Linear Regression Approach For Stock Chart Pattern Recognition

Finding stock chart patterns, like channels, triangles, head and shoulders, etc., in high-dimensional stock data is arduous. Technical analysis experts can visualize these patterns in stock candlestick charts based on experience and predict stock price movements. The subjectivity of visualizing these stock patterns by different experts increases the possibility of drawing different variations of the same patterns based on scale and time. Stock market pattern recognition is a complex challenge due to the volatile nature of the stock price. Various machine-learning algorithms can be used to detect stock chart patterns. A linear regression approach for forming these patterns can minimize human errors by automating the process. In this paper, we have implemented linear regression, which is an efficient approach for handling dynamic stock data and drawing a triangle pattern on charts. It has an average accuracy of 98.60% when the top ten stocks of the Information Technology sector and Pharmaceutical sector in terms of market capitalization in the Indian National Stock Exchange are analyzed.