General possible indicator values -scientific research-

I'm reading indicator results all over the net (without fundamental analysis) and i'll post most of them.
There is no perfect way to test an indicator or system using historical data because past performance is no guarantee of future results.
However the markets are driven by human emotion and crowd psychology.

TEST PERIODS


Full Article Golden Cross - Which is the best? | ETF HQ

Exponential Moving Average (EMA)
Conclusion
Moving average crossovers have proven themselves to be a powerful and effective form of technical analysis, however the so called “Golden Cross” of the 50 and 200 day SMA is far from the best. Our testing revealed that the EMA produces better results than the SMA and the best settings are that of a 13 / 48.5 EMA Crossover. The long duration of the trades produced, ability to sidestep bear markets and the high probability of profit make it worth testing as a major component in a complete trading system.

Our Testing Strategy Explained There are endless combinations of moving averages that we could test in search of the best. To cast our testing range wide but intelligently we have used progressions of a ratio; slow/fast MA: Fast Moving Averages (FC) = 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 Slow Moving Averages (SC) = 1.2 * FC, 1.4 * FC, 1.6 * FC, …….. 5.6 * FC, 5.8 * FC, 6 * FC So each of the ten FC settings were tested against twenty five SC settings based on a multiple of the FC. e.g The traditional Golden Cross with a SC of 50 and a FC of 200 has a multiple of 4 (because 50 * 4 = 200). The tests against a FC of 50 had a multiple as low as 1.2… (50 * 1.2 = 60) and as high as 6… (50 * 6 = 300).





The best returns came from an fast EMA of 10 days with a slow EMA of 50 (ratio of 5 because 10 * 5 = 50). Based on these results we will run more refined tests on fast moving averages in the range of 8 – 17 and slow moving averages 20 – 56.



There is a zone of dark green on the grid above but the very best from our tests, the True Golden Cross has a slow EMA of 48.5 and a fast EMA of 13. Reality is very different from the 50/200 SMA Golden Cross that someone made up once upon a time and that is why we must test everything.

Thanks for this review.

As far as I know - there is some insinuations between moving averages guys/indicators and ishimoku trading guys/indicators ...

Full Article MACD - Test Results | ETF HQ

Moving Average Convergence/Divergence (MACD)
Conclusion
As a tool for long term trading the MACD fails and can’t compete with its less evolved relative the Moving Average Crossover. As a tool for short term trading (4 days on average) the MACD is very powerful in theory but with such a small average return the practical applications are limited. As a tool for medium term trading the MACD should not be your first choice on the Long side of the market BUT on the Short side the MACD is simply outstanding! Using a ‘Fast’ Moving Average of 16, a ‘Slow’ Moving Average of 97 and a signal line of 2 you have a powerful indicator for taming the bear.
In an attempt to limit the length of this article we only published results from trades off the Signal Line when the MACD line was above zero (when Long) or below zero (when short). Please note however that trying the trade the MACD when it is on the wrong side of zero will lead to an unhappy bank account, an unhappy wife and an unhappy life.

Our Testing Strategy Explained; Because there are so many different possible settings for a MACD we started by testing a broad range with the hope this would reveal the areas to focus on more closely. To cast our testing range wide but strategically, we progressed in a liner fashion through the Fast Moving Averages (FC) and set the Slow Moving Averages (SC) as of multiple of the FC:
Fast Moving Averages (FC) = 10, 20, 30, 40, 50
Slow Moving Averages (SC) = 2 * FC, 3 * FC, 4 * FC, 5 * FC, 6 * FC
So each of the five FC settings were tested against five SC settings based on a multiple of the FC. e.g A SC of 50 would be tested against a FC of 100, 150, 200, 250, 300 as these are equal to 50 multiplied by 2, 3, 4, 5 and 6.
Each of these were tested against 10 different Signal Line settings:
Signal Line (SL) = 2, 4, 6, 8, 10, 12, 14, 16, 18, 20



MACD going Long with a ‘Fast’ Moving Average of 21, a ‘Slow’ Moving Average of 81 and a signal line of 2 compared to the best FRAMA (also notice the poor performance from the standard MACD of 12, 26, 9)



After refining the tests down several times we uncovered some interesting findings. Firstly the most efficient returns came from a MACD with a ‘Fast’ Moving Average of 1, which isn’t actually a MACD at all (a Moving Average with a period of 1 is equal to the price itself). So the best results come from measuring the Convergence and Divergence between an MA and the price, with the addition of a Signal Line. What is really exciting however is this also works exceptionally well on the Short side of the market:



The Short side of the market is often not worth trading because decent returns during exposure are difficult to get from a mechanical system. What we see with the MACD however are returns during exposure then exceed even the best that the FRAMA could produce when Long.

