Technical Analysis

Stochastic Oscillator (SO) – Test Results

The Stochastic Oscillator (SO) is a widely used momentum indicator. As part of the Technical Indicator Fight for Supremacy we have put it to the test through 16 different global markets~ (a total of 300 years data) to find out how well it works and what settings produce the best returns.

Download A FREE Spreadsheet With Data, Charts And Results

For all 1,248 Stochastic Oscillator Settings Tested

First of all lets establish how the market performs while the Stochastic Oscillator is in each 10th of its range:

Stochastic Oscillator Range 10

I have 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.

Lets now look at segments of 20-50% when the Stochastic Oscillator is above 50:

Stochastic Oscillator Range Above 50

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!!

Lets have a look at the trade profile:

252 Day Stochastic Oscillator EOD, 50-100 Range, Long, Any

The results above show what would have happened if a long position was opened and held any time, any of the 16 test markets had a reading above 50 on their Stochastic Oscillator (meaning that the market was in the top half of its range). We chose a period of 252 days, not because the returns were the best, but because this look back produced a longer average trade duration and 252 days is the average number of trading days in 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. They are included in the results spreadsheet for free download if you wish to review them however.

Stochastic Oscillator Conclusion

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?

Well… YES and here is why:

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.

“In the event of nuclear war, disregard this message.” – Warren Buffett

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.

Both of these applications will be included in future tests.

More in this series:

We have conducted and continue to conduct extensive tests on a variety of technical indicators. See how they perform and which reveal themselves as the best in the Technical Indicator Fight for Supremacy.

~The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.
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.

Stochastic Oscillator (SO)

The Stochastic Oscillator (SO) (pronounced sto kas’tik) is a momentum indicator that was developed in the 1950’s by a group of futures traders in Chicago. Primarily attributed to Dr. George Lane (1921 – July 7, 2004) it is sometimes referred to as ‘Lane’s stochastics’. The term stochastic refers to the location of the current price in percentage terms relative to it’s range over a specific period.

“Stochastics measures the momentum of price. If you visualize a rocket going up in the air; before it can turn down, it must slow down. Momentum always changes direction before price.” – Dr. George Lane

Interpreting the Stochastic Oscillator

Because of Lane’s belief that momentum changes direction before price, he looked for bullish and bearish divergences as a warning of pending reversals.

Another method is to only take positions when the Stochastic Oscillator is within a specific range. e.g Only going long when the SO is above 80 (meaning a stock is with within the top 80% of its range over the specified period).

Active traders may choose to trade the SO directly from its signal line. e.g go long when the %K line rises above the %D line.

Below is a Slow Stochastic Oscillator with the most commonly used settings of N(14), %K(3) and %D(3):

Stochastic Oscillator

How to Calculate the Stochastic Oscillator

%K = 100 * ( Average(CL,s) / Average(HL,s) )

%D = User selected moving average of %K.

Were:

s = User Selected smoothing period.

CL = Close – Low(n)

HL = High(n) – Low(n)

n = User selected look back period for measuring the price percentage range.

Notes:

An ‘s’ of 1 will produce a ‘Fast Stochastic’ while a setting of 3 is typicality used for a ‘Slow Stochastic’. Interestingly the Williams %R is identical to the %K but mirrored at the 0% line.

Free Stochastic Oscillator Excel Download

We have built a free Excel Spreadsheet for you to download containing an SO that will automatically adjust to the settings you choose. You will find it at the following link under Technical Indicators.

Is the Stochastic Oscillator a good indicator?

As part of the Technical Indicator Fight for Supremacy we putting it to the test through 300 years of data across 16 different global markets – See the results.

Relative Strength Index (RSI) – Test Results

The RSI is a staple indicator of the technical analysis community but how good is it, really? What are the best settings? What does its trade profile look like? Ask around and no one can tell you… Does it not seem strange that so many traders can be using an indicator without solid data on its performance? Well we are on a mission to change that. We tested 3800 different RSI settings through 300 years of data across 16 different global markets~ to reveal the facts.

Download A FREE Spreadsheet With Data, Charts

And Results For all 3,800 RSIs Tested

RSI Conclusion

Our Testing Strategy Explained

noʊtɑ bɛnɛ (Note Well) 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:

RSI Period Conversions

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.

For instance during our tests across 16 different markets and 300 years of data the range for the RSI(5) was 89.96 – 10.04 while for the RSI(100) it was 70.77 – 29.23. Clearly a direct level comparison between two RSIs of different look back periods is not suitable.

