Data Mining Novel Chart Patterns With Python | Algorithmic Trading Strategy
About this lesson
Using perceptually important points combined with unsupervised learning to find unique chart patterns for trading using python. We cluster the price structure patterns and select the high performing patterns using the martin ratio as an objective function. We perform a monte carlo permutation test to verify the results. We also perform a walkforward test. This video has a detailed explanation of the perceptually important points algorithm. Chart Pattern Algorithms: https://www.youtube.com/watch?v=X31hyMhB-3s Links Full Code: https://github.com/neurotrader888/TechnicalAnalysisAutomation Martin Ratio: https://www.tangotools.com/ui/ui.htm K-Means: https://en.wikipedia.org/wiki/K-means_clustering Silhouette: https://en.wikipedia.org/wiki/Silhouette_(clustering) Citations Chung, F.L., Fu, T.C., Luk, R., Ng, V., Flexible Time Series Pattern Matching Based on Perceptually Important Points. In: Workshop on Learning from Temporal and Spatial Data at IJCAI (2001) 1-7 Keogh, E., Lin, J., Truppel, W.: Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. Proc. of ICDM, (2003) 115-122 Fu, Tc., Chung, Fl., Luk, R., Ng, Cm. (2005). Preventing Meaningless Stock Time Series Pattern Discovery by Changing Perceptually Important Point Detection. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. Peter Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20(1):53–65, November 1987. The content covered on this channel is NOT to be considered as any financial or investment advice. Past results are not necessarily indicative of future results. This content is purely for education/entertainment.
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