论文标题
自动交易平台组件的统计统一和可比评估的通用方法
A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components
论文作者
论文摘要
尽管机器学习方法已在财务领域被广泛使用，但在非常成功的学位上，这些方法仍然可以根据解释性，可比性和可重复性来定制特定研究和不透明。这项研究的主要目的是通过提供一种通用方法来阐明这一领域，该方法对金融市场从业人员进行了调查，可解释，从而提高了其效率，降低了进入的障碍并提高了实验的可重复性。在两个自动交易平台组件上展示了所提出的方法。也就是说，价格水平，众所周知的交易模式和一种新颖的2步提取方法。该方法依赖于假设检验，该假设检验在其他社会和科学学科中广泛应用，以有效地评估除简单分类精度之外的具体结果。提出了主要假设，以评估所选的交易模式是否适合在机器学习设置中使用。在整个实验中，我们发现在机器学习设置中使用所考虑的交易模式仅由统计数据提供了部分支持，从而导致效应量微不足道（反弹7- $ 0.64 \ pm 1.02 $，篮板11 $ 0.38 \ pm 0.98 $，以及返回15- $ 1.05- $ 1.05 \ pm 1.16 $），但允许Null null null null null null null null null null null null null null null null null null null null null null null。我们在美国期货市场工具上展示了通用方法，并提供了证据表明，通过这种方法，我们可以轻松获得除传统绩效和盈利能力指标之外的信息指标。这项工作是将这种严格的统计支持方法应用于金融市场领域的第一批工作之一，我们希望这可能是更多研究的跳板。
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.
