Moving Average

Vertical Horizontal Filter Adaptive Moving Average (VHF-AMA) – Test Results

The Adaptive Moving Average (AMA) modifies the amount of smoothing it applies to data in an attempt to adjust to the changing needs of a dynamic market. It makes these adjustments based on the readings from a Volatility Index (VI). Any measure of volatility or trend strength can be used, however in this article we will focus on how the AMA performs using the Vertical Horizontal Filter (VHF).

The VHF-AMA requires four user selected inputs: A Vertical Horizontal Filter period, a High – Low smoothing period range for the AMA and a power that Alpha is raised to. With four variables there are thousands of possible combinations so we had to make some educated assumptions based on our previous tests to narrow the choices down.

In our tests on the Vertical Horizontal Filter in a VHF-VMA we revealed that VHF periods of 126, 252 and 80 produced the best results. Because Volatility Index settings have proven to produce similar results in both the VMA and the AMA, testing these three settings should be sufficient to capture the best results. Also they corresponded with the approximate number of trading days in standard calendar periods: 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year, so:

VHF = 80, 126, 252

In previous tests we have seen that a moving average range produces the best results when it can move to as little as 4 periods or less, therefore we will test:

AMA Actual Fast Moving Average (FN) = 1, 4, 10

With the slow moving average we have consistently seen 300 produce the best results while changing this setting hasn’t usually made a big impact. However we still ran tests through several settings:

AMA Actual Slow Moving Average (SN) = 200, 250, 300

For the Alpha Power we also tried several variables:

Alpha Power (P) = 0.5, 0.75, 1, 1.5, 2, 2.5

We tested trades going Long, using Daily data, taking End Of Day (EOD) signals~ analyzing all combinations of the above settings.

Each time the Alpha Power was adjusted the SC and FC had to be modified to account for the change but the actual FN and SN stayed the same.

For instance a SC – FC range of 1 – 24 with alpha ^ 2 has an actual FN – SN range of about 1 – 300 due to the effect of squaring alpha. Here is a table that shows the SC – FC ranges used so that the FN – SN ranges stayed constant regardless of ‘P’:

SC and FC values used to keep FN and SN constant as P was changed.

If that doesn’t make a lot of sense then please read our explanation of the Adaptive Moving Average. A total of 162 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 162 VHF-AMA Test Results

Vertical Horizontal Filter Adaptive Moving Average – Test Array

Vertical Horizontal Filter AMA - Ann Return as Alpha Power is Changed

Above we have charted the annualized returns achieved from each VHF with Alpha raised to different powers along the X axis. The chart on the left shows the results when the FN = 1 and SN = 300 while on the right FN = 4 and SN = 300. Clearly keeping the FN at 1 is important to achieve the best returns. A VHF period of 126 stood out as the best performer and this echoes previous tests. Finally, when FN = 1, raising Alpha to the power of 1.5 yielded the best results.

Best Vertical Horizontal Filter Adaptive Moving Average

126 Day VHF-AMA, EOD 1, 56 ^ 1.5 L - Performance

Included on the above chart is the performance of the 126 Day FRAMA, EOD 4, 300 Long because so far this has been the best performing Moving Average. The 126 Day VHF-AMA, EOD 1, 56 Long ^ 1.5 produced extremely similar results and even had the same average trade duration of 14 days. However it did slightly underperform the FRAMA by most measures but lets take a quick look under the hood to see what makes it tick:

126 Day VHF-AMA, EOD 1, 56 ^ 1.5 – Smoothing Period Distribution

126 Day VHF-AMA, EOD 1, 56 ^ 1.5 - Smoothing Distribution

As you can see the VHF-AMA does not have nearly as much of a spread with its smoothing range as the FRAMA. A larger range makes the FRAMA more able to adapt to different market environments.

126 Day VHF-AMA 1, 56 ^ 1.5 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day VHF-AMA 1, 56 ^ 1.5 and compared it to the best performing FRAMA and the best VHF-VMA to see if we could learn what makes a good volatility index for use in an AMA:.

126 Day VHF-AMA, EOD 1, 56 ^ 1.5 - Alpha Comparison

The VHF does not look as though it can change as nimbly as the FRAMA while both the AMA and VMA using a VHF look very similar.

Excel Spreadsheet

The VHF is outstanding for use in an AMA and we have build an excel spreadsheet for you to download free so you can have a play. Simply use the flowing link and you will find it under Downloads – Technical Indicators: Adaptive Moving Average (AMA).

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long for each average tested was generated with a close above that average and an exit signal was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) signals on Daily data. Eg. Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long and to close below that Average to close the position.
We used the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – AMA Indicator Equivalent

Relative Volatility Index Adaptive Moving Average (RVI-AMA) – Test Results

The RVI-AMA requires five user selected inputs: A Standard Deviation period, a Wilder’s Smoothing period, a High – Low smoothing period range for the AMA and a power that Alpha is raised to. With five variables there are thousands of possible combinations so we had to make some educated assumptions based on our previous tests to narrow the choices down.

In our tests on the Relative Volatility Index in a RVI-VMA we revealed that a Wilder’s Smoothing (WS) period of 14 worked the best and there is no reason to suggest that this will not also be true for a RVI-AMA so:

WS = 14

We selected the SD lengths that corresponded with the approximate number of trading days in standard calendar periods: 10 days = two weeks, 20 days = 1 month, 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year:

SD = 10, 20, 40, 80, 126, 252

In previous tests we have seen that a moving average range produces the best results when it can move to as little as 4 periods or less, therefore we will test:

AMA Actual Fast Moving Average (FN) = 1, 4, 10

AMA Actual Slow Moving Average (SN) = 100, 150, 200, 250, 300

For the Alpha Power we also tried several variables:

Alpha Power (P) = 0.5, 0.75, 1, 1.5, 2, 2.5

We tested trades going Long, using Daily data, taking End Of Day (EOD) signals~ analyzing several combinations of the above settings.

Each time the Alpha Power was adjusted the SC and FC had to be modified to account for the change but the actual FN and SN stayed the same.

SC and FC values used to keep FN and SN constant as P was changed.

