Trendfollowing indicators are the most popular type of indicators. They are good in identifying trends and giving you simple buy and sell signals.
One of the common charges against trendfollowing indicators is that that they lag significantly after price and generate signals too late. But you don’t want an indicator to get you into trend changes too fast either, as you can then be lead into too many false signals in a noisy, vacillating market. It is encouraged that you play with the length and calculation methods of different trend indicators to find the right balance between speed/responsiveness and smoothness/reliability.
Examples for Indicators that are trendfollowing are the Moving Average, MACD and the Ichimoku Kinko Hyo.
Trend Indicator  Relevance 

Moving Average  Most Popular by far, the King of All Indicators 
MACD  Second Most Popular, Great for Reducing Lag 
Ichimoku Kinko Hyo  Very Popular Japanese Indicator, used for centuries in manual systems, can also be profitably traded as an EA, and we have researched and rated 9 of them for you. 
One of the first things that a trader is taught is that “the trends is your friend,” and you have to “go with the flow.” A simple and highly popular approach which identifies the trend is the moving average. There is probably more money being traded today using moving averages than with all other technical indicators combined.
Popularity is difficult to quantify, but according to a quick review into the free indicator/EA repository of Desynced.net, it is the foremost popular indicator used as a basis for EA and indicator construction:
Popularity Rank  Indicator  EAs Based Upon  Indicators Based Upon 