To overcome this challenge we identified the range for each RSI across all different look back periods tested. Then divided each range by 10 and advanced from the mid line (50) in 1/10th increments specific for each different RSI. The normalisations were numbered based on how many 1/10th of their range they were from 50 (with the exception of the final increment at each end which was extended to 100 or 0 respectively).

Here is a table of the normalisations used which will hopefully clarify:

RSI Normalisations

For instance, lets say we wanted to see how the market performed within a 0 – 3 Normalised RSI range on a RSI(15) vs. an RSI(55). Using the table above as a guide we would test the RSI(15) from 50 – 78.30 and the RSI(55) from 50 – 70.91; in doing so we should be comparing apples with apples.

Next it was necessary to exclude some data because it was taken from a sample too small to be conclusive. Lets say you were to buy every time that an RSI(35) was in the -4 to -5 RSI range, in our tests your annualized return during exposure was 248377165801.21%… sounds great right? Yes and no; the average trade did return 1.16% per day… but the average trade duration was only 1 day and you would have only been exposed to the market 3 days a year. Statistics like this are invalid so we excluded anything that didn’t result in market exposure of at least 6%.

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

The key findings are published below, to see all the results download the full results spreadsheet.

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RSI: ANY, Range = 1

First, to see how the market behaved in each increment; a long position was taken ANY time the test market was in the corresponding RSI range:

RSI, ANY, Increment Range 1, Annualized Return During Exposure, Long

Above 0 (50 on the RSI) and the returns are positive, below zero and the returns were negative, you don’t often see such a clear edge over the market as that (see the results when going Short). The blank cells, (if you were wondering) are where data was excluded because the market exposure < 6%.

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RSI: ANY > 0, Range = 1, 2, 3, 4, 5

So we now know that RSI > 50 = Good and RSI < 50 = Bad. Lets now look at how far above 50 (the 0 increment) we can go and capture the best profits. A Long position was open ANY time the test market was in the corresponding RSI range:

RSI ANY - Long, Increment Range 1, 2, 3, 4, 5 > 0

From the above table we can see that the most gains on the Long side occur when the RSI is between the 0 and 4 increment (see results going short). The Look Back period makes surprisingly little difference although around 55 days we see the most gains captured over all:

55 Day RSI EOW, 50 - 77.88 Range, Long Any

Above are the results from an RSI(55) with a open Long position any time that the RSI was in in the 50 – 77.88 range (0 – 4 increment). The positions were only opened and closed at the End Of the Week (EOW) because switching from EOD to EOW almost doubled the average trade duration and the probability of profit (see the results EOD). While the trade profile is quite good, the MA Crossover or FRAMA are still both more desirable.

Note – our RSI(55) using an EMA is equivalent to an RSI(28) in your charting programs which use Wilder’s Smoothing.

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RSI, ENTRY > 0, Range = 1, 2, 3, 4, 5

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. It was then held as long as the RSI remained in the corresponding range:

RSI ENTRY - Long, Increment Range 1, 2, 3, 4, 5 > 0

By introducing entry criteria to the RSI trades the market exposure decreased and with it the returns in most areas (see results going short). One area that does stands out however; the Annualized Return During Exposure when the RSI(5) moves through the 0 – 1 increment. Lets take a look at the trade profile:

5-day-rsi-eod-0-1-l-entry

Above are the results from an RSI(5) with a position opened Long only when the RSI raised above 50. The position was then held until the RSI moved above 55.99 or back below 50 (the 0 – 1 increment). The resulting trade profile doesn’t suit my style but I will entertain the idea because it may suit yours…

You don’t have to look far in the quant blogosphere to find examples of systems based on holding a position for only one day following an fed announcement when the VIX is above a certain level etc. Anyway, be this a practical system or not, it does have a rather smooth looking equity curve and a high probability of profit. Just for fun, lets look at what happens if we add 4X leverage and only go Long when the 13 / 48 MA Crossover is confirming the RSI signal:

13 EMA > 48 EMA + 5 Day RSI, EOD, 50 - 60 Range, Long + Entry, 4X

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 do you think?

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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 (download all the results in a spreadsheet to see for yourself.) 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?

How would you like to see the RSI tested? Ideas?

