# SMA and EMA Crossover: Moving Average Trading Strategies

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Perhaps one of the simplest trading strategies of all is that of the moving average crossover. Simple and exponential crossover strategies have a wide variety of uses. They are straightforward yet can be surprisingly effective. Crossover is best suited to trend following and momentum strategies.

A number of sophisticated trading systems use crossover as their underlying approach. Taking the basic idea, they refine it to improve its reliability and to lessen the problem of false buy/sell signals and latency.

## How Do Crossover Strategies Work?

The principle behind the crossover system is that it tells us a trend is changing.

To understand this, take a look at the four charts in Figure 1.

Figure 1: Process of moving average crossover

At time t+1 there are three straight lines, the price, the moving average 1 (MA 1) and the moving average 2 (MA 2). At time t+2, the price line starts to fall. Notice that the blue line (MA 1) starts to fall next after the price line. Meanwhile at this time the red line (MA 2) is still rising.

At time t+3, all three lines are falling. The price, moving average 1 and moving average 2 are now all on the same path again after the price changed direction from rising to falling.

Finally at time t+4, the price starts to rise again and the same process happens again but in reverse. The blue line is the first to react to the price turning followed by the red line.

The length of time that the moving average uses in its calculation is the period. Moving average 1, the blue line, is a fast moving average because it uses fewer data points, or a shorter time period in its calculation. Moving average 2, the red is a slow moving average because it takes a larger sample of points and therefore has a slower reaction time to changes in price.

The longer the period of the average, the more stable the line is but the slower it is to react to changes.

### Crossover Events

A crossover occurs when two different moving average lines cross over one another. In the chart above, time t+2, and t+3, show a bearish crossover. This takes place when a fast moving average crosses down through a slow moving average. This implies that the trend is falling or becoming bearish.

At time t+4, the trend changes again and this produces a bullish crossover. The blue, or fast moving average, is the first to react. It crosses up through the slow line. After the crossing, all three lines then follow the same path as the trend continues upwards.

Some might say why not just look at the price, because that tells us instantaneously what’s happening. The reason for using the averages rather than the price is that in real markets, trends do not move in straight lines but rather follow meandering paths with many false stops and starts.

The purpose of the average lines is to smooth out the random movements and discover the underlying price trends.

## Which Averages Should You Use?

The first and most basic problem that a crossover trader faces is which moving average pair to use.

In hindsight we can always back test a market to find some unique moving average pair that will create a profitable strategy. Yet this is of little use because as we all know the past is not necessarily a reliable predictor of the future.

### The Death Cross and the Golden Cross

Perhaps the most common pairing is the 50-day verses the 200-day moving average. When the 50-day cross up through the 200-day moving average this is said to be a golden cross. It signifies to many the possibility of a new bull market.

On the flip side, when the 50-day moving average crosses down through the 200-day moving average this is said to be a death cross. It’s a sign of a market that’s turning bearish.

### Simple vs exponential moving averages

The other decision is in the formula that is used to average prices over time. Simple moving averages apply the same weighting to all prices.

On the other hand, exponential moving averages apply higher weighting to more recent prices and lower weighting to price further back in time.

Exponential moving averages would on the face of it seem to be better suited because they will be more responsive to recent price changes. But at the same time this also makes them more responsive to extreme and sudden price moves. This may or may not be a good thing, depending on the market conditions.

Typically, in SMA and EMA the price taken at each interval is the mid-price. Yet this can change and sometimes the open or close price is preferred.

## A Basic MA Crossover Strategy

The most basic moving average crossover strategy is as follows:

Buy when fast line crosses up through slow line
Set stop loss below slow moving average support line

Sell side entry:
Sell when fast line crosses down through slow line
Set stop loss above slow moving average resistance line

## SMA Strategy Results

We tested the SMA-50-200 strategy on five of the principal currency pairs, EURUSD, USDJPY, GBPUSD, USDCHF and USDCAD. The test was done using the past ten years’ worth of historical chart data using  the hourly period chart (H1).

In the test, a bearish crossing generates a sell signal and a bullish crossing generates a buy signal. We measured the market reaction from the earliest possible entry time over the following 30 time intervals (30 hours for the H1 chart).

Using the principle of equal risk and return we categorized each trade entry as correct or incorrect depending on the direction the market took, taking into account drawdown periods.

The table below summarizes the results of the test.

