Normalizing price velocity/volatility with respect to what?

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jklatt
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Normalizing price velocity/volatility with respect to what?

Post by jklatt » Tue Jun 19, 2007 4:47 pm

A lot of talk is about how you adjust your position size with respect to a given market's volatility, but I'm wondering if anybody had any ideas on how one might normalize a markets velocity and/or volatility over a given period of time.

My first idea was to take average true range over a given period and divide by the average price during the same period. Commodity X roughly has a range of Y percent each day for the last Z days. Seemed straight forward enough.

The problem is that my historical data (from Pinnacle) is adjusted in a fashion that allows price to go negative. Not only do the negative numbers foul up the calculation, but also very small closing prices result in a very high percentage which isn't "true".

So does anybody have any ideas on a simpler, possibly more robust, way to measure price velocity or volatility and normalize those figures across markets?

sluggo
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Post by sluggo » Tue Jun 19, 2007 5:42 pm

How do you feel about the ratio
  • (Linear Regression Slope) / (Average True Range)
Its units are "ATRs Per Day" which is comparable between different markets. Negative prices don't impact its calculations. It has the nice property that it gives approximately the same numerical value for Fullsize S&P as for eMini S&P, which you definitely want since these markets move essentially in lockstep.

If you want to get fancy you can modify the numerator. You can choose to take the linear regression slope of a smoothed version of price, rather than price itself. For example you could take the linear regression slope of a 3-bar EMA of ((H+L)/2). Fans of the Jurik Moving Average will have additional devious ideas.

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