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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.
仅限邀请脚本
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作者的说明
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
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。
仅限邀请脚本
只有经作者批准的用户才能访问此脚本。您需要申请并获得使用权限。该权限通常在付款后授予。如需了解更多详情,请按照以下作者的说明操作,或直接联系sbagdai。
除非您完全信任其作者并了解脚本的工作原理,否則TradingView不建议您付费或使用脚本。您还可以在我们的社区脚本中找到免费的开源替代方案。
作者的说明
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
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。