Full Article Weighted Moving Averages Put To The Test | ETF HQ

Weighted Moving Average (WMA)
Conclusion
The Triangular and Sine Weighted Moving Averages proved to be inferior to the W-MA while the standard Weighted Moving Average did produce reasonable returns. Those returns however, were similar (if slightly inferior) to those of an Exponential Moving Average while not offering any notable benefits. Therefore it can be concluded that none of the Weighted Moving Averages we tested are worth perusing further.

Our Testing Strategy Explained; A Weighted Moving Average smooths data by setting a separate but specific weighting for each data set over the length of its smoothing period. In this round of testing we will look at the standard Weighted Moving Average (W-MA), the Triangular Weighted Moving Average (TriW-MA) and the Sine Weighted Moving Average (SW-MA) in order to reveal which is the best and if any of them are worth including in your trading tool box.
To evaluate these averages we tested Long and Short trades using Daily and Weekly data, taking End Of Day (EOD) and End Of Week (EOW) signals with Moving Average lengths varying from from 5 – 300 days or 60 weeks.~ These tests were carried out over a total of 300 years of data across 16 different global indexes.



Above you can see how the 90 Day W-MA, EOW Long performed during the test period compared to the 75 Day EMA, EOW Long which was selected as the most effective Exponential Moving Average in a previous test. The Weighted MA produced very similar results to the EMA but didn’t offer any benefits.



Above you can see how the annualized return changes with the length of each Daily, EOD Moving Average for the Long and the Short side of the market. The relative performance of each MA was similar when going Long or Short but the returns on the Short side were much lower.
There is little difference in performance between the TriW-MA and the SW-MA while the W-MA was clearly superior. The W-MA performed particularly well with a setting of 35 days or 110 days, peaking with a annualized return of over 10% on these settings. As the smoothing period is extended beyond 110 days the returns gradually diminished.

Full Article Stochastic Oscillator (SO) - Test Results | ETF HQ
Stochastic Oscillator (SO)
Conclusion
The fact illustrated by these tests is that the majority of gains occur when the market is in the top 10% of its range and nearly all of the gains occur when the market is within the top half of its range.
There has been a lot or research published on momentum strategies and they typically involve buying the best performing assets out of a selection and then rotating funds periodically so as to constantly stay with the best. Many people fear holding markets that are near their highs so by rotating constantly into the current market leaders these fears are be alleviated . What our tests on the Stochastic Oscillator reveal however is that simply holding an index fund when it is in the top half of its range (over almost any look back period) will capture the majority of the gains while STILL avoiding those much feared ‘bubble burst’ like declines. Contrary to popular belief; when a market bubble bursts it does not do so over night. Penny stocks my grow exponentially and then plummet the next day. On rare occasions large companies may even do so. But major economies can not turn on a dime.

The Stochastic Oscillator %K line is too volatile and is not worth considering in your trading as originally suggested by Dr. George Lane in the 50′s. There are better options for short term trading such as the FRAMA. In fact, there are also better options available for longer-term indications of market direction than the Stochastic Oscillator as presented in this article… So is it worth bothering with at all?
Therefore the Stochastic Oscillator could be a useful addition to a momentum rotation type strategy. Another idea worth considering is to change the rules for your trading system based to the Stochastic Oscillator reading. For instance we know that most gains occur when the market is making new highs, therefore the rules for taking profits on a long position should be different when the Stochastic Oscillator is above 90 than they are when it is between 50 and 60



Highlighted each of the negative results across a Red—>Orange gradient and positive results across a Light Green—>Dark Green gradient (depending on how great the loss or gain). Clearly most of the market gains occurred while the Stochastic Oscillator was above 50 and the lion’s share when it was above 90. What this means is that when the market is in the top 50% of its range it has a tendency to go up and when it is in the top 90% of its range it has a strong tendency to go up. It also tells us that we want to avoid being long when the market is in the bottom 50% of its range. Over what period do we base this range? Interestingly, the returns do not change much over the different look back periods although the benefit of a longer look back is less volatility from the signals.



The table above is colour coded Red—>Yellow—>Green from Lowest—>Middle—>Highest return. The message coming through loud and clear is that you need to be long when the market is making new highs if you want to make money. Over a 255 day look back (about 1 year) the difference between going long in the 50-90 range vs the 50-100 range is the difference between making 2.68% or 8.38% a year!! –



The returns are not as good as we have seen from other indicators such as the RSI or the Moving Average Crossover but they are still respectable. Furthermore an average trade duration of 104 days is advantageous when looking for a long term indication of market direction.
What about the %K signal line? We did test this but the results were not worth taking the time to publish.