Relative Strength Index (RSI)

The Relative Strength Index (RSI) is a very popular Momentum Oscillator and was created by J. Welles Wilder, Jr. who first presented it in his landmark book New Concepts in Technical Trading Systems (June 1978).

The RSI moves within a range from 0 to 100 and typically has an upper extreme zone above 70 and a lower extreme zone below 30. When in the upper extreme zone a stock is considered overbought and when in the lower extreme zone a stock is considered oversold. At such extremes the RSI suggests that a recent stock movement is likely to slow or reverse. Welles recommended a 14 period RSI but increasing the RSI period will decrease its volatility (and vice versa) as seen in the example below where three different RSI periods are overlaid:

RSI Example

The Relative Strength Index measures declines relative to advances over a specified period. This is done by averaging out the amount that a stock advanced on the days that it moved higher and the amount that stock declined on the days it moved lower. A modified ratio of these two averages is then charted creating a visual Relative Strength Index of bulls and bears.

Wells used his own smoothing method in the RSI known as Wilder’s Smoothing (WS-MA). Despite having a unique calculation method, WS-MA is actually identical to an EMA with a period of (2 * RSI Period) – 1. So an RSI(14) actually has an EMA period of 27 = (14 * 2) -1. Why care? Because it helps to maintain constancy between methods and measures when comparing indicators as we are in the Technical Indicator Fight for Supremacy.

For instance if we were to compare the Relative Momentum Index (RMI) to the RSI it would be helpful to compare them over equivalent look back periods so any patterns become evident. For this reason we use the EMA instead of the WS-MA in the Relative Strength Index.

How to Calculate the RSI

RSI = 100 – (100 / 1 + RS)

RS = EMA of Gains / EMA of Declines

EMA = EMA(1) + α * (Current change – EMA(1))

Where:

α = 2 / (N + 1)

N = (2 * RSI Period) – 1

RSI Period = User selected value but typically 14

Note:

Declines are expressed as their absolute value (all as positive).

Each EMA can be seeded with a SMA of the relevant Gains or Losses.

Free RSI Excel Download

To make life easy we have built a free Excel Spreadsheet for you to download containing an RSI that will automatically adjust to the look back period you set. You will find it at the following link under Technical Indicators.

How to use the RSI

Overbought/Oversold: Wilder suggested the upper and lower extremes of 70 and 30 as an indication of turning points. He said that when the RSI rises above 30 this is a bullish sign, with the opposite indication when the RSI falls below 70. Some traders, after identifying the long term trend of a stock will use extreme readings from the RSI as an entry point.

Divergences: Confirmation of the strength of a medium term bullish trend can be gained by looking for higher highs from the stock confirmed by higher highs from the RSI. In a similar fashion; a stock that is declining and making lower lows while the RSI is making higher lows may become a buying opportunity.

Centreline Crossover: The centreline on an RSI is 50, above this level we know that the average gain has been larger than the average decline over the look back period. Many traders look to see the RSI above or below 50 as confirmation before opening a long or short position.

Is the RSI a good indicator?

That is a great question, at a guess I would say yes but rather than guess we tested it through 300 years of data across 16 different global markets – See the Results.

Top 7 Technical Analysts of All Time Share Their Secrets

My first brush with Technical Analysis was not a good one and I was left asking the question “Does Technical Analysis work?”. There was plenty of evidence to suggest Fundamental Analysis worked (Warren Buffett has Billions of evidence). But Fundamental Analysis really doesn’t suit my personality so what were the other options?

Does Technical Analysis Work? Everywhere you go online there is another guru selling the latest TA system accompanied with confusing looking charts. I decided that if there wasn’t a long list of very rich Technical Analysts out there then I had lost enough money using TA and was ready to quit. To my delight I discovered many successful traders and investors who had the track record to prove that Technical Analysis does work. Here is a list of the traders I found particularly noteworthy:

The Worlds Best TA Traders:

Marty Schwartz

Best Technical Analyst Marty Schwartz Originally a stock analyst but got sick of having to write bullish investment advice on overpriced companies. He developed and combined several technical indicators in an effort to determine lower risk entry points for his trades. Schwartz found success when he shifted to technical analysis and focused on mathematical probabilities.

He ran his account up from $40,000 to $20 Million and also won the U.S. Investing Championship in 1984. When asked if Technical Analysis works he replied “I used fundamentals for nine years and got rich as a technician”. A big advocate of moving averages, Schwartz identifies healthy stocks by looking for positive divergences in price action over the broad market.