If that doesn’t make a lot of sense then please read our explanation of the Adaptive Moving Average. A total of 321 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 321 RVI-AMA Test Results

Relative Volatility Index Adaptive Moving Average – Test Array

Relative Volatility Index AMA - Ann Return as Alpha Power is Changed

Above we have charted the annualized returns achieved from each RVI with Alpha raised to different powers along the X axis. The chart on the left shows the results when the FN = 1 and SN = 300 while on the right FN = 4 and SN = 300. Clearly keeping the FN at 1 is important to achieve the best returns. There was no SD period that really stood out so we shall go with 126 because of how it has performed in past tests. Finally, when FN = 1, raising Alpha to the power of 0.5 clearly yielded the best results.

Best Relative Volatility Index Adaptive Moving Average

126 Day RVI-AMA, EOD 1, 45300 ^ 0.5 (WS 14) - Performance

Included on the above chart is the performance of the 126 Day FRAMA, EOD 4, 300 Long because so far this has been the best performing Moving Average. The 126 Day RVI-AMA, EOD 1, 45300 Long ^ 0.5 (WS 14) produced an extremely fast moving average with a typical trade duration of just 4 days. This makes it unpractical for a real world application. Add to this the fact that it underperformed the best the FRAMA and this indicator is hardly worthy of further testing. However lets take a quick look under the hood to see what makes it tick and the causes of its weaknesses:

126 Day RVI-AMA, EOD 1, 45300 ^ 0.5 (WS 14) – Smoothing Period Distribution

126 Day RVI-AMA, EOD 1, 45300 ^ 0.5 (WS 14) - Smoothing Distribution

Instantly you can see a big problem; there isn’t really any smoothing distribution at all from the 126 Day RVI-AMA, EOD 1, 45300 ^ 0.5 (WS 14), instead it is basically a 2 day EMA. The far better performing FRAMA 0n the other hand has a wide spread of smoothing making on it much more adaptive to changing market conditions.

126 Day RVI-AMA 1, 45300 ^ 0.5 (WS 14) – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day RVI-AMA 1, 45300 ^ 0.5 (WS 14) and compared it to the best performing FRAMA and the best RVI-VMA to see if we could learn what makes a good volatility index for use in an AMA:.

126 Day RVI-AMA, EOD 1, 45300 ^ 0.5 (WS 14) - Alpha Comparison

Higher alpha readings result in a faster average and instantly you can see the RVI-AMA has a very high Alpha compared to the best RVI-VMA and FRAMA. Remember the RVI-AMA and the RVI-VMA both use the same volatility index but the different ways that the two modify Alpha result in a very different outcome.

Excel Spreadsheet

The RVI-AMA is not very useful but should you want to test it or another volatility index then we have build an excel spreadsheet for you to download free. Simply use the flowing link and you will find it under Downloads – Technical Indicators: Adaptive Moving Average (AMA).

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long for each average tested was generated with a close above that average and an exit signal was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) signals on Daily data. Eg. Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long and to close below that Average to close the position.
We used the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – AMA Indicator Equivalent

Standard Deviation Ratio Adaptive Moving Average (SDR-AMA) – Test Results

The SDR-AMA requires five user selected inputs: SD1, SD2, a High – Low smoothing period range for the AMA and a power that Alpha is raised to. With five variables there are thousands of possible combinations so we had to make some educated assumptions based on our previous tests to narrow the choices down.

First of all we have seen that nearly all of the performance characteristics exhibited by a VI have rung true in tests on both a VMA and an AMA. When we tested the SDR in a VMA we found that it was best if SD1 was around half of SD2. We also selected SD lengths that corresponded with the approximate number of trading days in standard calendar periods: 10 days = two weeks, 20 days = 1 month, 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year:

SD1/SD2 = 10/20, 40/80, 80/126, 126/252

Second we have seen that a moving average range produces the best results when it can move to as little as 4 periods or less, therefore we will test:

AMA Actual Fast Moving Average (FN) = 1, 4

AMA Actual Slow Moving Average (SN) = 100, 150, 200, 250, 300

For the Alpha Power we also tested several variables:

Alpha Power (P) = 0.5, 0.75, 1, 1.5, 2, 2.5

We tested trades going Long, using Daily data, taking End Of Day (EOD) signals~ analyzing several combinations of the above settings.

Now each time the Alpha Power was adjusted the SC and FC had to be modified to account for the change but the actual FN and SN stayed the same.

SC and FC values used to keep FN and SN constant as P was changed.

If that doesn’t make a lot of sense then please read our explanation of the Adaptive Moving Average. A total of 240 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 240 SDR-AMA Test Results

Standard Deviation Ratio Adaptive Moving Average – Test Array

Standard Deviation Ratio AMA - Ann Return as Alpha Power is Changed

Above we have charted the annualized returns achieved from each SDR with Alpha raised to different powers along the X axis. The chart on the left shows the results when the FN = 1 and SN = 300 while on the right FN = 4 and SN = 300. Clearly extending the FC to 4 had a positive effect and the best returns were achieved with a SDR of 126/252 where Alpha was raised to the power of 2.

Best Standard Deviation Ratio Adaptive Moving Average

126/252 Day SDR-AMA, EOD 2, 24 Long ^ 2

Included on the above chart is the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126 Day SDR-AMA, EOD 2, 24 Long ^ 2 performed OK but could not best the FRAMA and has a much shorter average trade duration; just 8 days compared to 14 for the FRAMA. For these reasons the FRAMA remains our preferred moving average and the SDR-AMA does not warrant further testing. But lets take a quick look under the hood:

126 Day SDR-AMA, EOD 2, 24 ^ 2 – Smoothing Period Distribution

126/252 Day SDR-AMA, EOD 2, 24 ^ 2 Smoothing Period Distribution

The smoothing distribution of the 126 Day SDR-AMA, EOD 2, 24 ^ 2 is much more localised around the 4 – 20 range than the FRAMA which explains the shorter trade duration. The FRAMA on the other hand allows the average to move much slower at times, presumably when the trend is weak.

126 Day SDR-AMA 2, 24 ^ 2 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day SDR-AMA 2, 24 ^ 2 and compared it to the best performing FRAMA and the best SDR-VMA to see if there were any similarities that would reveal what makes a good volatility index:.

