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Although machine learning approaches have been widely used in the field offinance, to very successful degrees, these approaches remain bespoke tospecific investigations and opaque in terms of explainability, comparability,and reproducibility. The primary objective of this research was to shed lightupon this field by providing a generic methodology that wasinvestigation-agnostic and interpretable to a financial markets practitioner,thus enhancing their efficiency, reducing barriers to entry, and increasing thereproducibility of experiments. The proposed methodology is showcased on twoautomated trading platform components. Namely, price levels, a well-knowntrading pattern, and a novel 2-step feature extraction method. The methodologyrelies on hypothesis testing, which is widely applied in other social andscientific disciplines to effectively evaluate the concrete results beyondsimple classification accuracy. The main hypothesis was formulated to evaluatewhether the selected trading pattern is suitable for use in the machinelearning setting. Across the experiments we found that the use of theconsidered trading pattern in the machine learning setting is only partiallysupported 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 genericmethodology on a US futures market instrument and provided evidence that withthis methodology we could easily obtain informative metrics beyond the moretraditional performance and profitability metrics. This work is one of thefirst in applying this rigorous statistically-backed approach to the field offinancial markets and we hope this may be a springboard for more research.

A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components