1  Moving Average  886  2353 
2  MACD Histogram  273  255 
3  RSI  257  547 
4  Stochastics  196  331 
5  CCI  138  333 
6  Parabolic  129  140 
7  Bollinger Bands  67  146 
8  Larry Williams Percent Range  62  183 
9  Movement Directional Index  59  240 
10  Momentum Indicator  45  68 
Moving averages have enjoyed such popularity because they provide the clearest method to identify a trend, smoothing the erratic data to see the trend more clearer.
Simply put, a simple moving average is the average of a currency over a set period of time. For example, a 9 day simple moving average is the average of the last 9 day’s prices. It is calculated by taking the sum of the last 9 days of a currency’s close price and then dividing by 9. It is called simple when there is equal weight given to each price over the calculation period. Other types of moving averages are weighted averages and exponentially smoothed averages, which we will discuss later.
There are three critical parts to any moving average:
 Length (and/or Time Frame)
 Calculation Method (Simple, Exponential, Smoothed, Linear Weighted)
 Crossover Method (Simple, Dual and Triple)
We will cover each in turn, exploring the variables of each, uncovering their strengths and weaknesses. All these parts must first, however, be placed in context of the vulnerabilities of the moving average itself (its problems with lag, noise and whipsaws), for making the choice between the different methods, lengths and crossover techniques are ways of countering these vulnerabilities.
Vulnerabilities of the Moving Average
The moving average is one of the most popular and useful indicators to depict a trend, but one should also be aware of its two inherent vulnerabilities:
 It lags the markets;
 It can be subject to market noise;
 It can be vulnerable to sideways and whipsaw markets.
Let us show you an example of these vulnerabilities from a screen shot of a 25 daily simple moving average superimposed upon the EURUSD daily chart of 2011:
You can easily see that from Jan to May 1, 2011, the market had remained above the 25day moving average, which means that it was in a strong uptrend. The moving average would have helped us see the trend and stay bullish on EURUSD for over five months. That is the strength of the moving average and during that time those who followed the trend set by the moving average would have racked up significant gains.
Now let us examine three weaknesses of the moving average over the same period. The first weakness is that when the trend changed direction in early May, the moving average did not show this trend change till 400500 pips later, until price crossed under the moving average at 1.4300. This is the problem of lag, which means that a significant move will have already occurred before the indicator was able to generate a signal. The second weakness is that in the middle of February the market dipped briefly below the moving average, signaling a false trend change. You can see another false trend change occur earlier in December when the market briefly rose above the moving average. This is the problem of market noise, a term that refers to all the price data that distorts the picture of the underlying trend, such as small corrections and intraday volatility. The third weakness can be seen from May to September 2011, where the market stayed in a sideways, very noisy, very narrow 300 pip range, with the market weaving up and down through the 25day moving average. This is the problem of a sideways and noisy market. This sideways, noisy period would have represented significant losses for traders employing moving averages as they would have entered and been beaten up on numerous fake trend signals and subsequent stopouts. Let us go over these three weaknesses in turn.
Weakness #1: The Problem of Lag
We have to remember that the moving average is trendfollowing. It can follow the trend when it is already developed but it cannot forecast a new one. In fact, it is a lagging indicator, in that it can still be rising after the price has hit resistance and crashed. In the picture above, the moving average was still rising when the EURUSD hit resistance at 1.4900 and crashed down 500 pips on May 5, 2011. You would have been able to catch some of the fall, as the price fell below the 1.4300 moving average and hit the floor of 1.3900, but it would have been nice to be able to catch more of the move from when it happened.
Fixing Lag: There are a couple possibilities. You can reduce the length (number of days) in the moving average to make it more responsive. A shorter period moving average is more sensitive to recent prices. You can also change the calculation method, opting for an exponential or linear weighted moving average that gives more value to recent price changes.
Weakness #2: The problem of Noise
A price series with prices varying far from the moving average is said to have a lot of noise, like the static you get from a car radio when it is out of range. A moving average is designed to smooth out the erratic data so that we can better able to detect a trend. Nevertheless, even in the best of moving averages, erratic data (in the form of volatile price spikes and short corrections) can still escape the containment of the moving average. We can see this in the picture above, in the middle of February 2011, where short lived bearish correction caused prices to temporarily fall below the 25daily moving average, putting some trend traders in short trades that would have ended in losses. Numerous false trend changes of this sort entered into the picture during the summer of 2011, when the market moved in a sideways, directionless fashion with significant noise.
Fixing Noise: There are a couple of possibilities. You can apply more days to the moving average to reduce noise. You can increase the length (number of days) in the moving average to smooth it out and make it less responsive; for instance, if you increase the days from 25 to 50, the noisy outliers become contained within the larger moving average, which makes the moving average safer to trade. An abnormally high or low price in a 50 period moving average is less significant than in a 25 or 10 period moving average because deviant price carries less weight in the calculation. Opting for simple or smoothed averages would also ally yourself with a form of calculating the moving averages that emphasizes the smoothness (antinoise) factor over the speed (antilag) factor.
Weakness #3: The problem of a sideways market
It is exceedingly difficult for any moving average based trend following strategy to overcome the pain of a sideways market. Sideways markets usually occur after a run up or down, consolidating in a narrow range before deciding which path to take again. The sideways EURUSD occurred during three summer months of 2011 (June, July and August), just after it fell hard in May. It was not sure if it should try to recover from the fall or keep falling. During the summer the market moved up and down through the moving average, and it would have done this up and down weaving, called a whipsaw (rapid movement of prices up and down in a volatile market, throwing up misleading signals to buy or sell at the end of the move), even if the moving average were reduced to 5 or 10, or increased to 50 or 200. Moreover, it would have produced this sideways whipsaw activity no matter the calculation method applied to it (no matter if it was smoothed or exponential). This vulnerability represents the greatest threat to traders employing the moving average as a determinant of trend direction, as it can result in numerous losses.
Fixing Sideways Markets: There is perhaps no surefire way to detect in advance the existence of a sideways market. Often it is best to follow the trend when it occurs, preparing to make the big money when it occurs 30% of the time, and also preparing yourself to survive the inevitable losses during sideways activity with sound money management. In the sideways situation of the summer of 2011, it was possible to take the summer off and avoid the legendary choppiness of the summer. Most big money traders are on holiday during the summer, which helps explain why there is no money in the market to sustain a trend direction. Hint: Trend traders should likewise take the summer off, or at least reduce the leverage on their positions. The only other option is to zoom in on a shorter (5 or 15 minute) time frames that may contain minitrends. These smaller time frames are generally far too noisy for most trend traders but they might bear some fruit during a sideways market.
Now that we have explored some of the vulnerabilities of the moving average and proposed some fixes, we will explore some of these fixes in more detail. Basically, they deal with the length of the moving average, the calculation method, and the crossover technique.
1. Altering Lengths (and/or Time Frames) to Overcome Twin Problems of Lag and Noise
Navigating the narrow straight of length is like trying to simultaneously avoid the Scylla (6headed sea monster) of lag, and Charybdis (whirlpool) of choppiness. The remedies for overcoming lag and noise tend to cure the one problem at the same time they bring about the side effect of the other. To overcome lag, we decrease length, which creates more noise, and to overcome noise, we increase the length, which creates more lag.
Problem  Length Remedy 
Time Frame Remedy 
Side Effect 