126/252 Day SDR-AMA, EOD 2, 24 ^ 2 - Alpha Comparison

Remember higher alpha readings result in a faster average. The SDR-AMA and the SDR-VMA are clearly both much faster than the FRAMA. However the SDR-AMA does slightly outperform the SDR-VMA and notice that the SDR-AMA’s Alpha moves through a greater range from high to low. This greater ‘adaptability’ is likely to have been a key factor in its better performance.

Excel Spreadsheet

Want to use this indicator? Get a free Excel spreadsheet at the flowing link under Downloads – Technical Indicators: Adaptive Moving Average (AMA). It will automatically adjust to your choice of many different VIs including the Standard Deviation Ratio used in this article.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long for each average tested was generated with a close above that average and an exit signal was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) signals on Daily data. Eg. Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long and to close below that Average to close the position.
We used the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – AMA Indicator Equivalent

Fractal Dimension Adaptive Moving Average (D-AMA) – Test Results

We did have to make one slight modification to the Fractal Dimension however. The Volatility index in an AMA needs to shift through a 0 – 1 range where higher readings indicate a stronger trend. The Fractal Dimension shifts through a 1 – 2 range where lower readings indicate a stronger trend. Therefore we shall use = ABS(D – 2).

The D-AMA requires four user selected inputs: A Fractal Dimension Period, a High – Low smoothing period range for the AMA and a power that Alpha is raised to. We tested trades going Long, using Daily data, taking End Of Day (EOD) and End of Week (EOW) signals~ analyzing combinations of:

D = 40, 80, 126, 252

Alpha Power (P) = 0.5, 0.75, 1, 1.5, 2, 2.5

AMA Actual Fast Moving Average (FN) = 1, 4, 10, 20, 40, 60

AMA Actual Slow Moving Average (SN) = 100, 150, 200, 250, 300

The D lengths were selected due to the fact that they correspond with the approximate number of trading days in standard calendar periods: 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year. In many of out past tests we have also tested VI lengths of 10 and 20 days, however these setting have always failed to yield the best results so we felt that it would be safe to omit them from this set of tests.

The AMA ranges were selected because they should capture the best results based on what we know from previous research into moving averages. Each time the Alpha Power was adjusted the SC and FC had to be modified to account for the change but the actual FN and SN stayed the same.

For instance a SC – FC range of 1 – 24 with alpha ^ 2 has an actual FN – SN range of 1 – 300 due to the effect of squaring alpha. Here is a table that shows the SC – FC ranges used so that the FN – SN ranges stayed constant regardless of ‘P’:

SC and FC values used to keep FN and SN constant as P was changed.

If that doesn’t make a lot of sense then please read our explanation of the Adaptive Moving Average. A total of 960 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 960 D-AMA Test Results

Fractal Dimension Adaptive Moving Average – Modifying Alpha by Raising to a Power

Kaufman had a theory that by squaring Alpha and thus causing the AMA to slow rapidly when the data lacked a strong trend he would achieve better results. When we tested this theory on the ER-AMA we found it to be false but with a different VI we may reach a different conclusion. So lets look at the affect of raising Alpha to different powers:

Fractal Dimension – AMA, Alpha to the Power of 1 – Annualized Return

Fractal Dimension - AMA ^ 1 - Annualized Return

With Alpha ^1 there is no modification to Alpha at all. Clearly as the FC is increased the returns decline and as the FC gets higher the change in SC has more impact. Generally it appears as though a SC of 100 is best on a D-AMA with an unmodified Alpha. ER lengths of 80 and 126 yielded the best returns, this finding is similar to that of our previous tests on other ‘intelligent’ moving averages.

Fractal Dimension – AMA to the Power of 2 – Annualized Return

Fractal Dimension - AMA ^ 2 - Annualized Return

By raising Alpha to the power of 2, returns at almost all the data points increased which is just the opposite of what we experienced when testing the ER-AMA. This shows that Kaufman’s theory of rapidly slowing the AMA during times where a trend is lacking had merit but is dependent on the VI being used.

The best results again came from ER lengths of 80 and 126 although an ER length of 40 did produce some notable returns. Changing the SC did not have as much of an effect with Alpha ^2 compared to Alpha ^ 1 however a SC of 24 (SN equivalent of 300) and a short FC tends to produce the best results. So lets rework the charts to focus on what we now know works best:

D-AMA Annualized Return with Alpha to Different Powers

Now we are only looking at ER periods of 40, 80 and 126 with a FN of 1 and 4 and a SN of 300. Each data point plots the change in returns with Alpha raised to different powers. As you can see, the best returns resulted from an ER period of 126 with alpha raised to the power of 2. Therefore when using the Fractal Dimension in an Adaptive Moving average you are best to square alpha as suggested in the original formula..

Best EOD Fractal Dimension Adaptive Moving Average

126 Day D-AMA, EOD 2, 24 Long ^ 2

I have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126 Day D-AMA, EOD 2, 24 Long ^ 2 put up a good fight against the FRAMA but ultimately underperformed by most measures to a small degree. On the Short side, the the D-AMA also underperformed slightly.

126 Day D-AMA, EOD 2, 24 ^ 2 – Smoothing Period Distribution

126 Day D-AMA 2, 24 ^ 2 Smoothing Period Distribution

Looking at the smoothing distribution you can see the 126 Day D-AMA, EOD 2, 24 ^ 2 is very similar to that of the 126 Day FRAMA, EOD 4, 300 but the FRAMA allows the average to slow down more often.

126 Day D-AMA 2, 24 ^ 2 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day D-AMA 2, 24 ^ 2 and compared it to the best performing FRAMA and the best D-VMA to see if there were any similarities that would reveal what makes a good volatility index:.

126 Day D-AMA 2, 24 ^ 2 - Alpha Comparison

You can clearly see that all three use the same VI, the only difference is how they manipulate Alpha. Remember, higher readings result in a faster average so the D-AMA is obviously the fastest of the three while the FRAMA appears to shift through the widest range.

Best EOW Fractal Dimension Adaptive Moving Average

There are times when an average with a longer trade duration better suits ones needs so we also ran the tests looking for the best average using EOW signals, here is the one that came out trumps:

252 Day D-AMA. EOW 111, 372 Long ^ 0.75

We have included on the above chart the performance of the 252 Day FRAMA, EOW 40, 250 Long becuase so far this has been the best performing EOW Moving Average. The 252 Day D-AMA, EOW 111, 372 Long ^0.75 is almost identical to the FRAMA but does outperform it by a fraction. They are so similar in fact that they may as well be the same average. Performance on the short side tells the same story.

