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eFkolos Tech Indicator
Machine learning-enhanced SuperTrend indicator that uses k-means clustering to adaptively optimize SuperTrend parameters based on historical performance. Let me break down what makes this unique:
Key Innovation
Instead of using a single fixed SuperTrend factor, this indicator:
Calculates multiple SuperTrends simultaneously (with factors from 1 to 5 by default, stepped at 0.5)
Tracks performance of each variant using exponential smoothing
Clusters them into 3 groups (Best/Average/Worst) using k-means algorithm
Adapts by selecting the average factor from your chosen cluster
Clever Technical Aspects
Performance Metric: Uses a smart approach where performance = EMA of (price_change × signal_direction), giving positive values when the SuperTrend correctly predicts direction.
K-means Implementation: Properly initializes centroids using quartiles and iterates until convergence - this is solid unsupervised learning.
Adaptive MA Layer: The perf_ama that adapts faster when the performance index is high (more confidence) and slower when low.
Memory Management: Uses UDTs (User Defined Types) efficiently with arrays to handle multiple SuperTrend instances.
Key Innovation
Instead of using a single fixed SuperTrend factor, this indicator:
Calculates multiple SuperTrends simultaneously (with factors from 1 to 5 by default, stepped at 0.5)
Tracks performance of each variant using exponential smoothing
Clusters them into 3 groups (Best/Average/Worst) using k-means algorithm
Adapts by selecting the average factor from your chosen cluster
Clever Technical Aspects
Performance Metric: Uses a smart approach where performance = EMA of (price_change × signal_direction), giving positive values when the SuperTrend correctly predicts direction.
K-means Implementation: Properly initializes centroids using quartiles and iterates until convergence - this is solid unsupervised learning.
Adaptive MA Layer: The perf_ama that adapts faster when the performance index is high (more confidence) and slower when low.
Memory Management: Uses UDTs (User Defined Types) efficiently with arrays to handle multiple SuperTrend instances.
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除非您完全信任其作者並了解腳本的工作原理，否則TradingView不建議您付費或使用腳本。您也可以在我們的社群腳本中找到免費的開源替代方案。
作者的說明
Machine learning-enhanced SuperTrend indicator that uses k-means clustering to adaptively optimize SuperTrend parameters based on historical performance. Let me break down what makes this unique:
Key Innovation
Instead of using a single fixed SuperTrend
免責聲明
這些資訊和出版物並不意味著也不構成TradingView提供或認可的金融、投資、交易或其他類型的意見或建議。請在使用條款閱讀更多資訊。
僅限邀請腳本
只有經作者批准的使用者才能訪問此腳本。您需要申請並獲得使用權限。該權限通常在付款後授予。如欲了解更多詳情，請依照以下作者的說明操作，或直接聯絡sbagdai。
除非您完全信任其作者並了解腳本的工作原理，否則TradingView不建議您付費或使用腳本。您也可以在我們的社群腳本中找到免費的開源替代方案。
作者的說明
Machine learning-enhanced SuperTrend indicator that uses k-means clustering to adaptively optimize SuperTrend parameters based on historical performance. Let me break down what makes this unique:
Key Innovation
Instead of using a single fixed SuperTrend
免責聲明
這些資訊和出版物並不意味著也不構成TradingView提供或認可的金融、投資、交易或其他類型的意見或建議。請在使用條款閱讀更多資訊。