Lag  Decrease  Decrease  Noise Increase 
Noise  Increase  Increase  Lag Increase 
First point: Decreasing length or time frame can mitigate the problem of lag.
Let us zoom into May of 2011 for the EURUSD, when the market stopped its bullish advance and turned bearish. I noticed how the 25day period moving average did not enter into the short trend until after the market plunged 500 pips. It would have been nice to capture this trend reversal sooner than later. Could decreasing the length catch the trend reversal sooner? Yes, it would.
Here is a screen shot of May 2011 comparing a 25day moving average versus shorter 10day moving average:
As you can see, the market crossed under the 10Day SMA much faster, 200 pips faster, than the 25Day SMA. That is a significant pip advantage for the faster period moving average, and thus it was successful in reducing the lag. However, it comes at a price: more noise. If you look at the market before and after this period you will see that using this shorter 10day SMA resulted in more false signals (highlighted in the purple circles above) from mini corrections that reverted back to the main trend. The losses from these fake outs would have negated the 200 pip advantage it gained picking up on the faster trend change on May 5, 2011.
Another way of reducing lag is reducing the time frame. For instance, if one wanted to get into the trend faster, one can decrease the daily period from 25 to 10, or the 10 day moving average can be translated into a 60 period moving average on H4 (6 four hour bars in 24 hrs; thus, 10X6= 60) to arrive at the same thing.
Ultimately, it is the noise in the market that undermines the performance of the moving average, and smoothness negates the noise. The foremost way to make a moving average smoother is to increase the length or time frame. A longer period average and a larger timeframe both have greater smoothing effects, and thus they both carry the benefit of staying the course of the trend, avoiding the false reversals and whipsaws. If one were really noise adverse, one would plot the moving average of this year on a 50day moving average or a 200day moving average.
In the world of stocks, the 50day moving average crossing over/under the 200day has been coined the “Golden Cross.” Here is the 50200 MA “Golden Cross” on EURUSD H4 chart:
You can see in the chart above that trading the “golden crossover” on the EUR/USD H4 time frame would have generated considerable profit for 2010. One could have rode a large downward trend from April to June and two significant upward trends from July to October 2010. The only hit you would have received would be the false short signal during August, which turned out to be a short lived correction from the upward advance.
The problem with the longer period average is that it can extend the time it takes for a market to turn around, and by the time it turns around the move may be over. To prevent the late arrival to a bull or a bear party, traders decrease the length (or time frame) and modify the calculation method of the moving average. But, as we have seen, the problem selecting shorter lengths is opening yourself to greater noise and choppiness, more false signals that can bleed your account.
2. Altering the Calculation Methods to Solve the Problems of Lag and Noise
Altering the length parameter of moving averages is the foremost way of dealing with lag and noise, but there are various calculations methods that can weigh in on solving the two problems. Some calculation methods weigh in on the side of speed (to reduce lag) and others weigh in on the side of smoothness (to reduce noise).
The four major calculation methods are: simple, exponential, smoothed and weighted.
Constant  Value  Name  Calculation Description 

