Moving averages are a popular technique among active traders. When markets consolidate, however, this indication causes a slew of whipsaw transactions, resulting in a frustrating succession of modest profits and losses. Analysts have worked for decades to enhance the simple moving average. In this essay, we examine their efforts and discover that their quest has resulted in beneficial trading tools. (For more information on simple moving averages, see Simple Moving Averages Make Trends Stand Out.)
The Benefits and Drawbacks of Moving Averages In the first edition of Technical Analysis of Stock Trends, Robert Edwards and John Magee summarized the benefits and drawbacks of moving averages, saying “And, in 1941, we delighted in discovering (though many others had done so before) that by averaging the data for a specified number of days…one could derive a sort of automated trendline that would definitely interpret the changes in trend… It nearly appeared too wonderful to be true. In fact, everything seemed too wonderful to be true.”
Edwards and Magee rapidly abandoned their goal of trading from a beach cottage since the problems outweighed the benefits. But, 60 years later, others are still looking for a simple instrument that would readily provide the riches of the markets.
Simple Moving Averages (SMAs) Add the prices for the relevant time period and divide by the number of periods chosen to produce a simple moving average. To calculate a five-day moving average, add the five most recent closing prices and divide by five.
- If the most recent close is higher than the moving average, the stock is said to be in an uptrend.
- Prices trading below the moving average indicate a downtrend. (For additional information, read our Moving Averages lesson.)
Moving averages may provide trading signals because of this trend-defining characteristic. In its most basic form, traders purchase when prices rise above the moving average and sell when prices fall below it. A strategy like this is sure to put the trader on the winning side of every important deal. Unfortunately, moving averages will lag behind market movement while smoothing the data, and the trader will almost always give up a considerable portion of their earnings on even the most profitable deals.
Moving Averages, Exponential Analysts seem to favor the moving average concept and have spent years attempting to lessen the issues associated with this lag. The exponential moving average is one of these developments (EMA).This method gives current data a larger weighting, which keeps it closer to the price movement than a simple moving average. The formula for calculating an exponential moving average is as follows:
EMA = ( Weight Close ) + ( ( 1 Weight ) EMAy) where: Weight = the smoothing constant chosen by the analyst beginaligned&textEMA=(textWeighttimestextClose)+((1-textWeight)timestextEMAy)&textbfwhere:&textWeight=textthe smoothing constant chosen by the analyst&textEMAy=textthe exponential moving average from yesterdayendaligned
A typical weighting value is 0.181, which is close to a simple moving average of 20 days. Another value is 0.10, which corresponds to a 10-day moving average.
Although it minimizes latency, the exponential moving average does not solve another issue with moving averages: its usage as trading signals results in a significant proportion of lost transactions. Welles Wilder estimates that markets only trend one-quarter of the time in New Concepts in Technical Trading Systems. Moving-average buy-and-sell signals are created regularly when prices quickly move above and below the moving average, accounting for up to 75% of trading activity. Several experts have proposed altering the weighting element of the EMA computation to overcome this issue. (For additional information, read How do moving averages work in trading?)
Moving Averages and Market Movement To overcome the shortcomings of moving averages, increase the weighting element by a volatility ratio. In turbulent markets, this would imply that the moving average would be farther away from the present price. Winners would be able to run as a result of this. As a trend ends and prices consolidate, the moving average will move closer to the present market activity, allowing the trader to maintain the majority of the gains made during the trend. In practice, the volatility ratio may be a tool like the Bollinger Band®width, which quantifies the distance between the well-known Bollinger Bands®. (For additional information on this indicator, read The Fundamentals Of Bollinger Bands®.)
In his book, New Trading Systems and Methods, Perry Kaufman proposed replacing the “weight” component in the EMA calculation with a constant based on the efficiency ratio (ER). This indicator is intended to quantify the strength of a trend across a range of -1.0 to +1.0. It is computed using the following formula:
ER = total price change for period sum of absolute price changes for each bar where: beginaligned &textER = fractexttotal price change during the time textsum of absolute price changes for each bar&textbfwhere:&textER = textefficiency ratioendaligned&textbf
Consider a stock that has a five-point range each day and has gained a total of 15 points after five days. This would have an ER of 0.67. (15 points upward movement divided by the total 25-point range).The ER would be -0.67 if this stock fell 15 points. (For additional trading tips from Perry Kaufman, see Losing To Win, which explains coping tactics for trading losses.)
The effectiveness of a trend is determined by how much directional movement (or trend) you obtain per unit of price fluctuation over a certain time period. An ER of +1.0 implies that the stock is in a perfect uptrend, while -1.0 suggests that it is in a perfect decline. In practice, the extremes are seldom attained.
To use this indication to determine the adaptive moving average (AMA), traders must first calculate the weight using the somewhat difficult formula:[ ( ER ( SCF SCS ) ) + SCS ] C = [ ( ER ( SCF SCS ) ] 2where: SCF = the exponential constant for the quickest allowed EMA (usually2)SCS = the exponential constant for the slowest EMA permitted (typically 30) beginaligned&textC = [(textER times (textSCF – textSCS)) + textSCS] 2&textbfwhere:&textSCF = textthe exponential constant for the fastest&qquadquadtext EMA permitted (typically 2)&textSCS = textthe exponential constant for the slowest&qquadquadtext EMA allowable (frequently 30)&textER = textthe efficiency ratio that was mentioned aboveendaligned
The value for C is then substituted for the simpler weight variable in the EMA calculation. Although difficult to compute by hand, practically all trading software programs feature an adaptive moving average option. (Read Exploring the Exponentially Weighted Moving Average for additional information on the EMA.)
Figure 1 shows examples of a basic moving average (red line), an exponential moving average (blue line), and an adaptive moving average (green line).
Figure 1: The AMA is in green and displays the most flattening in the range-bound activity on the right side of this chart. In most situations, the blue line represents the exponential moving average, which is closer to the price movement. The red line is the simple moving average.
The three moving averages shown in the graphic are all susceptible to whipsaw trading at different periods. This disadvantage of moving averages has so far proven insurmountable.
Conclusion In The Encyclopedia of Technical Market Indicators, Robert Colby examined hundreds of technical-analysis instruments. “Although the adaptive moving average is an intriguing novel notion with significant intellectual appeal,” he said, “our early testing fail to reveal any clear practical benefit to this more sophisticated trend smoothing approach.” This is not to say that traders should dismiss the concept. The AMA might be used in conjunction with other indicators to create a winning trading strategy. (For additional more on this subject, see Discovering Keltner Channels And The Chaikin Oscillator.)
The ER may be utilized independently as a trend indicator to identify the most successful trading opportunities. As an example, ratios greater than 0.30 imply strong uptrends and probable buys. Alternatively, since volatility swings in cycles, the companies with the lowest efficiency ratio should be studied for potential breakouts.
For more, see Basics Of Weighted Moving Averages.
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