They (traders) would rather lose money than admit they’re wrong… I became a winning trader when I was able to say, “To hell with my ego, making money is more important” – Marty Schwartz

Mark D. Cook

Top Technical Analyst Mark D. Cook Lost all his capital several times while learning to trade including one occasion when he lost more than his entire net worth. In 1982 he sold naked calls on Cities Service that expired deep in the money. His account dropped from $165,000 to a deficit of $350,000 in a matter of days; a total loss of $815,000 when taking into account for the money that he lost in his family’s accounts.

Not one to give up, after five years Mark had totally recovered from the losses but vowed never to sell another naked option. He attributes his turn around in success to the development of what he calls the ‘Cumulative Tick Indicator’.

There is a widely used indicator called the ‘Tick’ that measures the number of NYSE stocks whose last trade was an uptick minus the number whose last trade was a downtick. When the ‘tick’ indicator is above or below a neutral band the ‘cumulative tick indicator’ starts to add or subtract the ticks from a cumulative total. This works as an over brought and over sold indicator. When it reaches extremes of bullish or bearish readings the market tends to reverse direction.

In 1989 Cook finished second in the US Investing Championship trading stocks and in 1992 after shifting to options he won the championship with a return of 563%. Now he trades options holding them 3-30 days and day trades S&P 500 and NASDAQ futures.

To succeed as a trader, one needs complete commitment… Those seeking shortcuts are doomed to failure. And even if you do everything right, you should still expect to, lose money during the first five years… These are cold, hard facts that many would-be traders prefer not to hear or believe, but ignoring them doesn’t change the reality. – Mark D. Cook

Victor Sperandeo

Successful Technical Analyst Victor Sperandeo An options trader and technical analyst who had a string of 18 profitable years clocking an average return of 72%. His first loss was in 1990 with a 35% drawdown.

He described his style as only taking risks when the odds are in his favor. After an extensive two year study he identified ‘life expectancy’ profiles for market moves. For example he noticed that an intermediate swing on the Dow during a bull market is typically 20%. After that 20% has been realized the odds of further advances are diminished significantly.

Understanding this makes a big difference he says, like when a life insurance policy is written the risk profile of an 80 year old is very different from that of a 20 year old. Sperandeo believes that the most common reason for failure with technical analysts is that they apply their strategies to the market with no allowance for the life expectancy of the bullish or bearish move.

Theses days Victor is the President and CEO of Alpha Financial Technologies which is widely known for its trend-following, futures-based indices: The Diversified Trends Indicator, The Commodity Trends Indicator, and The Financial Trends Indicator.

The key to trading success is emotional discipline. Making money has nothing to do with intelligence. To be a successful trader, you have to be able to admit mistakes. People who are very bright don’t make very many mistakes. Besides trading, there is probably no other profession where you have to admit when you’re wrong. In trading, you can’t hide your failures. – Victor Sperandeo

Ed Seykota

THE pioneer when it comes to computerized trading systems. Inspired by the work of Richard Donchian he began developing futures trading systems in the 1970s. Seykota tested and implemented his ideas using an IBM 360. This was well before the days of online stock trading, back then such computers were the size of a large room and were programmed using punch cards.

Originally he wrote trend following systems with some pattern recognition and money management rules. By 1988 one of his clients’ accounts was up 250,000% on a cash-on-cash basis. Today it is reported that his daily trading efforts consist of the few minutes it takes him to run his computer programs and generate the new signals.

Ed attributes his success to good money management, his ability to cut losses and the technical analysis based systems he created. He refers to fundamentals as “funny-mentals” explaining that the market discounts all publicly available information making it of little use.

There are old traders and there are bold traders, but there are very few old, bold traders. – Ed Seykota

Worlds Richest TA Traders:

I was very happy to discover that the Forbes Rich List was scattered with investors and hedge fund managers who have profited handsomely despite giving fundamentals a back seat. Here are my favourites from the 2012 list:

2012 Forbes – #82 James Simons – 11.0 Billion

Sometimes referred to as the “Quant King” he is also a maths guru and a very smart cookie who studied maths at MIT and got a Ph.D. from UC, Berkeley. Simons deciphered codes for U.S. department of defence during Vietnam and went on to found Renaissance Technologies in 1982 and at the start of 2013 was managing over 15 billion.

