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This script was developed for personal use and the idea is spotting candles that are at least 99% bigger than average (using N = 3) as they will cross the upper and lower confidence interval limits. N = 2 would roughly provide a 95% confidence interval.

I recommend the standard values N = 2 and N = 3 that provide, respectively, approximately 95% and 99% confidence intervals.

Note: I suggest using smaller sample sizes (between the 30 and 100 last candles) for sigma estimation as they tend to represent better the recent volatility. I also suggest to use sample size=400 for long-term average volatility.

Remark: the original interpretation is a bit misleading. When the series crosses over the interval limits, one can say that the current candle length is 95% or 99% as extreme as the expected length.

Also in hypothesis testing, one could say that the hypothesis of the candle length being within the expected range is rejected at 5% or 1% significance level.

As closing prices can be seen as a random walk on chart, this series is basically modelling its error.

An analogous approach for candle length is just thinking of it as changes in the closing price ( I would rename ir as Price Change Outlier Detector if TV allowed it!).

A green column means a positive change in price and a red column means a negative change in price. These changes are always relative to the last price.

If a column crosses from from the inner band to the outer band, the change in price is considered to be approximately 95% as extreme as expected.

If it crosses both bands, the change in price it considered to be approximately 99% as extreme as expected.

版本注释:
Minor bug fix.

版本注释:
Fixed sigma estimator so it doesn't depend on the present value.

版本注释:
Fixing last fix.