252 Day D-AMA, EOW 111, 372 ^ 0.75 – Smoothing Period Distribution

252 Day D-AMA, EOW 111, 372 ^ 0.75 - Smoothing Period Distribution

The smoothing distribution for the 252 Day D-AMA 111, 372 ^ 0.75 has a smaller range than that of the 252 Day FRAMA 40, 250 but the median, lower quartile and minimum are almost identical. You can view an Alpha Comparison Here.

Conclusion

In our tests on the ER-AMA we came to the conclusion that the squaring of alpha as suggested in the standard AMA formula was actually detrimental to performance. However when using the Fractal Dimension as the VI, squaring Alpha was beneficial. Therefore the best Power to use in manipulating alpha varies depending on the VI in use.

Overall the D-AMA produced results that were near identical to that of the FRAMA but the D-AMA is a slightly faster average. The best performing EOD D-AMA was the 126 Day ER-AMA, EOD 2, 26 ^ 2 while the best EOW or ‘slower’ moving average was the 252 Day D-AMA, EOW 111, 372 ^ 0.75.

It is very difficult to pick between the FRAMA and the D-AMA but becuase the FRAMA offers a slightly longer trade duration it the best Moving Average we have tested so far.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) and End Of Week (EOW) signals on Daily data. Eg. Daily data with an EOW signal would require the Week to finish above a Daily Moving Average to open a long or close a short while Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long or close a short and vice versa.
We used the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – AMA Indicator Equivalent

Efficiency Ratio Adaptive Moving Average (ER-AMA) – Test Results

The ER-AMA requires four user selected inputs: An Efficiency Ratio Period, a High – Low smoothing period range for the AMA and a power that Alpha is raised to. We tested trades going Long, using Daily data, taking End Of Day (EOD) and End of Week (EOW) signals~ analyzing combinations of:

ER = 10, 20, 40, 80, 126, 252

Alpha Power (P) = 0.5, 0.75, 1, 1.5, 2, 2.5

AMA Actual Fast Moving Average (FN) = 1, 4, 10, 20, 40, 60

AMA Actual Slow Moving Average (SN) = 100, 150, 200, 250, 300

The ER lengths were selected due to the fact that they correspond with the approximate number of trading days in standard calendar periods: 10 days = 2 weeks, 20 days = 1 month, 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year.

SC and FC values used to keep FN and SN constant as P was changed.

If that doesn’t make a lot of sense then please read our explanation of the Adaptive Moving Average. A total of 1020 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 1020 ER-AMA Test Results

ER Adaptive Moving Average – Modifying Alpha by Raising to a Power

Kaufman had a theory that by squaring Alpha and thus causing the AMA to slow rapidly when the data lacked a strong trend he would achieve better results. Here we will put this theory to the test and be looking at the affect of raising Alpha to different powers:

Efficiency Ratio – AMA, Alpha to the Power of 1 – Annualized Return

Efficiency Ratio - AMA ^ 1 - Annualized Return

With Alpha ^1 there is no modification to alpha at all and the results are impressive. It can be said that in most cases as the FC increased the returns declined while changing the SC did not have much of an impact. ER lengths of 80 and 126 yielded the best returns, this finding is similar to that of our previous tests on other ‘intelligent’ moving averages.

Efficiency Ratio – AMA to the Power of 2 – Annualized Return

Efficiency Ratio - AMA ^ 2 - Annualized Return

By raising Alpha to the power of 2 the returns drop almost across the board which immediately brings into question the need to include this function in the AMA formula and what would happen if we used a power below 1? It would appear as though the ER length needs to be at least 40 to be of value in this context with the best results again coming from ER lengths of 80 and 126. Clearly the FC is best when kept short so lets rework the charts to focus on what we now know works best:

ER-AMA Annualized Return with Alpha to Different Powers

Now we are only looking at ER periods of 80 and 126 with a FN of 1 and 4 and a SN of 300. Each data point plots the change in returns with Alpha raised to different powers. As you can see, the best returns resulted from an ER period of 126 with alpha raised to the power of 0.75. As the Power was increased beyond this point, the returns decreased almost across the board. Therefore when using an Efficiency Ratio in an Adaptive Moving average you definitely should not square alpha as suggested in the original formula..

Best EOD Efficiency Ratio Adaptive Moving Average

126 Day ER-AMA EOD 1, 1600 Long ^ 0.75

We have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126 Day ER-AMA, EOD 1, 1600 Long ^ 0.75 actually outperformed the best FRAMA up until 2008 when the market had a big pull back. As a result, over the full term of the test the FRAMA did perform slightly better. Also the FRAMA has a few added benefits such as turning a profit on the bear ravaged Nikkei 225 and having a 40% longer average trade duration (14 vs 10 Days).

On the Short side, the the ER-AMA also underperformed over the full term but again outperformed until 2008. This makes it very difficult to pick which moving average is the better of the two. But because our personal preference leans towards a longer trade duration we will stick with the FRAMA as being the best moving average we have found so far. (See the results on the short side)

126 Day ER-AMA, EOD 1, 1600 ^ 0.75 – Smoothing Period Distribution

126 Day ER-AMA, EOD 1, 1600 ^ 0.75 - Smoothing Period Distribution

Looking at the smoothing distribution you can see the 126 Day ER-AMA, EOD 1, 1600 ^ 0.75 is quite similar to that of the 126 Day FRAMA, EOD 4, 300 but the FRAMA spends more time as a slow average which explains the longer trade duration.

126 Day ER-AMA, 1, 1600 ^ 0.75 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day ER-AMA, 1, 1600 ^ 0.75 and compared it to the best performing FRAMA and the best ER-VMA to see if there were any similarities that would reveal what makes a good volatility index:.

126 Day ER-AMA, 1, 1600, ^ 0.75 - Alpha Comparison

Because the ER-AMA and the ER-VMA both use the same volatility index, obviously their Alpha is identical apart from the slight modifications caused by the separate method of manipulating alpha. Remember, the higher the Alpha the faster the resulting average so you can see why the best ER-AMA was faster moving than the best ER-VMA. The Alpha patterns of the best FRAMA and best ER-AMA do have strong similarities but notice how the FRAMA is far less volatile. It is always preferable to work with indicators that generate clean readings with low levels of noise assuming they still produce good results.