Full Article Relative Strength Index (RSI) - Test Results | ETF HQ
Relative Strength Index (RSI)
Conclusion
Never before have I seen such a dichotomy of profitable and unprofitable trades when an indicator is above or below a level as is the case with the RSI being above or below 50. This proves that momentum is a strong and valuable predictor of market direction and the theory behind the RSI is sound. For this reason it would be worth testing your system with entry signals confirmed by the RSI(55) being on the appropriate side of 50. (Remember to use the conversion table; our RSI(55) will be an RSI(28) in your charting program.)
While the RSI clearly provides valuable information, unfortunately we are yet to identify a method of use that presents a more desirable trade profile than the simple effectiveness of the MA Crossover or the FRAMA.
We also tried using an EMA signal line on the RSI but the results where not worth writing about However I feel that there will be other worthwhile ways to test the RSI. Perhaps it could be used as a breadth indicator where the number of higher highs from the RSI is compared to the number of higher highs from the stocks within an ETF?

Our Testing Strategy Explained;we use an EMA when calculating the RSI instead of a WS-MA. This is not just to be difficult, please read more about the RSI for an explanation. The formula to convert the EMA Look Back period to the identical equivalent WS-MA used by your charting programs when calculating the RSI is (Period + 1)/2. Below is a table with all the Look Back Periods we tested and how they convert to the original RSI



Now there are many different ways that signals can be taken from the RSI but to start with we wanted to see how the market behaved when the RSI was in different ‘zones’. We also wanted to find out which RSI Look Back period is the most desirable. But this presents a problem because changing the Look Back period alters the range of an RSI. We tested all combinations of increment ranges:
Range of 1 = -5 to -4, -4 to -3 … 3 to 4, 4 to 5
Range of 2 = -5 to -3, -4 to -2 … 2 to 4, 3 to 5
Range of 3 = -5 to -2, -4 to -1 … 1 to 4, 2 to 5
Range of 4 = -5 to -1, -4 to -0 … 1 to 4, 2 to 5
Range of 5 = -5 to 0, -4 to 1 … -1 to 4, 0 to 5



You must admit, once you crank up the leverage and remove the bear markets by confirming the signals with the 13 / 48 MA Crossover; this is an impressive looking equity curve. The best part is that you are only exposed to the market 7% of the time! Realistic in the real market? Questionable…
Perhaps with the use of futures this could be a workable strategy. It was profitable on 15/16 global indices we tested and showed a 54% probability of profit through 3837 trades (a nice large sample).



What if we only opened a Long position when the RSI was rising? In these tests a position was only initiated when the RSI went from being below 50 (the 0 increment) to above 50.
By introducing entry criteria to the RSI trades the market exposure decreased and with it the returns in most areas .One area that does stands out however; the Annualized Return During Exposure when the RSI(5) moves through the 0 – 1 increment.

Bull / Bear Dichotomy
Full Article Bull / Bear Dichotomy Indicator v 1.0 (BBD) | ETF HQ
Conclusion
The EMA crossover shows us that there is value in measuring shorter term momentum vs longer term momentum. The RSI shows us that there is value in a measure of declines vs advances. While the Stochastic Oscillator shows us that there is value in being long a market when it is in the top half of its range. The trade profile for each indicator is desirable but their signals are often in conflict.
Bull / Bear Dichotomy is an equally weighted average of the RSI (126), SO(225) and MA Cross (13-48), smoothed with a EMA(10).

EMA(13) > EMA(48) = Bullish
EMA(13) < EMA(48) = Bearish

RSI(126) > 50 = Bullish
RSI(126) < 50 =Bearish

SO(252) > 50 = Bullish
SO(252) < 50 = Bearish



Above you can see the readings from each indicator during a randomly selected 2.5 year period on the Australian All Ordinaries Index. The thick red line is an equally weighted average of the RSI, SO and MA Cross, smoothed with a EMA(10). This we are calling the Bull / Bear Dichotomy or BBD Indicator v1. By combining all three indicators, a greater level of stability and robustness is achieved. See below the full trade profile:



In looking at the trade profile the signal stability is clear with an average trade duration of 170 days, an average profit of 21.81% and a probability of profit sitting at 48%! The other statistics are not dissimilar to the component indicators. The only thing missing from the BBD is a measure of volüme.

