Moving averages

Moving averages smooth the price data to establish a trend following indicator. They do not predict price direction, but to define the current direction with the delay. Moving averages are lagging behind because they are based on recent prices. Despite this lag, moving averages help smooth price action and filter noise. They also form the building blocks for many other technical indicators and overlays, such as Bollinger Bands, MACD and the McClellan Oscillator. The two most popular types of moving averages are the Simple Moving Average (SMA) and exponential moving average (EMA). These moving averages can be used to identify the direction of trends or define potential support and resistance levels.
Calculation SMA

A simple moving average is formed by calculating the average cost of security over a specified number of periods. Most moving averages are based on closing prices. A 5-day simple moving average is five days a sum of closing prices divided by five. As the name implies, the moving average on average it moves. The old data is dropped as new data comes available. This causes the average to move along the time scale. Below is an example of a 5-day moving average is developed over three days.

Daily Closing Prices: 11,12,13,14,15,16,17

First day on day 5-SMA: (11 + 12 + 13 + 14 + 15) / 5 = 13

Second day of 5 day-SMA (12 + 13 + 14 + 15 + 16) / 5 = 14

Third day on day 5-SMA: (13 + 14 + 15 + 16 + 17) / 5 = 15

On the first day moving average just covers the last five days. The second day moving average drops first data point (11) and adds the new data point (16). On the third day moving average continues to decline the first data point (12) and adding new data point (17). In the example above, the price gradually increases from 11 to 17 during the seven days. Note also that average rises ranging from 13 to 15 in three days during the accounting period. Also note that any moving average value is just below the last price. For example, moving average for one day is 13 and the final price is 15. Prices of previous four days were lower and this causes the moving average to lag.
Calculating EMA

Exponential moving averages reduce the lag by applying more weight to recent prices. The weight to apply the latest price depends on the number of periods in the moving average. There are three steps for calculating the exponential moving average. First, calculate the simple moving average. Exponential moving average (EMA) is to start somewhere so simple moving average is used as EMA in the first period of the previous calculation. Second, calculate the weight of the multiplier. Thirdly, calculating the exponential moving average. The formula below is for 10-day EMA.

SMA: 10 period sum / 10

Multiplier: (2 / (Time periods + 1)) = (2 / (10 + 1)) = 0.1818 (18.18%)

EMA: {Close - EMA (previous day)} + x multiplier EMA (previous day).


A 10-period exponential moving average of 18.18% applies weight to the latest price. A 10-period EMA, may also be called 18.18% EMA. A 20-period EMA refers to the measurement of 9.52% latest price (2 / (20 +1) = .0952). Note that weight for a shorter time period is over weight for a longer period of time. In fact, the weight drops by half every time the moving average period doubles.

Below is a table example of a 10-day simple moving average and 10-day exponential moving average for Intel. Simple moving averages are straightforward and require little explanation. The 10-day simple moving average as the new prices will become available and old prices drop. Exponential moving average starts with a simple moving average (22.22) in the first calculation. After the first calculation formula normally take. Because the EMA starts with a simple moving average, its real value will be realized up to 20 times or so later. In other words, the value of the table into Excel may differ from the table value as a short look back period. This chart only goes back 30 times, which means that the influence of a simple moving average has 20 times for breaking up. Stockcharts.com 250-back periods for its calculations, so the effects of a simple moving average in the first calculation are fully used.

The moving average - Table

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The moving average - Table 1
The Lag Factor

The longer the moving average, the more lag. A 10-day exponential moving average price will embrace very carefully and turn quickly turn prices. Short moving averages like speed boats - nimble and quick to change. In contrast, the 100-day moving average contains many data which slows down the past. Longer moving averages are like ocean tankers - lethargic and slow to change. It takes larger and larger price movement of 100-day moving average to change course. Table 2 shows the S & P 500 ETF with a 10-day EMA carefully monitor prices and 100-day SMA grind higher. Even with the decline from January to February, the 100-day SMA held a course and do not turn down. On day 50-SMA fits somewhere between 10 and 100 day moving average when it comes to lag factor.