MODE_SMA  0  Simple Moving Average  Equal weight is given to each price over the calculation period. Bias: Smooth (antinoise) 
MODE_EMA  1  Exponential Moving Average  More weight is given to recent prices in attempt to reduce lag. Bias: Speed (antilag) 
MODE_SMMA  2  Smoothed Moving Average  Similar to a SMA; however, rather than subtracting the oldest value, the previous smoothed average value is subtracted. Bias: Smooth (antinoise) 
MODE_LWMA  3  Linear Weighted Moving Average  Designed to put more weight on recent data and less weight on past data. Bias: Speed (antilag) 
Simple Moving Average:
The most commonly used type of moving average, the simple moving average (SMA) is calculated by adding and then averaging a set of numbers representing the market. The SMA is by far the more popular mode, and it is considered highly useful because of its smoothing effect.
The formula for the SMA is as follows:
Where N – Number of Calculation Periods
It is just simple arithmetic. We all have been taught how to average in public school, measuring 10 of something, adding them up and then dividing by 10. In this case, we would be adding up the average number of 10 closing prices. The next day you add the newest close price to the total and subtract the oldest close price, keeping the total number of close prices a constant of 10.
The SMA Advantage: The SMA emphasizes smoothness, that is, it tries to smooth out the erratic behavior of the market in order to see the trend. However, there are those who do not like the fact that the SMA lags behind the latest data point by nature of its smoothing, and they prefer to give more weight to more recent data points, as in the weighted and exponential moving averages.
Exponential Moving Average:
The Exponential Moving Average (EMA) is calculated by adding the moving average of a certain share of the current closing price to the previous value.
The formula for the EMA is as follows:
Where:
CLOSE(i) — the price of the current period closure;
EMA(i1) — Exponentially Moving Average of the previous period closure;
P — the percentage of using the price value.
The EMA advantage: Exponential moving averages assign more meaning to the recent prices and less to the closing price from the period’s beginning. Thus it is faster at detecting a trend reversal. Naturally, and depending on the length, it can be more susceptible to market noise.
Smoothed Moving Average (SMMA):
The smoothed moving average is like a simple moving average with twice the smoothing effect. The first value of this smoothed moving average is calculated as the simple moving average (SMA):
SMMA1 = SUM1/NThe second and succeeding moving averages are calculated according to this formula:
PREVSUM = SMMA(i1) *N
SMMA(i) = (PREVSUMSMMA(i1)+CLOSE(i))/N
Where:
SUM1 — is the total sum of closing prices for N periods;
PREVSUM — is the smoothed sum of the previous bar;
SMMA1 — is the smoothed moving average of the first bar;
SMMA(i) — is the smoothed moving average of the current bar (except for the first one);
CLOSE(i) — is the current closing price;
N — is the smoothing period.
SMMA Advantage: The SMMA emphasizes smoothness even more so than the SMA, trying to smooth out the erratic behavior of the market in order to see the trend. You will see that the SMMA line looks like a doubling of the length of the EMA. However, the problem with the SMMA is that it could lag too far behind the price movement.
Linear Weighted Moving Average (LWMA):
Like the EMA, the latest data is of more value than more early data. Weighted moving average is calculated by multiplying each one of the closing prices within the considered series, by a certain weight coefficient.
Where:
SUM(i, N) — is the total sum of weight coefficients.
LWMA Advantage: Like the EMA, the LWMA assigns more meaning to the recent prices and less to the closing price from the period’s beginning. Thus they are faster at detecting a trend reversal, though it they can be more prone to market noise.
In the end, while one may have a bias for the simple for its smoothness, or the exponential for its speed, one can never know which will be the real queen of the game until both are given a fair trial.
3. Choosing the Crossover technique (Single, Dual, and Triple) as ways to deal with Lag and Noise.
Single MA Crossover
In its simplest form, called the Single Moving Average Crossover, you go long or short when the closing price crosses over / under the moving average. You buy when the closing price crosses over the moving average, and sell when it crosses under the moving average.
Here is an example of 200MA on EURUSD H4 chart (Sept2010 to Sept2011):
You can see that it did successfully pick up the three large downtrends (+410, +600, +460) on the EURUSD over the last year. You could have picked up these profits with the dual crossover alone without having to pay attention to the news of the debt contagion in Europe. Though it has less false trades than the single moving average, it it is still vulnerable to sideways markets. The dual crossover suffered during the summer (JunSept) of 2011, and it suffered in the spring (MarApril) of 2012.
Benefits: The major benefit of the dual crossover is that it is still a relatively simple and popular trend following technique while overcoming some of the potential choppiness of the single crossover method. Because you are delaying the entry till the fast moving average cross instead of the closing price cross, you can sidestep many false cross signals.
Drawbacks: The drawback of the dual crossover is that waiting for the crossover event can delay the entry and exit. This delay can cause you to lose part or all of the move.
Triple Moving Average
The triple moving average employs three moving averages of various lengths (fast, medium and slow): when the fast moving average crosses a medium moving average, and the medium crosses a slow moving average, a bullish or bearish signal is generated depending on the direction of the crossovers.
The common moving averages used for this event are 4, 9 and 18 periods, particularly on the daily time frame (at least in the world of stocks). When the 4day (fast MA) crosses above/below the 9day moving average (mid MA), the event has “started”, and it is confirmed when the 9day moving average (mid MA) crosses above/below the 18day moving average (slow MA). A bullish signal is generated when the crossover above, and a bearish signal is generated on the crossover below.
Wait a minute, you ask: isn’t the entry trigger given when the midMA crosses over /under the slow MA, and thus isn’t very much like the dual crossover in that respect? Yes, that is true. There is overlap in the entry. I think the real difference lies in the exit. Traders use the triple moving average to more quickly exit their positions. They exit when the fast MA crosses above / below the mid MA. While it may seen like a good idea at first, in practice it has the disadvantage of getting out of the trend too early, before it has had a chance to fully mature. It is wise to back test this idea to see if offers any extra edge than the other two types of crossovers; in my own experience it has offered no extra edge and often diminished the returns.
Conclusion
The moving average is perhaps the simplest of the trend following indicators, but its proper usage can be more complicated than one suspects. Again and again, I warn about the vulnerabilities of the moving average, namely its problem with lag (catching the trend too late), and its problem with noise and choppiness (catching too many false trend reversals). The additional problem is that lag and noise are twin problems that one needs to navigate between, and if one veers to avoid the one, one gets too close to the other. It is like trying to steer between Scylla and Charybdis, where Scylla the 6headed sea monster represents the problem of lag (that eats away your potential pips) and Charybdis the whirlpool represents the problem of noise and choppiness (which can sink your account in a series of churning waves). Modifying the length, calculation method and crossover method are ways to steer the moving average away from one problem and closer to the other. Here is a table that sums up the dilemma:
Modifications  Avoid Lag (Scylla) Side effect: More Noise (Charybdis) 
Avoid Noise (Charybdis) Side Effect: More Lag (Scylla) 

