  Although machine learning approaches have been widely used in the field of
finance, to very successful degrees, these approaches remain bespoke to
specific investigations and opaque in terms of explainability, comparability,
and reproducibility. The primary objective of this research was to shed light
upon this field by providing a generic methodology that was
investigation-agnostic and interpretable to a financial markets practitioner,
thus enhancing their efficiency, reducing barriers to entry, and increasing the
reproducibility of experiments. The proposed methodology is showcased on two
automated trading platform components. Namely, price levels, a well-known
trading pattern, and a novel 2-step feature extraction method. The methodology
relies on hypothesis testing, which is widely applied in other social and
scientific disciplines to effectively evaluate the concrete results beyond
simple classification accuracy. The main hypothesis was formulated to evaluate
whether the selected trading pattern is suitable for use in the machine
learning setting. Across the experiments we found that the use of the
considered trading pattern in the machine learning setting is only partially
supported by statistics, resulting in insignificant effect sizes (Rebound 7 -
$0.64 \pm 1.02$, Rebound 11 $0.38 \pm 0.98$, and rebound 15 - $1.05 \pm 1.16$),
but allowed the rejection of the null hypothesis. We showcased the generic
methodology on a US futures market instrument and provided evidence that with
this methodology we could easily obtain informative metrics beyond the more
traditional performance and profitability metrics. This work is one of the
first in applying this rigorous statistically-backed approach to the field of
financial markets and we hope this may be a springboard for more research.