The moving average - Chart 2
Simple vs. Exponential

Although there are clear differences between the simple moving average and exponential moving average, is not always better than others. Exponential moving averages are lagging behind and therefore less sensitive to recent prices - and recent changes in prices. Exponential moving averages will be converted to simple moving averages. Simple moving averages, on the other hand, represent a true average of prices for the entire period. As such, the simple moving averages may be better suited to identify support or resistance levels.

Moving average depends on the choice of the objectives, analytical style and time horizon. Chartists should experiment with both types of moving averages, as well as different deadlines to find the best fit. Table 2 shows IBM, with 50 day-SMA in red, and 50-day EMA in green. Both peaked in late January but the fall in the EMA is sharper than the fall of the SMA. EMA appeared in mid-February, but SMA extended below the end of March. Note that the SMA appeared in over a month after the EMA.

The moving average - Figure 3
Lengths and dynamics

The length of the moving average depends on the analytical objectives. Short moving averages (5-20 times) are best suited for short-term trends and trade. Chartists interested midterm trends will opt for longer moving averages that can be expanded from 20 to 60 periods. Long-term investors will want the moving average with 100 or more periods.

Some moving average lengths are more popular than others. The 200-day moving average is perhaps the most popular. Due to its length, this is clearly a long-term moving average. Next, the 50-day moving average is quite popular for medium-term trend. Many chartists use the 50-day and 200-day moving averages together. Short term, the 10-day moving average was quite popular in the past because it was easy to calculate. One simply add numbers and moved the decimal point.
Identify trend

The same signals can be generated using simple or exponential moving averages. As noted above, the choice depends on each individual. These examples below will use both simple and exponential moving averages. The term "moving average" refers to the simple and exponential moving averages.

The direction of average motion carries important information about prices. A rising moving average shows that prices generally rise. A falling moving average indicates that prices, on average, are falling. A rising long-term moving average reflects the long-term uptrend. A falling long-term moving average reflects the long-term downtrend.

The moving average - Table 4

Table 4 shows the 3M (MMM), a 150-day exponential moving average. This example shows just how well moving averages work when the trend is strong. A 150 day EMA rejected in November 2007 and again in January 2008. We note that this 15% decline to reverse the direction of this moving average. These lagging indicators identify trend reversals as they occur (at best) or after they occur (at worst). You know continue lower in March 2009, then increased 40-50%. Note that the 150-day EMA did not turn up until after this wave. Once I do that, however, continued higher You know the next 12 months. Moving averages work brilliantly in strong trends.
Double crossovers

Two moving averages can be used together to generate crossover signals. In a technical analysis of financial markets, John Murphy calls this "double-crossover method. Double crossovers involve a relatively short moving average and a relatively long moving average. As with all moving averages, the general length of the moving average defined time frame for the system. A system using a 5-day EMA and 35-day EMA will be considered short term. A system using 50-SMA and 200 day-SMA day is considered the medium term, perhaps even the long term.

A bullish crossover occurs when the shorter moving average crosses above a longer moving average. This is also known as the golden cross. A bullish crossover occurs when the shorter moving average crosses below the longer moving average. This is known as a dead cross.

Moving average crossovers produce relatively late signals. Anyway, the system has two trailing indicators. The longer the moving average periods, the greater the delay in signals. These signals big deal when a trend is a good place. However, moving average crossover system will produce very whipsaws in the absence of a strong trend.

There is also a triple crossover method which consists of three moving averages. Again, a signal is generated when the shortest moving average crosses over two moving averages. Simple triple crossover system may include 5-day, 10-day and 20-day moving average.

The moving average - Graph 5

Figure 5 shows Home Depot (HD), a 10-day EMA (green dotted line) and 50-day EMA (red line). The black line is the daily close. Using a moving average crossover would result in three whipsaws before catching a good trade. The 10-day EMA break below the 50-day EMA at the end of October (1), but it did not last long, as 10-day back up in mid-November (2). This cross lasted longer, but the next bearish crossover in January (3) occurred near the end of November price levels, resulting in another whipsaw. This bearish cross did not last long, as 10-day EMA is back above 50-day several days later (4). After three bad signals, the fourth signal predicted a strong move as the stock advanced more than 20%.

