SuperTrend AI Clustering

The SuperTrend AI indicator is a novel take on bridging the gap between the K-means clustering machine learning method & technical indicators. In this case, we apply K-Means clustering to the famous SuperTrend indicator.

🔶 USAGE

Users can interpret the SuperTrend AI trailing stop similarly to the regular SuperTrend indicator. Using higher minimum/maximum factors will return longer-term signals.

The displayed performance metrics displayed on each signal allow for a deeper interpretation of the indicator. Whereas higher values could indicate a higher potential for the market to be heading in the direction of the trend when compared to signals with lower values such as 1 or 0 potentially indicating retracements.

In the image above, we can notice more clear examples of the performance metrics on signals indicating trends, however, these performance metrics cannot perform or predict every signal reliably.


We can see in the image above that the trailing stop and its adaptive moving average can also act as support & resistance. Using higher values of the performance memory setting allows users to obtain a longer-term adaptive moving average of the returned trailing stop.

• ATR Length: ATR period used for the calculation of the SuperTrends.
• Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
• Step: Increments of the factor range.
• Performance Memory: Determine the degree to which older inputs affect the current output, with higher values returning longer-term performance measurements.
• From Cluster: Determine which cluster is used to obtain the final factor.

• Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
• Historical Bars Calculation: Calculation window of the script (in bars).