Best EOW Efficiency Ratio Adaptive Moving Average

There are times when an average with a longer trade duration better suits ones needs so we also ran the tests looking for the best average using EOW signals:

252 Day ER-AMA, EOW 10, 100 Long ^ 1

We have included on the above chart the performance of the 252 Day FRAMA, EOW 40, 250 Long becuase so far this has been the best performing EOW Moving Average. The 252 Day ER-AMA, EOW 10, 100 Long ^1 underperforms by a fraction in most measures and while almost identical to the FRAMA it certainly is not superior. Performance on the short side tells the same story.

252 Day ER-AMA, EOW 10, 100 ^ 1 – Smoothing Period Distribution

252 Day ER-AMA, EOW 10, 100 ^ 1 - Smoothing Period Distribution

Looking at the smoothing distribution for the 252 Day ER-AMA 10, 100 ^ 1 you can see that it is far faster than the 252 Day FRAMA 40, 250 and has a more limited range despite the median, upper and lower quartiles being almost identical.

252 Day ER-AMA 10, 100 ^ 1 – Alpha Comparison

252 Day ER-AMA 10, 100 ^ 1 - Alpha Comparison We have included on this chart the best EOW FRAMA and EOW ER-VMA. The ER-VMA and ER-AMA use the same volatility index but the 252 Day ER-AMA 10, 100 ^1 results in a higher Alpha which explains the faster average. The Alpha for the FRAMA and the AMA does stay in a similar zone but the FRAMA is far less volatile which is preferable.

Conclusion

In the standard formula for the AMA, alpha is squared to force the average to slow rapidly during times when there is a lack of trend. When using an Efficiency Ratio as the Volatility Index (which is most commonly used VI in an AMA) we have clearly shown that squaring Alpha has a detrimental effect on returns. We suggest instead not modifying alpha at all or raising it to the power of 0.75

Overall the ER produced some impressive returns during out tests as the VI in an AMA as it did when we used in a VMA. The best performing EOD ER-AMA was a 126 Day ER-AMA, EOD 1, 1600 Long ^ 0.75 which did show periods of out performance over our current best performing MA the 126 Day FRAMA, EOD 4, 300 Long. However due to a longer trade duration we still rate the FRAMA as superior.

When it comes to an EOW or ‘slower’ moving average the 252 Day ER-AMA, EOW 10, 100 ^ 1 is almost identical to our current best ‘slow’ MA the 252 Day FRAMA, EOW 40, 250 Long but certainly does not offer any benefits.

Both the Efficiency Ratio and the Adaptive Moving Average have proven themselves against some formidable competition but based on our findings so far the FRAMA still remains slightly superior.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) and End Of Week (EOW) signals on Daily data. Eg. Daily data with an EOW signal would require the Week to finish above a Daily Moving Average to open a long or close a short while Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long or close a short and vice versa.
We used the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – AMA Indicator Equivalent

Fractal Dimension Variable Moving Average (D-VMA) – Test Results

The Variable Moving Average (VMA) dynamically adjusts its own smoothing period to the changing market conditions based on a Volatility Index (VI). While any VI can be used, in this article we will look at how the VMA performs using the Fractal Dimension (D). This measure was originally used by John F Ehlers as a component in his Fractal Adaptive Moving Average (FRAMA) which has so far set the standard in our moving average tests.

We did have to make one slight modification to the Fractal Dimension however. The Volatility index in a VMA needs to shift through a 0 – 1 range where higher readings indicate a stronger trend. The Fractal Dimension shifts through a 1 – 2 range where lower readings indicate a stronger trend. Therefore we shall use = ABS(D – 2).

The D-VMA requires two user selected inputs: A Fractal Dimension Period and a VMA period. We tested trades going Long and Short, using Daily data, taking End Of Day (EOD) and End Of Week (EOW) signals~ analyzing all combinations of:

D = 10, 20, 40, 80, 126, 252

VMA = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50

The D lengths were selected due to the fact that they correspond with the approximate number of trading days in standard calendar periods: 10 days = 2 weeks, 20 days = 1 month, 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year.

The VMA periods were selected after preliminary tests showed that when combined with the different ER lengths they resulted in median smoothing periods between 12 and 133 days; a range that should capture the best results based on what we know from previous research into moving averages.

A total of 240 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 240 D-VMA Long and Short Test Results

D Variable Moving Average EOD vs EOW Returns:

. Fractal Dimension Variable Moving Average - Average Annualized Return, Long

It must be noted that every single D-VMA using EOD or EOW signals managed to outperform the average buy and hold annualized return of 6.32%^ during the test period (before allowing for transaction costs and slippage). This is very impressive when you consider that most fund managers fail to out perform a simple buy and hold approach.

Clearly the D periods of 126 and 252 produced the best results using both EOD and EOW signals. This echoes previous results on other ‘intelligent’ moving averages. The 126 Day D-VMA with a constant of 10 stands out as the best performer with EOD signals while the 252 Day D-VMA with a constant of 30 was the best when taking EOW signals.

Because the returns hold up so well when using EOW signals lets take a closer look at the how the probability of profit and trade duration compares for EOD and EOW signals with a D of 126 and 252:

Fractal Dimension VMA - Probability of Profit and Average Trade Duration, Long .

Clearly there is a large jump in the probability of profit and the average trade duration when using EOW signals; both are highly desirable characteristics especially if they can be achieved without sacrificing too much return.

Best EOD Efficiency Ratio Variable Moving Average

126 Day D-VMA EOD, 10 Long .

I have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126 Day D-VMA, EOD 10, Long produced almost identical results to the best that the FRAMA could produce and there is really very little between the two.

126 Day D-VMA, EOD 10 – Smoothing Period Distribution

126 Day D-VMA, EOD 10 - Smoothing Period Distribution .

The smoothing distribution for the two averages has a similar shape but the D-VMA starts from 10 and the FRAMA starts from 4.

126 Day D-VMA, 1 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day D-VMA, 1 and compared it to the best performing FRAMA to see if there were any similarities that would reveal what makes a good volatility index:

126 Day D-VMA, 10 - Alpha Comparison .

It is not surprising that the shapes of the alpha readings are almost identical because they are both based on the same Fractal Dimension reading. The only real difference is that the FRAMA tends to move to extremes more readily.

