported are not repeatable by other
researchers. This is not satisfactory if we intend to approach finance as a
hard science. To counterweight the status quo, we demonstrate what one could do
by using collaborative and investigative features of contributions and
leveraging the power of crowds.