He Co-authored Cherns-Simons theory in 1974; a geometry based formula now used by mathematicians to distinguish between distortions of ordinary space that exist according to Einstein’s theory of relativity. In addition to this it had been used to help explain parts of the string theory.

Renaissance Technologies is a quantitative hedge fund that uses complex computer models to analyze and trade securities. A $10,000 investment with them in 1990 would have been worth over $4 million by 2007.

We are a research organization… We hire people to make mathematical models of the markets in which we invest… We look for people capable of doing good science, on the research side, or they are excellent computer scientists in architecting good programs. – James Simons

The flag ship Medallion Fund trades everything from Pork Bellies to Russian Bonds. In 2008 the fund forged ahead another 80% even after the 5% management and 44% performance fee. More recently 9.9% returns were seen net of fees through the end of July 2012. Unfortunately the Medallion fund is now only open to employees, family and friends.

The key to the success of Renaissance Technologies has much to do with the people they hire; PhDs and not MBAs. About a third of their 275 employees have PhDs. Those on the payroll include code breakers and engineers, people who have worked in computer programming, astrophysics and language recognition.

They also look for people with creativity. Simons says that creativity is about discovering something new and you don’t do that by reading books or looking in the library, you need ideas.

Everything’s tested in historical markets. The past is a pretty good predictor of the future. It’s not perfect. But human beings drive markets, and human beings don’t change their stripes overnight. So to the extent that one can understand the past, there’s a good likelihood you’ll have some insight into the future. – James Simons

Forbes 2012 #88 – Ray Dalio – 10 Billion

Rich Technical Analyst Ray Dalio Placed his first trade at the age of just 12, studied finance at Long Island University and got and MBA from Harvard in 1973. Dalio traded futures early in his career and founded Bridgewater Associates in 1975 when he was just 25. From the moment he started managing money Dalio kept notes in a trading diary with the hope that his ideas could later be back tested.

Now king of the rich hedge fund industry, Dalio controls the world’s biggest hedge fund Bridgewater Associates which has about $130 billion in assets. His flag ship fund ‘Pure Alpha’ has had an average annual return of 15% from 1992 – 2010 and has never suffered a loss over 2%. Big bets on U.S. and German government bonds saw his funds surge about 20% in 2011; a year where most hedge funds struggled.

Dalio focuses heavily on understanding the processes that govern the way the financial markets work. By studying and dissecting the fundamental reasons and outcomes from historical financial events he has been able to translate this insight into computer algorithms that scan the world in search of opportunities. He says by doing this research it provides “a virtual experience of what it would be like to trade through each scenario”.

Ray is particularly interesting because he does not believe in an approach devoid of understanding fundamental cause-effect relationships. He has however been able to use technical analysis to identify mispriced assets based on fundamental information. So to say that Ray gives fundamentals analysis the back seat to technical analysis would not be entirely accurate.

Well defined systems, processes and principles are his key when is comes to making investing decisions. All strategies are back tested and stress tested across different time periods and different market around the world to ensure that they are timeless and universal. The strategies are all about looking at the probabilities and extreme caution is exercised; for a hedge fund Bridgewater uses relatively low leverage of 4 to 1.

While the hedge fund industry as a whole has an average correlation to the S&P 500 of 75% Dalio claims to have discovered 15 uncorrelated investment vehicles. Bridgewater focuses mostly in the currency and fixed income markets but uses powerful computers to identify mispriced assets on dozens of markets all over the world. To find so many different uncorrelated investments requires stepping well beyond the realm of the stock exchange.

I learned to be especially wary about data mining – to not go looking for what would have worked in the past, which will lead me to have an incorrect perspective. Having a sound fundamental basis for making a trade, and an excellent perspective concerning what to expect from that trade, are the building blocks that have to be combined into a strategy. – Ray Dalio

2012 Forbes – #106 Steven Cohen – $8.8 Billion

Top Technical Analyst Steven Cohen Now a well know force on Wall Street due to his world class performance and high volume of trading which accounts for about 2% of the daily volume on the New York Stock Exchange. Steven started trading options in 1978 and made $8,000 on his first day.

He founded hedge fund SAC Capital in 1992 with $25 million in assets. By the end of 2012 SAC had about $13 billion under management across 9 funds and had averaged 36% net return annually. It is reported however that SAC suffered a loss of approximately 15% in 2008. Its flagship fund was up 8% in 2011, a year in which the average hedge fund was down 5% and up again in 2012 8% through to August.

