Abstract
Given the advancement in algorithmic trading, the needs for real-time monitoring of patterns and execution of trades in stock exchanges become increasing important for investors. However, real-time monitoring of patterns from a vast number of markets become inefficient when tens of thousands of time series are required to be processed. In order to alleviate these problems, we propose a novel approach called Multi-resolution Chart Patterns Classification (FMCPC) based on a decision tree. In the proposed approach, a Customized Decision Tree (CDT) is built for pattern matching. CDT starts with a detailed analysis of known patterns. CDT then forms generalizations of these examples by identifying commonalities for designing decision rules. To evaluate our approach, experiments are conducted on the real datasets containing 2,527,800 data points from 19,150 stocks across top 10 Exchanges in the world. Our results reveal that FMCPC with CDT can effectively identify the chart patterns within 1 min.
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j is determined when S is generated by Algorithm 1.
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Due to the space limitation, they cannot be enlarged. The main purpose of including these figures is to highlight the differences in tree structure.
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This research was funded by University of Macau (File No. MYRG2019-00136-FST).
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Sun, Q., Si, YW. (2020). Customized Decision Tree for Fast Multi-resolution Chart Patterns Classification. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_39
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