Length  Shorter Length  Longer Length 
Calculation Method  EMA, LMA  SMA, SMMA 
Single, Dual and Triple  Single (and Triple)  Dual 
To overcome lag, we decrease length or use EMA/LMMA calculation methods or use the single (and triple) MA crossover techniques, all of which creates more noise. To overcome noise, we increase the length or use SMA/SMMA calculation methods or the dual moving average technique, all of which which creates more lag.
It may be possible to find that Greek “middle way”, the balance between the two extremes: an optimized length, calculation and crossover method that is appropriately fast enough to reduce lag and yet smooth enough to reduce noise. If you can find it, the more power to you.
But if you have to make a decision between the two, it is probably better to choose, like Odysseus did, the lesser of the two evils. It is probably better to pass closer to lag of Scylla rather than noise of Charybdis, because in the end it is preferable to lose out on some potential profit (from not getting into the trend fast enough), rather than become mired in a broiling and dangerous whipsawwhirlpool market activity that can sink all your profits. You would be better off losing a few sailors than your whole ship.
The AntiLag MACD
The MACD (Moving Average Convergence/Divergence) was originally developed by Gerald Appel, a stock market technician, in the late 1970s (Appel, Gerald. The Moving Average ConvergenceDivergence Method. Great Neck, NY: Signalert, 1979). It is used to spot changes in the strength, direction, momentum, and duration of a trend in a stock’s price. There have been many other technical tools that have been developed since the MACD, but it has remained a favorite and useful tool by traders over the course of time.
Popularity is difficult to quantify, but according to a quick review into the free indicator/EA repository of Desynced.net, MACD is the second most popular indicator used as a basis for EA construction:
Popularity Rank  Indicator  EAs Based Upon  Indicators Based Upon 

