These are my observations:
1- MSFT correction in January 2022, was the biggest and sharpest correction since March 2020. (Post pandemic)
2- Highest Price-volume ever..(295 Billion) + Biggest negative monthly return since 2016.
3- Making a dome shape Top, after a sharp bullish move in November 2021, Some call this pattern Bump and Run.
The bump and Run chart pattern is a remarkable reversal pattern that will help you spot the end of a trend and the beginning of a new one.
What do you think???
For further information you can read this article: (interesting content, I can not share because of copyright)
Zheng, Yuechu & Si, Yain Whar & Wong, Raymond. (2021). Feature extraction for chart pattern classification in financial time series. Knowledge and Information Systems. 63. 10.1007/s10115-021-01569-1.
Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based, and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related features are widely used for chart pattern matching in financial time series, the intrinsic properties of these features and their relationships to the patterns are rarely investigated in the research community. This paper aims to formally identify shape-related features used in chart patterns and investigates their impact on chart pattern classifications in financial time series. In this paper, we describe a comprehensive analysis of 14 shape-related features which can be used to classify 41 known chart patterns in the technical analysis domain. In order to evaluate their effectiveness, shape-related features are then translated into rules for chart pattern classification. We perform extensive experiments on real datasets containing historical price data of 24 stocks/indices to analyze the effectiveness of the rules. Experimental results reveal that the features put forward in this paper can be effectively used for recognizing chart patterns in financial time series. Our analysis also reveals that high-level features can be hierarchically composed of low-level features. Hierarchical composition allows the construction of complex chart patterns from features identified in this paper. We hope that the features identified in this paper can be used as a reference model for future research in chart pattern analysis.
You can see the most important support(green line) and resistance (red line) levels.
Best,
Moshkelgosha
DISCLAIMER
I’m not a certified financial planner/advisor, a certified financial analyst, an economist, a CPA, an accountant, or a lawyer. I’m not a finance professional through formal education. The contents on this site are for informational purposes only and do not constitute financial, accounting, or legal advice. I can’t promise that the information shared on my posts is appropriate for you or anyone else. By using this site, you agree to hold me harmless from any ramifications, financial or otherwise, that occur to you as a result of acting on information found on this site.
1- MSFT correction in January 2022, was the biggest and sharpest correction since March 2020. (Post pandemic)
2- Highest Price-volume ever..(295 Billion) + Biggest negative monthly return since 2016.
3- Making a dome shape Top, after a sharp bullish move in November 2021, Some call this pattern Bump and Run.
The bump and Run chart pattern is a remarkable reversal pattern that will help you spot the end of a trend and the beginning of a new one.
What do you think???
For further information you can read this article: (interesting content, I can not share because of copyright)
Zheng, Yuechu & Si, Yain Whar & Wong, Raymond. (2021). Feature extraction for chart pattern classification in financial time series. Knowledge and Information Systems. 63. 10.1007/s10115-021-01569-1.
Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based, and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related features are widely used for chart pattern matching in financial time series, the intrinsic properties of these features and their relationships to the patterns are rarely investigated in the research community. This paper aims to formally identify shape-related features used in chart patterns and investigates their impact on chart pattern classifications in financial time series. In this paper, we describe a comprehensive analysis of 14 shape-related features which can be used to classify 41 known chart patterns in the technical analysis domain. In order to evaluate their effectiveness, shape-related features are then translated into rules for chart pattern classification. We perform extensive experiments on real datasets containing historical price data of 24 stocks/indices to analyze the effectiveness of the rules. Experimental results reveal that the features put forward in this paper can be effectively used for recognizing chart patterns in financial time series. Our analysis also reveals that high-level features can be hierarchically composed of low-level features. Hierarchical composition allows the construction of complex chart patterns from features identified in this paper. We hope that the features identified in this paper can be used as a reference model for future research in chart pattern analysis.
You can see the most important support(green line) and resistance (red line) levels.
Best,
Moshkelgosha
DISCLAIMER
I’m not a certified financial planner/advisor, a certified financial analyst, an economist, a CPA, an accountant, or a lawyer. I’m not a finance professional through formal education. The contents on this site are for informational purposes only and do not constitute financial, accounting, or legal advice. I can’t promise that the information shared on my posts is appropriate for you or anyone else. By using this site, you agree to hold me harmless from any ramifications, financial or otherwise, that occur to you as a result of acting on information found on this site.