AI SuperTrend Clustering Oscillator

The AI SuperTrend Clustering Oscillator identifies three key outputs—bullish, neutral, and bearish—derived from variations between multiple SuperTrend indicators.

🔶 FUNCTIONALITY

The oscillator consists of three primary elements:

1. Bullish Output: Represents the most optimistic signal, always the highest value.
2. Bearish Output: Reflects the most pessimistic signal, always the lowest value.
3. Consensus Output: Falls between the bullish and bearish outputs, serving as an overall trend indicator.

A positive consensus value suggests an upward trend, while a negative value indicates a downward trend. Adjusting the minimum factor focuses the analysis on broader trends, while tweaking the maximum factor sharpens the focus on shorter-term fluctuations.

Strong trends are highlighted when the bullish and bearish outputs diverge. For instance:

• A strong bullish trend occurs if the bearish output rises above zero.
• A strong bearish trend emerges if the bullish output drops below zero.

When the consensus output aligns with a trend direction but the bullish or bearish outputs conflict, it could signal an impending reversal or correction.

🔶 MECHANISM

The indicator operates by clustering differences between the closing price and multiple SuperTrend values, calculated using a range of factors. These differences are grouped into three clusters:

• Bullish Cluster: Contains the largest differences, representing optimistic market behavior.
• Bearish Cluster: Contains the smallest differences, representing pessimistic market behavior.
• Consensus Cluster: Contains intermediate differences, capturing neutral behavior.

This clustering process is applied across the historical data for comprehensive analysis.

🔶 CONFIGURATION OPTIONS

• ATR Length: Defines the period for calculating Average True Range (ATR) used in SuperTrends.
• Factor Range: Sets the minimum and maximum factors for SuperTrend calculations.
• Step: Determines the interval between factor values.
• Smooth: Adjusts the degree of smoothing applied to the outputs.

🔹 Performance Settings

• Maximum Iterations: Limits the number of steps for centroid determination. Lower values speed up processing but may reduce clustering accuracy.
• Historical Bars: Specifies the range of past data (in bars) for calculations.