Average tester – are averages any good?

Vonasi · Tue, 23 Feb 2021 10:11:14 +0000

I have always hated averages. They are horrible lagging things that can easily be curve fitted but I decided to code something to see a little more easily how bad they are and to see if any of the popular average types are better than others. So I coded this simple code that uses arrays to record the average gain/loss of any candle if it is preceded by a candle that meets a preset average condition. This condition can be something like close > average or low > average or even fast average > slow average. This tests long conditions but you can also test short by using the reverse condition. The 8 most popular average types are tested so that their performance can be compared. The result is the average gain per candle. A datum of every candle on the chart is also calculated so we can see how our average condition performance compares to buy and hold. //Period setting for average //p = 24 if barindex > 1 then //Choose only 1 of these result conditions // c1 = close - open //for long //c1 = open - close //for short if islastbarupdate then $lastopen[0]=open endif if open<>$lastopen[0] then for t = 0 to 8 //Choose only 1 of these entry conditions // //Long Conditions //avgcond = low[1] > average[p,t](customclose)[1] //Low is above average //avgcond = close[1] > average[p,t](customclose)[1] //Close is above average //avgcond = average[p,t](close[1]) > average[p,t](close[2]) //Average is rising avgcond = average[p,t](close[1]) > average[p*2,t](close[1]) //Fast average is above slow average //Short Conditions //avgcond = high[1] < average[p,t](customclose[1]) //High is below average //avgcond = close[1] < average[p,t](customclose[1]) //Close is below average //avgcond = average[p,t](close[1]) < average[p,t](close[2]) //Average is falling //avgcond = average[p,t](close[1]) < average[p*2,t](close[1]) //Fast average is below slow average if avgcond then $count[t] = $count[t] + 1 if c1 then $total[t] = $total[t] + c1 $avg[t] = ($total[t]/$count[t]) endif endif next endif dcount = dcount + 1 dtotal = dtotal + c1 davg = (dtotal/dcount) endif return $avg[0] as "Simple", $avg[1] as "Exponential", $avg[2] as "Weighted", $avg[3] as "Wilder", $avg[4] as "Triangular", $avg[5] as "End Point", $avg[6] as "Time Series", $avg[7] as "Hull", $avg[8] as "Zero Lag", davg as "Datum" The first attached image is of a test on the DJI daily chart. It tests long performance if the 24 day average > 48 day average. All averages have a lower average gain per candle than the buy and hold datum except for Hull, Zero Lag, Time Series and End Point averages where the performance is improved. So perhaps a simple filter like this can improve a strategy? The second image tests if close > 24 day average and only the Time Series average beats buy and hold.

This topic has 15 replies, 4 voices, and was last updated 3 years ago by Khaled.

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04/10/2021 at 8:32 PM #166737

Khaled

Hi Vonasi, based on my little experience, I agree with you on the suboptimal indication given by MA, too much lag. Typically, all the indicators are lagging, but there are a few which lag less than others. MA and MACD are definitly in the bottom of the list. My basic conclusion is that price action and volume should be at the heart of any system. The way I use MA as a filter is to compare Slopes of Fast vs Slow with a sensitive MA like Hull.

Khaled
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