Abstract
The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.
Highlights
Technical analysis based on trading pattern curves is an important method for quantitative analysis of stocks, and in recent years, there have been a number of scholars who have proved the effectiveness of technical analysis from different perspectives as an effective means of extracting information from market prices [1,2,3]
Existing stock pattern recognition methods can be divided into rule-based pattern recognition and templatebased pattern recognition, both of these methods are based on the extraction of important points and are subjective in nature. erefore, this study attempts to use the overall graphical identification, that is, the overall identification of the price pattern of a certain time window the abovementioned approach can avoid the problem of how to objectively select the important points that constitute the price pattern and the mathematical definition of different price patterns
The wavelet transform is used to denoise the original stock price series, the SymN4 wavelet function is selected according to the characteristics of the stock time series, and the comprehensive scores of different wavelet decomposition layers and wavelet threshold functions are calculated based on the entropy weight method; the results show that the 4-layer decomposition layers and the suppression detail factor threshold method have the highest scores, so the wavelet denoising parameters selected in this study are SymN4 wavelet function, 4-layer decomposition layers, and suppression detail factor threshold method. e improved DTW algorithm was used to calculate the distance between the two sequences to select the optimal graph that is most similar to the target graph
Summary
Shanxi University of Finance & Economics, No 696, Wucheng Road, Taiyuan, Shanxi, China. We propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). At is, using the wavelet transform to smooth the original price graph, and using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. E pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. e pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods
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