Best EOW Efficiency Ratio Variable Moving Average

252 Day D-VMA EOW, 30 Long .

I have included on the above chart the performance of the 252 Day FRAMA, EOW 40, 250 Long becuase so far this has been the best performing ‘slower’ Moving Average. The 252 Day D-VMA, EOW 30, produced almost the exact same results but did under perform ever so slightly.

252 Day D-VMA, EOW 30 – Smoothing Period Distribution

252 Day D-VMA, 30 - Smoothing Period Distribution .

The smoothing distribution for the 252 Day D-VMA, 30 is more spread than that of the FRAMA but is still very similar.

252 Day D-VMA, 30 – Alpha Comparison

252 Day D-VMA, 10 - Alpha Comparison .

Not surprisingly you can see the close similarity between the alpha readings for the D-VMA and the FRAMA thus the similar performance.

Conclusion

The fact that the D-VMA produces almost the exact same results to the FRAMA shows that the volatility index is more important than the method for translating those readings into an ‘intelligent’ moving average. However because the FRAMA offers more control and fractionally better returns it remains the best moving average we have found so far.

Want to use the Fractal Dimension Variable Moving Average? Get a free Excel spreadsheet at the flowing link under Downloads – Technical Indicators: Variable Moving Average (VMA). It will automatically adjust to one of many different VIs that you can select including the Fractal Dimension used in this article.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) and End Of Week (EOW) signals on Daily data. Eg. Daily data with an EOW signal would require the Week to finish above a Daily Moving Average to open a long or close a short while Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long or close a short and vice versa.
^ This was the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – Alpha Comparison

Relative Volatility Index Variable MA (RVI-VMA) – Test Results

The RVI-VMA requires three user selected inputs: A Standard Deviation (SD) period, a Wilder’s Smoothing (WS) period and a VMA constant. We tested trades going Long using Daily data taking End Of Day (EOD) signals~ analyzing all combinations of:

SD = 10, 20, 40, 80, 126, 252

WS = 9, 14, 19

VMA = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50

The SD lengths were selected due to the fact that they correspond with the approximate number of trading days in standard calendar periods: 10 days = 2 weeks, 20 days = 1 month, 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year.

The WS periods were selected because the standard setting for a RVI is 14 and it makes sense to test a few days either side of this in search of the best option.

The VMA periods were selected after preliminary tests showed that when combined with the different SD lengths they resulted in median smoothing periods between 3 and 173 days; a range that should capture the best results based on what we know from previous research into moving averages.

A total of 180 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 180 RVI-VMA Long and Short Test Results

RVI Variable Moving Average EOD Returns, Long:

. RVI-VMA Annualized Return - Long, WS Period Comparison

As with our previous VMA tests, every single RVI-VMA using EOD signals outperformed the average buy and hold annualized return of 6.32%^ during the test period (before allowing for transaction costs and slippage).

The charts above are split into three sets according to their WS period. Each set reveals very similar results but, low and behold the standard setting of 14 proved the best by a small margin.

To our surprise the Standard Deviation period didn’t really matter and despite testing a huge range from 10 days to 252 days, all the results were very similar. So we decided to select 126 days as the best SD period becuase it has been the best Volatility Index setting in several previous VMA tests.

For the VMA constant, a period of 10 stood out as producing the best results across the board. Therefore we want a RVI-VMA within a SD period of 126, a WS period of 14 and a VMA constant of 10:

Best EOD Relative Volatility Index Variable Moving Average:

126, 14 Day RVI-VMA, EOD 10, Long .

I have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126, 14 Day RVI-VMA, EOD 10, Long can’t compare in terms of performance with the FRAMA and offers no outstanding attributes in any other areas.

126, 14 Day RVI-VMA, EOD 10 – Smoothing Period Distribution:

126 Day RVI-VMA, EOD 10 – Smoothing Period Distribution .

The RVI-VMA is very localized around its median smoothing period of 20. Almost the entire distribution (96%) is covered with a 12 – 31 range which only represents 28% of the smoothing for the better performing FRAMA.

126, 14 Day RVI-VMA, 10 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126, 14 Day RVI-VMA, 10 and compared it to the best performing FRAMA to see if there were any similarities that would reveal what makes a good volatility index:

126 Day ER-VMA, 1 – Alpha Comparison .

As you can see the Alpha for the 126, 14 Day RVI-VMA, 10 is very volatile but stays within a tight range. The better performing 126 Day FRAMA 4, 300 on the other hand produces readings that are much more stable however they do move to extremes upon occasion resulting in a more ‘Variable’ Moving Average.

Conclusion

The RVI-VMA outperformed a buy and hold approach in our tests but is nowhere neat as effective as the FRAMA and therefore is not worthy of being used as a trading tool.

Want to have a play with this indicator anyway? Get a free Excel spreadsheet at the flowing link under Downloads – Technical Indicators: Variable Moving Average (VMA). It will automatically adjust to one of many different VIs that you can select including the Relative Volatility Index featured in this article.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long for each average tested was generated with a close above that average and an exit signal was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) signals on Daily data. Eg. Daily data with EOD signals requires the Daily price to close above a Daily Moving Average to open a long and vice versa.
^ This was the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

Vertical Horizontal Filter Variable MA (VHF-VMA) – Test Results

The VHF-VMA requires two user selected inputs: A Vertical Horizontal Filter Period and a VMA period. We tested trades going Long and Short using Daily data taking End Of Day (EOD) signals~ analyzing all combinations of:

VHF = 10, 20, 40, 80, 126, 252

VMA = 1 – 20

The VHF lengths were selected due to the fact that they correspond with the approximate number of trading days in standard calendar periods: 10 days = 2 weeks, 20 days = 1 month, 40 days = 2 months, 80 days = ⅓ year, 126 days = ½ year and there are 252 trading days in an average year.

The VMA periods were selected after preliminary tests showed that when combined with the different VHF lengths they resulted in median smoothing periods between 3 and 173 days; a range that should capture the best results based on what we know from previous research into moving averages.

A total of 160 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 160 VHF-VMA Long and Short Test Results

VHF Variable Moving Average EOD Returns, Long:

. VHF-VMA Annualized Return EOD, Long

As with previous VMA test, every single VHF-VMA using EOD signals managed to outperform the average buy and hold annualized return of 6.32%^ during the test period (before allowing for transaction costs and slippage).

