Re: Indicator levels & iCustom

2
Cladi39 wrote:Dear mladen, i want use this indicator iCustom function, but im interested when cross levels 85 and 15 like in the picture, dont know how to do beacouse the indicator seems to be based on Bollinger Bands and have dinamic levels.
No, it does not have dynamical levels
You can simply call it using first buffer and compare that value to desired level you want to check

Re: Indicator levels & iCustom

4
Cladi39 wrote:Thanks soo much for your help mladen but dont work, thats levels 15 and 85  in the picture are for other indicator, this indicator have other levels thats change with the movement of the price, something like -28000.
You must use the levels that indicator reaches or you have to normalize the values of the indicator you want to use into desired range and then use levels of your choice


IdeaRe: Indicator levels & iCustom

8
Cladi39 wrote:Thanks for the definition mladen but i need to learn how to do it.
Cladi39

There are following 6 types defined there :

  1. Standard Score
    • Normalizing errors when population parameters are known. Works well for populations that are normally distributed
  2. Student's t-statistic
    • The departure of the estimated value of a parameter from its hypothesized value, normalized by its standard error.
  3. Studentized residual
    • Normalizing residuals when parameters are estimated, particularly across different data points in regression analysis.
  4. Standardized moment
    • Normalizing moments, using the standard deviation {\displaystyle \sigma }\sigma as a measure of scale.
  5. Coefficient of variation
    • Normalizing dispersion, using the mean \mu as a measure of scale, particularly for positive distribution such as the exponential distribution and Poisson distribution.
  6. Min-max feature scaling
    • Feature scaling is used to bring all values into the range [0,1]. This is also called unity-based normalization. This can be generalized to restrict the range of values in the dataset between any arbitrary points {\displaystyle a}a and {\displaystyle b}b, using for example

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For you probably the last is the most interesting : that is much better known as "raw" stochastic (or min/max normalization) than feature scaling. If you apply stochastic to any value, then you are going to get those normalized values


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