Steven keeps his activities very secretive but his style is understood to be high volume hair-trigger stock and options trading.

The old guard wasn’t crazy about me, I used to hear it all the time… Most of the old-school had no belief in anything that wasn’t based on fundamental analysis… We were trading more than investing, and people frowned on it, they looked at it and didn’t want to partake. Finally, they said, ‘Shoot. He’s making money.’ And they started copying me. – Steven Cohen

He believes that 40% of a stocks price fluctuations are due to the market, 30% to the sector and 30% to the stock itself.

Despite the great performance of SAC Capital their best trader makes a profit on 63% of their trades while most of the traders are profitable 50-55% of the time. Interestingly 5% of their trades account for virtually all their profits. Something to keep in mind the next time you get a spam email claiming that your can buy a 95% accurate ‘Stock Trading Robot’.

Steven attributes the success of SAC to the breath of experience and skills found in the people working for the firm. They look for traders who have the confidence to take risks, those who wait for someone to tell them what to do never succeed.

You have to know what you are, and not try to be what you’re not. If you are a day trader, day trade. If you are an investor, then be an investor. It’s like a comedian who gets up onstage and starts singing. What’s he singing for? He’s a comedian. – Steven Cohen

Forbes 2012 #330 – Paul Tudor Jones II – 3.6 Billion

Successful Technical Analyst Paul Tudor Jones, II Both a discretionary and systems trader who had his early success trading cotton futures. Jones majored in economics at the University of Virginia in 1976 and got a job working for the cotton speculator Eli Tullis not long after graduating. The greatest lesson that he learnt from Eli was emotional control but was later fired for falling asleep on the job after a big night out on the town with his friends.

In 1983 Jones began the hedge fund Tudor Investment Corp with $300,000 under management. At the end of 1012 the fund was estimated to be managing $12 billion and had achieved an average annual return of 24%. His firm’s flagship fund, BVI Global saw a gain of 2% in 2011 and 3.8% net of fees through to August 2012.

Much of his fame came from predicting the 1987 stock market crash from which he pulled a 200% return or roughly $100 million. Jones claims that predicting the crash was possible because he understood how derivatives were being used at the time to insure positions and how selling pressure on an over priced market would set off a chain reaction. He says that you need a core competency and understanding of the asset class you are trading.

He attributes his success to a deep thirst for knowledge and strong risk management. Jones is a swing trader, trend follower and contrarian investor who also uses Elliot Wave principles. Most of his profits have been made picking the tops and bottoms of the market while often missing the ‘meat in the middle’. Jones believes that prices move first and fundamentals come second.

A self professed conservative investor who hates losing money. He tries to identify opportunities where the risk/reward ratio is strongly skewed in his favor and does not use a lot of leverage. In his eyes a good trader is someone who can deliver an annual return of 2-3 times their largest draw down.

Don’t be a hero. Don’t have an ego. Always question yourself and your ability. Don’t ever feel that you are very good. The second you do, you are dead… my guiding philosophy is playing great defense. If you make a good trade, don’t think it is because you have some uncanny foresight. Always maintain your sense of confidence, but keep it in check. – Paul Tudor Jones II

Top Traders Secrets

It is clear that Technical Analysis has worked in the past and continues to work for many successful traders and investors today. But what are the common aspects that are being were used by these successful market technicians?

Unfortunately due to the extreme secrecy surrounding nearly all of these traders, the specific methods that they use are not known. However I did uncover the following:

Common Themes

Mechanical trading models were used my many of the most successful.
They all used clearly defined systems and stuck to their rules.
Many of them back tested their ideas before implementing them in the real market.
Most of them surrounded themselves with exceptional people who had the expertise they needed.
Many of them lost money for the first few years before hitting their stride.
Each trading system suited their personality.

Common Personality Traits

Low Emotional Reactivity – Staying calm; experiencing neither major highs nor lows.
Detached – Understanding the market does what it does that they have no control over it.
Humble – With little ego they have no challenge taking losses or letting profits run.
Decisive – They reach decisions quickly and take action without second guessing.
Conscientious – Self-controlled, disciplined, consistent, and plan-driven, they persevere.
Confident – They have faith in their system and their ability to implement it.

It is undeniable that Technical Analysis does work so ignore all those who try and tell you otherwise. The next step is to make Technical Analysis work for you and that first requires identifying or creating a system that suits your personality.