The VHF periods of 126, 252 and 80 produced the best results when the VMA period was 10 or less while the highest returns came from a VHF period of 126 and a VMA period of 2. We have seen in every ‘intelegent’ moving average test so far the predominance of 126 and 252 as the volatility or trend strength indicator settings that produce the best returns.

Best EOD Vertical Horizontal Filter Variable Moving Average:

126 Day VHF-VMA EOD, 2 Long .

I have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126 Day VHF-VMA, EOD 2, Long produced respectable results compared to the best that the FRAMA could produce but still under performed slightly. Plus there are several other things that go against the 126 Day VHF-VMA, EOD 2 such as a lower return on the NASDAQ, not turning a profit on the Nikkei 225 and a lower average trade duration. (It also under performed on the short side by a small margin).

126 Day VHF-VMA, EOD 2 – Smoothing Period Distribution:

126 Day VHF-VMA, 2 - Smoothing Period Distribution .

Looking at the smoothing distribution you can see that the range for the VHF-VMA of just 5 – 43 is much smaller than the FRAMA. In fact the entire VHF-VMA range covers only 68% of the FRAMA smoothing periods. The median for the VHF-VMA is also lower which explains why it produces a shorter average trade duration.

126 Day VHF-VMA, 2 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day VHF-VMA, 2 and compared it to the best performing FRAMA to see if there were any similarities that would reveal what makes a good volatility index:

126 Day VHF-VMA, 2 – Alpha Comparison

The alpha pattern is not dissimilar for the 126 Day FRAMA 4, 300 and the 126 Day VHF-VMA 2 which explains why they produce comparable results. The VHF-VMA however tends to produce higher readings resulting in a faster average and rarely moves to extremes. While the lack of volatility from the VHF-VMA reading is a positive, it provides little variation in the smoothing period as the market changes.

Conclusion

The VHF-VMA does produce good returns and helps to further prove the validity of Variable Moving Averages in general. However we found its performance to be slightly lower than the 126 Day FRAMA, EOD 4, 300 in almost every respect and therefore the Vertical Horizontal Filter Variable Moving Average does not warrant use as a trading tool.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) signals on Daily data. Eg. Daily data with EOD signals requires the Daily price to close above a Daily Moving Average to open a long or close a short and vice versa.
^ This was the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

Efficiency Ratio Variable Moving Average (ER-VMA) – Test Results

The Variable Moving Average (VMA) dynamically adjusts its own smoothing period to the changing market conditions based on a Volatility Index (VI). While any VI can be used, in this article we will look at how the VMA performs using an Efficiency Ratio (ER). This is identical to the modified CMO that Tushar S. Chande suggested be used in his October 1995 article in Technical Analysis of Stocks & Commodities – ‘Identifying Powerful Breakouts Early‘.

The ER-VMA requires two user selected inputs: An Efficiency Ratio Period and a VMA period. We tested trades going Long and Short, using Daily data, taking End Of Day (EOD) and End Of Week (EOW) signals~ analyzing all combinations of:

ER = 10, 20, 40, 80, 126, 252

VMA = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

The VMA periods were selected after preliminary tests showed that when combined with the different ER lengths they resulted in median smoothing periods between 6 and 207 days; a range that should capture the best results based on what we know from previous research into moving averages.

A total of 240 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 240 ER-VMA Long and Short Test Results

ER Variable Moving Average EOD vs EOW Returns:

. Efficiency Ratio Variable Moving Average - Average Annualized Return, Long

As with previous VMA test, every single ER-VMA using EOD signals managed to outperform the average buy and hold annualized return of 6.32%^ during the test period (before allowing for transaction costs and slippage).

Clearly the ER periods of 126 and 252 produced the best results using both EOD and EOW signals. This echoes previous results on other ‘intelligent’ moving averages. The 126 Day ER-VMA with a constant of 1 stands out as the best performer with EOD signals while the 252 Day ER-VMA with a constant of 9 was the best when taking EOW signals. (The results on the short side reiterate this).

It is interesting to note that the returns hold up quite well when using EOW signals on a 252 ER so lets take a closer look at the how the probability of profit and trade duration compares for EOD and EOW signals:

Efficiency Ratio VMA - Probability of Profit and Average Trade Duration, Long

Best EOD Efficiency Ratio Variable Moving Average

126 Day ER-VMA EOD, 1 Long .

I have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average. The 126 Day ER-VMA, EOD 1, Long produced almost identical results to the best that the FRAMA could produce but still under performs slightly (The same is true on the short side). Plus there are other little things that go against the 126 Day ER-VMA, EOD 1 like a slight increase in the biggest loss and not turning a profit on the Nikkei 225.

126 Day ER-VMA, EOD 1 – Smoothing Period Distribution

Looking at the smoothing distribution you can see it is quite similar to the FRAMA but with a lower median and a MASSIVE range.

126 Day ER-VMA, 1 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126 Day ER-VMA, 1 and compared it to the best performing FRAMA to see if there were any similarities that would reveal what makes a good volatility index:

126 Day ER-VMA, 1 - Alpha Comparison

The alpha does have a very similar pattern for both the 126 Day FRAMA 4, 300 and the 126 Day ER-VMA 1 and this further helps to explain why their performance is so similar. Notice however that the FRAMA is far less volatile. It is always preferable to work with indicators that generate clean readings with low levels of noise assuming they still produce good results.

Best EOW Efficiency Ratio Variable Moving Average

252 Day ER-VMA, EOW 9, Long

I have included on the above chart the performance of the 252 Day FRAMA, EOW 40, 250 Long becuase so far this has been the best performing ‘slower’ Moving Average. The 252 Day ER-VMA, EOW 9, under performs by a small amount by almost every measure but it does offer a longer average trade duration of 86 days compared to 63 days for the FRAMA. This makes the 252 Day ER-VMA, EOW 9 a very strong candidate as the best ‘slower’ moving average although its performance on the short side under performs by a slightly greater margin.

