A Personalized Stock Market Acquisition Method Based on Scrapy - Ming, Shi; Rasli, Roznim Mohamad; Wang, Shir Li; Rasli, Roznim Mohamad (International Journal of Computer Science and Information Technology, 2024)
Stock data is of great significance for stock analysis and decision-making. In the field of stock data analysis and mining, the quality and diversity of data have a crucial impact on the depth of research and work efficiency. Traditional data acquisition methods, such as official channels or third-party data source API and SDK interfaces, often provide standardized data. Although these data elements meet basic analysis needs to a certain extent, due to their inherent limitations, it is difficult to support personalized and in-depth research. With the development of the stock market and the advancement of data analysis technology, researchers' demand for data is constantly increasing. They not only require real-time and accurate data, but also hope to obtain more diversified and customized data elements, such as investor posting data, social network data, etc. Behind this demand is the desire to build more refined models, understand more complex market behaviors, and provide more accurate decision support. This paper proposes a stock data collection method based on the Scrapy framework, aiming to address the limitations of traditional data acquisition methods. By analyzing specific stock websites and crawling and processing stock data sources, it shows how to use crawler technology to achieve efficient data collection and analysis. The system improves data acquisition efficiency and system stability by optimizing crawler performance and storage algorithms. This research result provides new ideas and technical support for stock data analysis and mining. space before of 18-point and after of 60-point.