What has your experience been with Technical Analysis? Did I leave anyone off the list? Let me know in the comments section below. (Also I realize that I listed 8 traders not 7 :))

Related Posts

MACD – Test Results

The MACD is one of the most widely used technical indicators in the world and is included in every charting program worth owning. Unfortunately however, reliable data on its performance is almost non-existent. Are the standard settings of 26, 12, and 9 the best? To reveal the answer we tested 2000 different combinations through 300 years of data across 16 different global markets~. Stand by for the results below…

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And Results For all 2,000 MACDs Tested

MACD – Test Results:

Our Testing Strategy Explained
MACD Test Sets – Broad
MACD Test Sets – Short Term Trading
MACD Test Sets – Practical Trades: Long
MACD Test Sets – Practical Trades: Short

MACD Conclusion

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

Trading Rules:

An entry signal to go Long for each MACD tested was generated when the MACD Line was above zero AND above the Signal Line. The position was closed when the MACD Line moved below zero OR below the Signal Line (vice versa when going short)^.

If what you have read so far does not make much sense, please read more about the MACD before continuing 🙂

MACD Test Sets – Broad

MACD, EOD Long - Annualized Return During Exposure

Above you can see the annualized return during the time each MACD was exposed Long to the market. Clearly the Signal Line setting is far more influential than the ‘Fast’ and ‘Slow’ Moving Averages (MACD Line). To my surprise having the Signal Line as fast as just 2 days produced the the best results and even more surprising is that the trades produced are not prohibitively short (8 – 27 days on average from the table above, see spreadsheet for full stats).

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MACD Test Sets – Short Term Trading

MACD EOD, Long - Annualized Return During Exposure

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:

MACD EOD, Short - Annualized Return During Exposure

For me, when assessing a trading system, I am more interested in the return during exposure than the return overall. A system may have you exposed to the market for 30%, 70% or 99% of the time but the more time you are exposed to the market the more time your money is at risk. While my money is at risk, if it is not working hard, if I am not getting a high return, then I would rather sit in cash!

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…. Sooooo, what is the catch? Lets have a closer look:

MACD EOD 1, 56 Long and Short, Sig 2

The pink line on the chart above is the performance, taking signals both Long and Short from a MACD with a ‘Fast’ MA of 1 (price), a ‘Slow’ MA of 56 and a Signal Line of 2. I have included the results from the best FRAMA for comparison.

The impressiveness of this MACD can’t be doubted; consider the fact that it achieves these returns while only being exposed to the market 56% of the time and delivers a 42% probability of profit for each trade. But can you see the problems? With an average return of just 0.25% and an average trade duration of 4 days, a MACD with these settings is limited in its practical applications.

Firstly you would need near frictionless trading, such as that offered by some index mutual funds (e.g. Rydex, ProFunds, or Direxion)

Secondly you would need to gain exposure to several diverse equity index funds. Part of the reason for the success of this strategy is the fact that it spreads the risk across 16 different global markets, some of which performed better than others in our tests. In the real world frictionless trading is not accessible to such a variety of indices.

Thirdly between 2003 and 2007 while the Global Average was experiencing a very strong bull market the MACD underperformed quite significantly.

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MACD Test Sets – Practical Trades: Long

MACD EOD, Long - Annualized Return During Exposure

The table above is the result of a search for some more practical MACD settings. As you can see the best returns localise around the the 21/81 mark area:

MACD EOD 21, 81 Long, 2

Above we are looking at the performance of a 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 – See Full Stats).

Now when comparing the 21, 81, 2 MACD to the FRAMA it must be taken into consideration that the MACD is only exposed to the market 35% of the time while the FRAMA is exposed 57% of the time. So a side by side, total return comparison is not really fair. What is good to see however is the consistency and stability from the MACD during market declines. What I don’t like though is the familiar under-performance during the strong bull market between 2003 and 2007.

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MACD Test Sets – Practical Trades Short

MACD EOD, Short - Annualized Return During Exposure
The Short side of the market behaves differently to the Long so it is not surprising to see that a more reactive MACD performs better, and the top returns were found around 16/97:

MACD EOD 16, 97 Short, 2

Above we are looking at the performance of a MACD going Short with a ‘Fast’ Moving Average of 16, a ‘Slow’ Moving Average of 97 and a signal line of 2 compared to the best FRAMA (also included is the standard MACD of 12, 26, 9 – See Full Stats).