1  Moving Average  886  2353 
2  MACD Histogram  273  255 
3  RSI  257  547 
4  Stochastics  196  331 
5  CCI  138  333 
6  Parabolic  129  140 
7  Bollinger Bands  67  146 
8  Larry Williams Percent Range  62  183 
9  Movement Directional Index  59  240 
10  Momentum Indicator  45  68 
The MACD is yet another attempt to solve the lagging problem of the moving average crossover. As has been noted with the simple moving average, and longer lengths, by the time you get a signal or crossover, the move may be almost over. To make them more sensitive to the current market, traders have developed exponential moving averages (EMA), which gives more weight to current prices. The MACD also uses the EMA in its calculations, as it compares the difference between a fast period EMA and a slower period EMA, with the standard periods being 12 and 26.
The MACD consists of three components:
 MACD line – the difference between the 12 and 26 period exponential moving average (EMA). Subtract the longer EMA (26) from the shorter EMA (12)
 Signal line – the 9 day EMA of the MACD line
 Block histogram – the difference between the MACD and the signal line
MACD line: The MACD line is the 12Period EMA minus the 26period EMA. If the MACD line is positive and rising, the rate of change between the 12 day and the 26 day is increasing. This is positive momentum and indicates a bullish period. If the MACD line is negative and falling, the short term indicator is falling faster than the long term and shows the market is going down.
Signal line: After the MACD line is determined, a 9period EMA of the MACD line inserted as the trigger and it is called the MACD signal line. When the MACD line goes above the MACD signal line, this is a bullish signal. A move below the MACD signal line is a sell signal.
Example: USDCHF daily chart.
As you can see, when the 12Period EMA crossed below the 26Period EMA, the MACD crossed below the Zero Line. This could have been a nice entry for a long run downwards. A better entry can be found just prior, when the MACD (blue line) crossed below the MACD Signal Line (red line). The histogram illustrates this crossover by indicating the a red histogram forming underneath the Zero line.
The MACD presents traders with three strategies:
Strategy #1: Crossing the 0 line
A move by the MACD line above the 0 line is a buy signal, while below is a sell signal. This is the same as the 12period EMA crossing over the 26 period EMA.
MT4 Indicators to Plot Buy/Sell Arrows on MACD Crossover of :
Indicator  Descriptor 

MA_Crossover_Signal.mq4  Blue Buy Arrow: when MACD Line Crosses Over 0. Red Buy Arrow: when MACD Line Crosses Under 0. 
Strategy #2: MACD crossing the MACD Signal Line
When the MACD crosses over the MACD signal line, this is known as a bullish crossover, and is a buy signal. When the MACD crosses MACD signal line, this is known as a bearish crossover, and is a sell signal. Note that the MACD crossover of the MACD Signal Line significantly reduces the lag found in the 1226 EMA crossover.
MT4 Indicators to Plot Buy/Sell Arrows of MACD Line Crossover of MACD Signal Line:
Indicator  Descriptor 

MACD_Crossover_Alert.mq4  Green Buy Arrow: MACD line crosses over the MACD Signal Line Red Sell Arrow: MACD line crosses under the MACD Signal Line. 
Strategy #3: The divergence between the MACD and price action.
Bullish Divergence occurs when the MACD is starting to turn up, making a higher low, but the underlying currency being analyzed is still making new lows. This Bullish divergence also occurs if the MACD is making a lower low, but the price is making a higher low. Sell signals occur when the opposite circumstances from those described above transpire
Here is a simple table to define the different divergences:
Name  Indicator / Price  Location 

1. Bullish Divergence Reversal  MACD is making a higher low / Price is making a lower low 
MACD Valleys 
2. Bearish Divergence Reversal  MACD is making a lower high/ Price is making a higher high 
MACD Peaks 
3. Bullish Divergence Continuation 
MACD is making a lower low / Price is making a higher low 
MACD Valleys 
4. Bearish Divergence Continuation 
MACD is making a higher high / Price is making lower high. 
MACD Peaks 
The bullish (or bearish) divergences are not common and often takes some study to detect but they can be detected faster with custom indicators developed below.
MT4 Indicators to Plot Buy/Sell Arrows on MACD Divergences:
Indicator  Descriptor 

