I am looking for a way to take an accumulation/distribution indicator and normalize it so I can compare a bunch of stocks with stock prices that have no relationship with each other. EDIT: This would be straightforward on interday data, but the problem is with intraday data: you can't get an intraday normalized volume measure, because the volume is always skewed where there is huge volume in the first and last half out, and relatively meaningless volume otherwise.

What I am currently doing is dividing the different AD measures by 1,000 and then capping at +/-80 anything above/below that:

closeVsLow = (close - low) ;
closeVsHigh = (high - close) ;
closeVsOpen = (close - open) ;
myrange = (high - low) ;
myVolume = volume;

AD= ((closeVsLow - closeVsHigh) / myrange) * volume;
pvalue = acdOne / 1000;
if (pvalue > 80) then pValue = 80;
if (pvalue < -80) then pValue = -80;

Is there a better way of normalizing this?

  • $\begingroup$ Couldn't you just look at the CLV? That's the AD you have above without the volume component. Every stock will thus be in the same range of -1 to +1. $\endgroup$ Commented Sep 22, 2012 at 13:31
  • $\begingroup$ You're right about the CLV, but in this case the volume is the most important piece. This is an intr-day indicator, and AD is the only intra-day volume indicator that is actually useful. Other intraday volume measures are useless..they just show huge volume beginning/end of day, and relatively nothing happen in between. $\endgroup$ Commented Sep 22, 2012 at 16:44

1 Answer 1


If I understand well, one part of your analytic is already normalized ((closeVsLow - closeVsHigh) / myrange), but not the other (volume). If you just aim to compare the values of AD from any stock with the other, why not normalizing volumeby the usual daily volume (median of the daily volume) of the stock during the last 60 days?

Moreover, if you really want to detect outliers in terms of relative positioning of open, high, low and close, I suggest you read the following paper by Garman and Klass: On the Estimation of Security Price Volatility from Historical Data.

Of course the volume will not be taken into account (they just use a diffusive assumption on the price). But if you consider that volume should be homogenous to time in usual conditions, then you can extract from their paper an interesting theoretical statistic on how normal is the value of the conjunction of (open,high,low,close,volume). There is a paper by Jemery Large: Estimating Quadratic Variation When Quoted Prices Change by a Constant Increment that could be useful to read too.

  • $\begingroup$ Thanks for your post. But I left out an important piece of information: This measure is an intraday measure only. I corrected my post. $\endgroup$ Commented Sep 23, 2012 at 18:35

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