252 Day ER-VMA, EOW 9 – Smoothing Period Distribution

252 Day ER-VMA, 9 - Smoothing Period Distribution .

Looking at the smoothing distribution for the 252 Day ER-VMA, 9 you can see that it is far more spread out with just 33% of its the periods covered in the first 50 data points while the same range covers 82% for the FRAMA. It also has a much higher median smoothing period of 119 compared to 52 for the FRAMA which explains why it has a longer average trade duration.

252 Day ER-VMA, 9 – Alpha Comparison

252 Day ER-VMA, 9 - Alpha Comparison .

This time we see that the alphas are very different but once again the FRAMA is far less volatile. Remember the higher the reading the faster the resulting smoothing period; the ER-VMA stays much lower than the FRAMA which results in a slower average.

Conclusion

The ER-VMA produces some impressive returns and gives the FRAMA a good run for its money. For a ‘fast’ moving average the 126 Day FRAMA, EOD 4, 300 is definitely superior to the 126 Day ER-VMA, 1 because it outperforms by almost every measure and is guided by readings (D) that are far less volatile.

For the ‘slower’ moving average it is more difficult to select the winner. I like the fact that the 252 ER-VMA, 9 has a much more even distribution of smoothing and a longer average trade duration. However it is unfortunate that there is so much more noise in the readings (ER) that guide it. The ER-VMA certainly warrens mention and perhaps further research but based on our findings so far the FRAMA remains slightly superior in almost every way.

Want to use this indicator? Get a free Excel spreadsheet at the flowing link under Downloads – Technical Indicators: Variable Moving Average (VMA). It will automatically adjust to one of many different VIs that you can select including the Efficiency Ratio used in this article.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) and End Of Week (EOW) signals on Daily data. Eg. Daily data with an EOW signal would require the Week to finish above a Daily Moving Average to open a long or close a short while Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long or close a short and vice versa.
^ This was the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.

252 Day ER-AMA, 9 – Alpha Comparison

Standard Deviation Ratio Variable Moving Ave (SDR-VMA) – Test Results

The Variable Moving Average (VMA) dynamically adjusts its own smoothing period to the changing market conditions based on a Volatility Index (VI). While any VI can be used, in this article we will look at how the VMA performs using a Standard Deviation Ratio (SDR). This is the VI that Tushar S. Chande first suggested be used when he presented what he called a Volatility Index Dynamic Average (VIDYA) in the March 1992 edition of Technical Analysis of Stocks & Commodities – Adapting Moving Averages To Market Volatility.

The SDR-VMA requires three user selected inputs: A Short Standard Deviation (SD1), a Longer Standard Deviation (SD2) and a VMA period. We tested trades going Long and Short, using Daily data, taking End Of Day (EOD) and End Of Week (EOW) signals~ analyzing all combinations of:

SD1 = 10, 20, 40, 80, 126

SD2 = 20, 40, 80, 126, 252

VMA = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50

The VMA periods were selected after preliminary tests showed that when combined with the different SDR combinations, these settings resulted in a median smoothing period between 6 and 280 days; a range that should capture the best results based on what we know from previous research into moving averages.

A total of 390 different averages were tested and each one was run through 300 years of data across 16 different global indexes (details here).

Download A FREE Spreadsheet With Raw Data For

All 390 SDR-VMA Long and Short Test Results

SDR Variable Moving Average Test Results, Daily EOD, Long:

The data collected from our tests has been split by SD1 length with return plotted on the “y” axis, the VMA constant on the “x” axis and a separate series displayed for each SD2 length.

VIDYA Annualized Return .

First up it must be noted that every single SDR-VMA Long using EOD signals on Daily data outperformed the average buy and hold annualized return of 6.32%^ during the test period (before allowing for transaction costs and slippage). This is a vote of confidence for the concept especially seeing as each average was typically sitting in cash 37% of the time.

Perhaps the most interesting information from the data however is the fact that the best performer from each set had a SD2 that was twice the length of SD1. This formula of SD2 = 2*SD1 should therefore be used whenever utilizing the Standard Deviation Ratio.

The Best SDR-VMA Parameters

The best performing average was found where SD1 = 126, SD2 = 252 and the VMA constant = 5. In the FRAMA tests we also saw that the periods of 126 (half a year) and 252 (a full trading year) produced the best results so this appears to be a reoccurring theme:

126, 252 Day SDR-VMA, EOD 5, Long .

I have included on the above chart the performance of the 126 Day FRAMA, EOD 4, 300 Long becuase so far this has been the best performing Moving Average and as you can see the SDR-VMA under performs. To make matters worse it has an typical trade duration of just 9 days compared to the FRAMA’s 14, and underperformed the buy and hold returns of both the Nikkei 225 and the NASDAQ. Therefore we can conclude that the SDR-VMA, despite being effective is not as good as the FRAMA.

A look at the Smoothing Period:

126, 252 Day SDR-VMA, EOD 5 - Smoothing Period Distribution .

By looking at the smoothing distribution you can see why the SDR-VMA is so much faster than the FRAMA. While the FRAMA has a range of 293 days and a median of 21, the SDR-VMA has a range of just 37 days and a median of 8.

126, 252 Day SDR-VMA, 5 – Alpha Comparison

To get an idea of the readings that created these results we charted a section of the alpha for the 126, 252 Day SDR-VMA, 5 and compared it to the best performing FRAMA to see if there were any similarities that would reveal what makes a good volatility index:

126, 252 Day SDR-VMA, 5 - Alpha Comparison .

The alpha patterns are similar for both the 126 Day FRAMA 4, 300 and the 126, 252 Day SDR-VMA 5 but the readings are still very different. The SDR-VMA’s indicator is nearly always higher than the FRAMA’s which is why the resulting VMA is much faster.

It is desirable to see however that the SDR-VMA’s alpha is so clean and noise free in its movements. This leads me to believe that the 126, 252 SDR would be a good VI if it were adjusted to produce a slower average. Also due to the lack of noise from the SDR it may offer value in other applications such a way of ranking a universe of stocks by their trend strength, but that is the topic of another set of tests.

For more in this series see – Technical Indicator Fight for Supremacy

~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) and End Of Week (EOW) signals on Daily data. Eg. Daily data with an EOW signal would require the Week to finish above a Daily Moving Average to open a long or close a short while Daily data with EOD signals would require the Daily price to close above a Daily Moving Average to open a long or close a short and vice versa.
^ This was the average annualized return of the 16 markets during the testing period. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.