The 16, 97, 2 MACD is quite exceptional, managing to match the returns from the FRAMA with 2/3 the market exposure and a higher probability of profit. These results are very exciting. It would appear as though the MACD’s true strength is in its ability to go Short.

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MACD Conclusion

I have been a big fan of the MACD for a long time and had high expectations for these test results. But reality has been harsh on the MACD and in many ways the Emperor has no clothes.

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.

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.

Golden Cross – Which is the best?

The Golden Cross typically referrers to the crossing of the 50 and 200 Day Simple Moving Averages. When the shorter term average moves above the longer term average this is seen by many as the beginning of a sustained bullish period and vise versa. It is not wise however to risk your money in the market on the assumption that such a theory is true.

One has to ask, which is better, a SMA Golden Cross or an EMA Golden Cross? Are the settings of 50 & 200 really the best? What is the profile of the trades that this strategy generates as far as duration, probability of profit, draw downs etc. In order to answer these questions we applied some brute mathematical force and tested 1750 different combinations through 300 years of data across 16 different global markets~. We have done the hard work and you get the benefits for free… aren’t you lucky.

Michael Stokes over at MarketSci has also written a great series on Trading The Golden Cross.

Download A FREE Spreadsheet With Data, Charts

And Results For all 1750 Moving Average Crossovers Tested

Golden Cross, Moving Average Crossover – Test Results:

Our Testing Strategy Explained
Simple vs Exponential Moving Average Crossover
Daily vs Weekly Moving Average Golden Cross
Golden Cross – Refined Test Sets
The True Golden Cross

Golden Cross Conclusion

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).

Hopefully by using this tactic we can identify the multiples or ratios that deserve more targeted testing.

Simple vs Exponential Moving Average Crossover

In our original MA test; Moving Averages – Simple vs. Exponential we revealed that the Exponential Moving Average (EMA) was superior to the Simple Moving Average (SMA). If the same proves to be true with the ‘Golden Moving Average Crossover’ then this will further validate the EMA as being of higher-caliber than the SMA.

Simple vs Exponential MA Crossover Returns

The chart above fades between the results from the EMA and the SMA crossover tests. As you can see the EMA outperforms the SMA by well over a percentage point on average. This unequivocally confirms that the EMA is superior to the SMA. Further more it should be noted that every single EMA combination tested (and most SMAs) outperformed the buy and hold annualized return of 6.32%^ during the test period (before allowing for transaction costs and slippage). But “technical analysis doesn’t work” they say.

Below are the all the individual Annualized returns from the EMA chart above:

EMA Crossover, EOD Long, Annualized Return During Exposure

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.

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Daily vs Weekly Moving Average Golden Cross

What about Weekly data you ask? We didn’t test with Weekly data but we did test using End Of Week (EOW) signals on Daily data (previous tests on the EMA revealed that the two produce almost identical results). When we used EOW signals the returns dropped by 0.5% on average while the trade duration increased by just 10 days and the probability of profit increased by only 2%. In other words; you are better to use daily data and EOD signals on a Moving Average Crossover Strategy.

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Golden Cross – Refined Test Sets

After getting a better idea of the ‘sweet’ spot from our first round of testing, we refined our range and instead of continuing with ratios of fast and slow EMA crossovers, we progressed in a liner fashion. So what is the “True Golden Cross” that proved the best returns during our tests?

EMA Crossover, EOD Long, Annualized Return

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.

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The True Golden Cross

The profile of the trades produced by the true Golden Cross have many very desirable features; a significant average trade duration (94 days), a high probability of profit (45%) and solid returns across the board (even on the difficult, bear savaged Nikkei 225). While it does not produce returns any where near as good as the best FRAMA, it certainly out performs the traditional Golden Cross of 50 / 200. Plus with the long trade duration, it may be more desirable than the slower FRAMA for use as a long term indicator as one part of a complete trading system:

252 Day FRAMA, EOW 40, 250 Long

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Golden Cross 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.

The moving average crossover is a component of the popular Moving Average Convergence Divergence (MACD), see the completed test results here.

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More in this series:

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~ An entry signal to go long for each crossover tested was generated when the faster moving average of each pair closed above the slower moving average (the opposite closed the position or triggered a signal to go short. 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 for Daily data. Eg. Daily data with an EOW signal means that only the signals at the end of each week were taken.
^ 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 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.