Blue Buy Arrow: on bullish divergence. Red Buy Arrow: on bearish divergence. 
Conclusion
The MACD is a spin off the moving average in an attempt to reduce the problem of lag. The MACD crossing the 0 line (Strategy #1) is nothing more than the Fast (12) EMA crossing the Slow (26) EMA, and because it uses relatively shorter lengths along with the exponential calculation method, it strives to pick up the trend reversals faster than using longer lengths and simple (smoother) calculation methods. Lag is not entirely reduced, so the MACD crossing the Signal Line (Strategy #2) is an attempt to eliminate lag altogether. The MACD line (the difference between Fast and Slow EMA) crossing over the Signal Line (9EMA of MACD line) creates a hyper sensitive trend changer, so much so that it seems to anticipate the trend change and become a leading instead of lagging indicator. The MACD divergence strategy (Strategy #3) is yet another way to anticipate trend change before it occurs, and so followers of MACD prefer the last two strategies because they seem ahead of the curve.
But caution goes out to all usages of MACD: in striving to reduce lag and become more of a leading indicator, it courts the risk of noise, of entering into frequent false signals generated from the up and down (trendless) vacillations of the market. Because of this problem, the MACD can never be used as a standalone, stop and reverse system, as it will inevitably be chopped to pieces in sideways, directionless markets. Any backtest can quickly prove this point. MACD leading strategies can lead you into early trends sometimes, like the tip of the spear that points out the trend, but they can often lead you to the end of the spear the tip of which is hurtled back against your stop loss. Any attempt to try to lead or predict the markets will be punished with more noise and less reliability, for the chaos composition of Mr. Market will thwart any attempt to foretell its future intent.
The different MACD strategies can, however, be used as compliment conditions to for other indicator conditions, or even as act as an early trend change filter for an existing strategy. There are many robust EAs out there use the MACD, particularly strategy #2, as an additional condition or filter, so as to enter or exit the trade earlier than can be achieved from using moving averages alone.
You might have seen or heard about the the beautiful looking Ichimoku Kinko Hyo indicator (Ichimoku Kinkō Hyō, or, simply, Ichimoku), with its eyecatching moving averages and dynamically unfolding clouds.
The name translates from Japanese to “Equilibrium chart at a glance,” aptly describing how its five separate components come together to form a “whole” picture of price action to be seen “at a glance”. One simple look at an Ichimoku chart is said to provide its practitioners with an immediate understanding of sentiment, momentum and trend strength.
The background story is intriguing.
While an indicator is usually formulated by statisticians or mathematicians in the industry, this indicator was, strangely enough, developed before WWII by a Tokyo newspaper writer named Goichi Hosoda and a handful of assistants running multiple calculations.
After 20 years of testing, Mr. Hosoda finally released the system to the public in a book published in 1968. The indicator has been used extensively in Asian trading rooms since Hosoda published his book, though it did not make its appearance in the West until the 1990s.
The Ichimoku Kinko Hyo indicator consists of five main elements:
 Tenkansen (“turning line”): a 9period fast moving average based on HighLow difference rather than traditional Close levels,
 Kijunsen (“standard line”): a 26period slow moving average based on HighLow difference rather than traditional Close levels,
 Senkou Span A (“1st leading line”): average of Tenkansen and Kijunsen plotted with some shift in the future,
 Senkou Span B (“2nd leading line”); average of maximum and minimum price for the given period plotted with the same shift in the future,
 Chinkou Span (“lagging line”): price Close level plotted with the same shift but in the past.
Senkou Span A and B together form what is known as Kumo cloud.
The chart below shows the five elements in their complete setup:
Apparently, there are different ways to trade with Ichimoku Kinko Hyo: one can trade the Tenkan/Kijun cross, like the MACD, or trade the Kumo cross, or trade the cross of price with any of Ichimoku’s five lines.
For the longest time, the strategies were traded manually, but I’m a trader who desires proof of a system’s profitability in the form of backtests, and so I combed the web for different versions of the EA – free and commercial – and below are the ones I found. I backtested the EAs below on two pairs, USDJPY and EURJPY, on H4 time frame, from January 01 2008 to September 27 2013.
EA Name  Source  Logic  Backest: USDJPY@H4 (Jan012008toSept272013) 


Ichimoku Kumo Breakout EA (Elite Section) 
tradingsystemforex.com  – Buy when closing price crosses up the cloud (with shift option and minimum distance between closing and the cloud); Sell when closing price crosses down the cloud (with shift option and minimum distance between closing and the cloud); 
7854 (2220 DD), PF 1.63, 285 trades (33% Win) ★★★★☆ 

Ichimoku EA v1.02 & v.1.04 (Elite Section) 
tradingsystemforex.com  Works on 5 strategies in combination: For long signal: 1) Tenkan Sen / Kijun Sen Cross 2) Price is greater than cloud (spanA and spanB) 3) Tenkan > cloud 4) Kijun > cloud 5) Chikou > cloudopposite for sell signal 
v1.04 ★★★★☆ 

