the question relates to CPU usage. Does it take a lot of CPU for Ninja to supple an indicator value multiple times?
The answer depends on if Ninja calculates the value from scratch everytime, or if it retains the calculation somewhere behind the scenes so that another access of it does not incur any overhead.
Let's take an example. If I want to know Stochastics(...)[0], Ninja has to calculate the Stochastics series, then give me the value for the current bar.
Then imagine I want to know Stochastics(...)[5]. Does it calculate all over again? Or does it retain the DataSeries from before so it can just immediately grab the required data without more CPU?
I'm asking because with pattern recognition of oscillators there is a LOT of accessing across past data, over and over again.
What method is the most CPU efficient?
Thanks,
saltminer

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