Adaptive detection of FOREX repetitive chart patterns

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.