Full Article http://etfhq.com/blog/2013/01/11/log...oving-average/

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Log-Normal Adaptive Moving Average (LAMA)


The Log-Normal Moving Average (LAMA) is the name I have given to an Adaptive Moving Average that uses the adaptive process developed by John F Ehlers for use in his FRAMA. Stock prices are said to be Log-Normal so Ehlers used EXP to relate his Volatility Index (The Fractal Dimension) to Alpha. The LAMA is designed so that any VI can easily be incorporated as long as it shifts between a range of 1 – 0 where high readings indicate high volatility.
How to Calculate an Log-Normal Moving Average

Seed it with the Close price then after that the LAMA is calculated according to the following formula:

LAMA = LAMA(1) + New α * (Close – LAMA(1))

Where:

New α = 2 / (New N + 1)

New N = ((SC – FC) * ((N – 1) / (SC – 1))) + FC

SC = Your choice of a Slow moving average > FC

FC = Your choice of a Fast moving average < SC

N = (2 – α) / α

α = EXP(W * (1 – VI))

W = LN(2 / (SC + 1))


How EXP affects Alpha and the Smoothing Period:

Ehlers used EXP to relate the Volatility Index (VI) to Alpha (α) so lets have a look at what affect this has:



In the top chart you can see Alpha taken directly from the the Fractal Dimension and also taken after it has been modified by applying EXP. In the bottom chart you can see the smoothing period that results from each version of Alpha. Clearly by applying EXP, Alpha is reduced creating an significantly faster Smoothing Period.

The use of EXP results in not just a slower LAMA overall but one that exponentially slows as alpha decreases. This affect is similar to that of raising Alpha to a power as seen in the Adaptive Moving Average (AMA). In fact, it turns out that the LAMA is identical to the AMA if you were to raise it to the power of about 988,869,997.798!!!!!! That is not a typo. The LAMA and therefore the FRAMA is identical to the AMA raised the power of almost 100 million….

In discovering this there is little point in running the tests on this indicator because previous tests on the AMA already reveal it will not be able to out perform. Oh well, that saves some work! That is why we take the time to look closer at these things and try not to make too many uneducated assumptions.

As this sample,
i trade;
1318164 2014.01.03 21:02 sell 1.00 usdtry
1318165 2014.01.03 21:03 buy 1.00 usdtry
At the same time buy and sell.
It is interesting.
I know that price will be circle at 2.18xx and 2.16xx. So order buy and sell.
As a result all open positions win.
Just think about it.


DetailedStatement.htm

Moving Average Convergence Divergence (MACD)

MACD stands for Moving Average Convergence Divergence and was first developed by Gerald Appel in the late 1970s. It is an Absolute Price Oscillator (APO) and can be used in an attempt to identify changes in market direction, strength and momentum.

It calculates the convergence and divergence between a ‘fast’ and a ‘slow’ Exponential Moving Average (EMA) known as the MACD Line. A signal EMA is then plotted over the MACD Line to show buy/sell opportunities. Appel specified the MA lengths as the following percentages:

• Slow EMA = 7.5% (25.67 period EMA)
• Fast EMA = 15% (12.33 period EMA)
• Signal EMA = 20% (9 period EMA)

Usually however these are rounded to EMAs of 26, 12 and 9 respectively. Many charting packages will also plot the difference between the Signal Line and MACD Line as a Histogram.

One of the biggest challenges when dealing with financial data is noise or erratic movements that cause false signals. By smoothing data out you can reduce the number of false signals. But this comes at a cost, and causes an increase in the lag of your signals. The genius of the MACD is that it begins by smoothing data (thus causing lag) and then speeds up the signals from the smoothed data. This combination helps to reduce false signals while minimising the lag.

By comparing EMAs of different lengths the MACD can help to identify subtle changes in the trend and momentum of a security. It is a great visual representation of the acceleration or rate of change in a trend.

How to Calculate a MACD

MACD Formula:

• MACD Line = EMA,12 – EMA,26
• Signal Line = EMA[MACD,9]
• MACD Histogram = MACD – Signal Line
• Histogram Trigger = EMA[MACD Histo,5]

Obviously you can change the parameters to any value of your choice.

MACD Excel File

We have put together an Excel Spreadsheet that will automatically adjust to the MACD settings you desire. Find it at the following link near the bottom of the page under Downloads – Technical Indicators: Moving Average Convergence Divergence (MACD)

Test Results

Is the MACD an effective indicator? We are putting it into the ring for the Technical Indicator Fight for Supremacy. It will be tested through 300 years of data across 16 global markets to discover which settings produce the best results and how it performs compared to other indicators:

1. Moving Average Crossovers – Completed - Golden Cross – Which is the best?
2. Moving Average Convergence Divergence (MACD) – Completed – Results
3. ZeroLag MACD (ZL-MACD)
4. MACD Z Score (MAC-Z)