Named after Charles Spearman and denoted by the Greek letter ‘ρ’ (rho), the Spearman rank correlation coefficient is the nonparametric version of the Pearson correlation coefficient. Use the Spearman rank correlation when you have two ranked variables, and you want to see whether the two variables covary. That is, as one variable increases/decreases, the other variable tends to increase/decrease respectively.
It is best used to discover if two variables (and in this first version of the indicator, two ticker symbols) increase and decrease together. There are advantages to using this version of correlation vs. the Pearson R.
The value oscillates between +1 and -1.
- A value of +1 means the two variables are perfectly correlated, that is they are increasing and decreasing together in perfect harmony.
- A value of -1 means the two variables exhibit a perfect negative correlation, that is they increase and decrease oppositely.
- A value of zero means the two variables are not correlated at all (noise).
- Removed the 'lookahead = true' parameter from the security function, not needed.
- Fixed logic of duplicates to be more straightforward.
- Fixed issue with hLines.
- Updated symbol input to input.symbol.
Question: is there a simple way to make it a function? For example, I want to make a correlation "ribbon" with 20 correlations, each a different length:
I will understand if its not feasible in the current format...but i wanted to ask!