 SMA-50-200 (H1) EURUSD USDJPY GBPUSD USDCHF USDCAD Total Bearish crosses 238 249 260 232 245 1224 Percentage correct 53.8% 49.8% 49.2% 54.3% 53.1% 52.0% Bullish crosses 238 248 261 230 245 1222 Percentage correct 52.5% 45.6% 51.0% 58.7% 55.1% 52.5%

There were a total of 2446 crossover events, of which 1224 were bearish and 1222 were bullish.

As shown in the table, when following the SMA strategy, 52.5% of the bullish crossovers were correct and 52% of the bearish crossovers where correct.

Over this time frame then, our simple SMA strategy produced a result slightly better than expected by chance alone.

On the daily chart, the results were comparable, though the number of crossings is less than the hourly test.

 SMA-50-200 (D1) EURUSD USDJPY GBPUSD USDCHF USDCAD Total Bearish crosses 20 24 27 27 23 121 Percentage correct 45.0% 58.3% 40.7% 59.3% 52.2% 51.2% Bullish crosses 20 24 27 27 22 120 Percentage correct 45.0% 58.3% 59.3% 51.9% 50.0% 53.3%

## EMA Strategy Results

We performed the same test using the EMA-50-200 strategy. The results are summarized in the table below.

 EMA-50-200 (H1) EURUSD USDJPY GBPUSD USDCHF USDCAD Total Bearish crosses 226 255 261 250 256 1248 Percentage correct 54.0% 56.1% 51.0% 46.8% 48.4% 51.2% Bullish crosses 228 252 259 250 255 1244 Percentage correct 53.1% 38.1% 49.8% 51.2% 57.7% 49.9%

In our tests, the EMA proved to be less successful than SMA. The EMA strategy produced 51.2% correct bearish entries, but only 49.9% correct bullish entries. This result is no better than random.

The EMA strategy performed much better on the daily chart.

 EMA-50-200 (D1) EURUSD USDJPY GBPUSD USDCHF USDCAD Total Bearish crosses 19 19 25 25 20 108 Percentage correct 47.4% 57.9% 52.0% 60.0% 50.0% 53.7% Bullish crosses 20 19 25 24 19 107 Percentage correct 60.0% 63.2% 64.0% 41.7% 57.9% 57.0%

The bullish crossovers were correct 57% of the time, and the bearish crossings were correct 53.7% of the time. Though given the low number of crossings that can be analyzed on the daily chart, we have to be slightly cautious with these results.

## False Crossover

One of the complications all crossover strategies need to deal with is that of false crossover signals.  False crossings or false crossovers happen when the moving average lines cross one another briefly, but then revert back again.

Figure 2: Problem of false crossovers

On the chart this means that the trend only changes direction for a short time but then continues in the same direction. Figure 2 shows an example for USDCAD – United States dollar versus Canadian dollar.

Here with USDCAD, a dip in the bullish trend is enough to generate a crossing of the 50-200 day moving average.

However, the market turns bullish again and shortly after the false sell signal, a new buy signal is generated. This would almost certainly have resulted in a loss on the short positions, though this could have been recouped if the subsequent buy signal was acted on.

## The Latency Conundrum

The other issue faced by crossover strategies, and most technical strategies in general, is that of latency. Latency is the delay and the consequence of it is that by the time the signal is triggered, the market has already turned again.

Figure 3: The latency conundrum

The chart in Figure 3 shows USDCAD daily again. Here the buy signal appears after a strong bullish breakout. The crossover duly takes place however by the time the crossover triggers the buy signal, the bullish move is nearly over.

By following the strategy, the buyer would enter at no lower than 1.0279, but the market peaks 7 days later at 1.0658. Then the trend turns bearish again and soon falls well below the buy entry level.

## Double and Triple EMA Systems

There are a few ways to overcome or alleviate the above weaknesses in the crossover strategy. One is to use a position accumulation method. With this, the position size is increased or decreased according to risk.

In this way, the position is only increased as and when a trend develops in strength. False signals are still produced, but they are exited quicker so the amount risked on each new trend is lower. This is one of the cornerstones of the Turtle Trading System. The Turtle however uses a shorter moving average pair.

The thinking behind accumulation is that false signals can’t be eliminated altogether but instead we can increase or decrease the risk according to a trend’s longevity.

Alternatively, there are enhanced crossover strategies that try to alleviate some of these problems. The Triple EMA and Double EMA are two such examples.

## Summary

Crossover strategies are simple yet as we’ve shown can be profitable under the right market conditions. This important class of strategy is often the foundation of more advanced trading systems such as trend followers.

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