Ichimoku EA v1.3 (free) 
lifesdream.org  Works on 3 Strategies, singularly or together: 1) Tenkan Sen / Kijun Sen Cross 2) Kijun Sen Cross 3) Kumo Breakout 
738 (3664 DD), PF:1.03, 493 trades (50% Win) ★☆☆☆☆ 

Ichimoku 5.3.3 (free) 
forextsd.com  Tenkan Sen / 34EMA Cross + Tenkan Sen / Kijun Sen Confirmation + 40/40 trail stop 
1760 (800 DD), PF:1.56, 118 trades (74% Win) ★★★★☆ 

Ichimoku Chinkou Cross (free) 
earnforex.com  Buys (sell) when Chinkou crosses over (under) the close + confirm long (short) if close above (below) cloud + confirm long (short) if Chinkou above (below) cloud 
1060 (2076 DD), PF: 1.09, 373 trades (35% Win) ★☆☆☆☆ 

Ichimoku EA v1.4 (free) 
forexfactory.com  Works on 4 Strategies, singularly or together, with lots of options: 1) Kijun Signals 2) Tenkan Signals 3) Senkou Signals 4) Kumo Width Filter 
couldn’t get EA to backtest properly  
Ichimoku Power EA ($79) 
ichimokupower.com  Customizable to trade in multiple ways  N/A  
Ichimoku Cloud (297) 
ichimokucloud.com  Customizable to trade in multiple ways  N/A 
As you can see, there have been at least 8 attempts to create different EA versions of Ichimoku system, four of which are free and open to the public, two downloadable from tradingsystemforex’s elite section, and two that are commercial.
There was hardly a consistent and rigorous method of providing backtesting for any of the above EAs when they were first developed by their authors. At best the EA was backtested on one instrument over a period of 1 year, which is scant evidence of the EA’s reliability. At worst – like the commercial versions – there was no attempt to provide a backtest, an omission which always make me suspicious.
I had to find and download the latest versions of the above EAs and conduct my own backtests on two pairs, USDJPY and EURJPY, on H4 time frame, from Jan 01, 2008 to Sept 26, 2013, observing that those two pairs and that timeframe works particularly well for that system.
Again, and as you can see from the results above, there are some promising EAs based on Ichimoku, such as the two EAs from tradingsystemforex’s elite section (IchimokuKumo Breakout and Ichimoku EA v1.02/1.04), and the Ichimoku 5.3.3 EA from forextsd.
I am going to come give you yet one more version of Ichimoku EA that should help you in your trading.
Signal Condition = Buy (sell) If current Chinko is above (below) the close and previous Chinkou was below (above)
Confirmation Condition1 = Buy (sell) if close is is above (below) both Senkou Span A and Senkou Span B
Confirmation Condition2 = Buy (sell) if Chinkou span is above (below)
Conclusion
For decades the Ichimoku Kinko Hyo system had been a manual system meant for the trader to see at a glance the interaction and balance of each of its five main indicator lines in order to capture medium term trends.
But the problem with manual systems is that they hard to quantify: one cannot know for sure how well they have done in the past, or how well they might work out in the future. Moreover, the Ichimuku system has many variations on how to trade it, from taking a crossover or confirmation from any or all of the five line elements, and it is hard to know for sure which variation works the best.
However, in the last few years, there has been scattered attempts to build EAs of the different variations, though the thoroughness of the backtesting left much to be desired.
I have dug up eight EAs that have tried to tackle the Ichimoku system in different ways, and when I conducted backtests on USDJPY and EURJPY on the H4 time frame, I discovered there was some interesting EAs that had made some noteworthy profits in the last 6 years. These are nice to add to one’s arsenal.
I also created one additional variant of the Ichimoku EA – playing off the Chinkou crossover, and hopefully that EA can help you as well.
I give a deep bow to Mr. Hosoda for being the Clark Kent of his time – being the newspaper man during the day and the “superman” strategy developer at night (albeit helped by a few assistants). He accomplished an incredible feat from observation and manual testing. Now that we have computers and strategy testers loaded with 10 years of historical data, we can see that the system he gave us over 40 years ago is still very much profitable today – or at least some particular variations of the system on particular pairs and timeframes.