The global financial ecosystem has evolved and matured along with the ever-changing world economy that grew increasingly complicated due to globalisation. As traders are often inundated with information from various sources when formulating trading strategies, numerous analysis methods have been developed to ease the decision-making process. However, factors such as prior experience and knowledge of the trader as well as various psychological factors often influence the final trading decision. Focusing on charting-based analysis, it still suffers from drawbacks due to the time-warping properties of the chart patterns and the reliance on a large number of pre-defined chart patterns. Hence, in order to address the gaps within the FOREX research, the paper endeavours to propose a novel chart detection algorithm. The auto-segmentation implementation within the algorithm utilises piecewise linear regression to detect chart patterns within the FOREX historical data. By successfully extracting the repetitive chart patterns and subsequently establishing its similarities using Agglomerative Hierarchical Clustering, the information provided could potentially be used to assist traders in solidifying their investment decisions. The experimental results obtained show that repetitive chart patterns can indeed be successfully detected and extracted from the FOREX historical data.

中文翻译：

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自适应检测外汇重复图表模式

全球金融生态系统已经随着不断变化的世界经济发展和成熟，由于全球化，世界经济日趋复杂。在制定交易策略时，由于交易员经常被来自各种来源的信息所淹没，因此已经开发了许多分析方法来简化决策过程。但是，诸如交易者的先前经验和知识以及各种心理因素之类的因素通常会影响最终的交易决策。专注于基于图表的分析，由于图表模式的时间扭曲特性以及对大量预定义图表模式的依赖，它仍然存在缺陷。因此，为了解决FOREX研究中的空白，本文努力提出一种新颖的图表检测算法。该算法中的自动分段实现利用分段线性回归来检测FOREX历史数据中的图表模式。通过成功提取重复图表模式并随后使用聚集层次聚类建立相似性，提供的信息可能会用于帮助交易者巩固其投资决策。获得的实验结果表明，可以成功地从FOREX历史数据中检测并提取出重复的图表模式。提供的信息可能会用于帮助交易者巩固其投资决策。获得的实验结果表明，可以成功地从FOREX历史数据中检测并提取出重复的图表模式。提供的信息可能会用于帮助交易者巩固其投资决策。获得的实验结果表明，可以成功地从FOREX历史数据中检测并提取出重复的图表模式。