There are two takeaways here. First, crossovers are prone to whipsaw. The price or time filter can be applied to prevent whipsaws. Traders may require a crossover of the last 3 days before acting or ask for a 10-day EMA to move above / below 50 day EMA with a certain amount before you act. Secondly, MACD can be used to identify and quantify these crossovers. MACD (10,50,1), will represent the difference between two exponential moving averages. MACD turns positive during a golden cross and negative during the dead cross. Percentage Price Oscillator (PPO) can be used in the same way to show the percentage differences. Note that the MACD and PPO are based on exponential moving average and will coincide with a simple moving average.

The moving average - Graph 6

Figure 6 shows Oracle (ORCL) with a 50-day EMA, 200-day EMA and MACD (50,200,1). There were four moving average crossovers about 2 1 / 2 years. The first three resulted in whipsaws or bad trades. A steady trend started as a fourth crossover ORCL advanced to the mid-20th Once again, moving average crossovers big deal when the trend is strong, but produces losses in the absence of trend.
Price crossovers

The moving average also can be used to generate signals with simple price crossovers. A bullish signal is generated when prices move above the moving average. A bearish signal is generated when prices move below the moving average. Price crossovers can be combined to trade within a larger trend. The longer the moving average sets the tone for a larger trend and the shorter moving average is used to generate signals. One will look for the bullish price crosses only when prices are already above the long moving average. This will be trading in harmony with the increasing trend. For example, if the price is above the 200-day moving average, chartists will only focus on alerts when the price moves above the 50-day moving average. Obviously, a step below the 50-day moving average would precede such a signal, but such a bearish cross will be taken into account, since larger trend is up. A bearish cross would simply propose the withdrawal within a larger uptrend. A cross back over the 50-day moving average would signal a reversal in prices and the continuation of major uptrend.

Figure 7 shows Emerson Electric (EMR) with a 50-day EMA and 200-day EMA. The stock moved above and held over 200-day moving average in August. There were dips below 50-day EMA in early November and again in early February. Prices quickly back over the 50-day EMA to provide bullish signals (green arrow) in harmony with the larger uptrend. MACD (1,50,1) is displayed in the window to confirm the price indicator crosses above or below 50-day EMA. The 1-day EMA is equal to the closing price. MACD (1,50,1) is positive when close above 50-day EMA and negative when in close under the 50-day EMA.

The moving average - Graph 7
Support and resistance

The moving average also can act as support in the uptrend and resistance in a downtrend. A short term uptrend can find support near the 20-day simple moving average, which is also used in Bollinger bands. The long-term uptrend can find support near the 200-day simple moving average, which is the most popular long-term moving average. If fact, the 200-day moving average may offer support or resistance simply because it is so widely used. It's almost like a self-fulfilling prophecy.

The moving average - Graph 8

Graph 8 shows the New York Composite 200-day simple moving average since mid-2004 until the end of 2008. The 200-day support provided countless times during the advance. Since the opposite trend with a double top support a pause, the 200-day moving average acted as resistance around the 9500th

Do not expect total support and resistance levels of the moving averages, especially in the moving average. Markets are driven by emotions, making them prone to overshoots. Instead of true levels, moving averages can be used to identify support or resistance zones.
Conclusions

The advantages of using moving averages should be weighed against the disadvantages. Moving averages are trend following, or lagging, indicators that will always be a step behind. This is not necessarily a bad thing though. Anyway, the trend is your friend and it is best to trade in the direction of the trend. Moving averages ensure that the trader is in line with the current trend. Although this trend is your friend, securities spend a lot of time in trading ranges, which render moving averages ineffective. Once the trend, moving averages will keep you in, but also give late signals. Do not expect to sell at the top and bottom purchase using moving averages. As with most technical analysis tools, moving averages should not be used on their own, but combined with other complementary tools. Chartists can use moving averages to define the overall trend and then use the RSI to define overbought or oversold levels.
Mas and SharpCharts

Moving averages are available as cost overlay feature SharpCharts. The overlay option price, users can choose either a simple moving average or exponential moving averages. The first parameter is used to set the number of periods. The second optional parameter can be added to shift the moving average of the left (past) or right (future). Negative numbers (-10) will shift the moving average from the left 10 times. A positive number (10) will shift the moving average of the right 10 times. Use a comma to separate the moving average parameter shift parameter.