Spearman’s Rank Correlation

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ubi is the sum of squares of rank differences, is the number of paired observations.

It is reasonable to use the rank correlation when there is just a small amount of observations. This method can be used for both numerical data and in the cases, when the registered values are detected by attributes of various intensity. The source of the above description was found hic.

This indicator is one of oscillators. tamen, being compared to stochastic oscillator, it is smoother. Praeterea, it does not delay in pivot points.
The only external parameter that influences the calculation algorithms is rangeN. It sets the amount of bars, for which we are trying to find regularities. If rangeN = 14, then we take the close price sequence Close[ego], Close[i+1], ... Close[i+rangeN-1], and construct a rank sequence for them, i.e., we find the location of each close price when the sequence is sorted. In hoc casu, one real chart turns to be compared to another, monotone increasing chart.

The parameter of direction means sorting from the highest to the lowest value (verum) or from the lowest to the highest value (falsus). The value of verum shows a more usual picture, whereas falsus produces an inverted image. Parameter CalculatedBars is introduced in order to limit the amount of bars under calculation, to save CPU resources (though that wasn't needed). Zero value of this parameter means that calculations will be performed for all history available. Parameter Maxrange = 30 sets the maximum calculation period. This parameter was introduced in order to save resources, as well, so maybe somebody will need it.