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Technical Studies
Technical analysis has witnessed the development of a large number of technical studies (or technical "indicators") over the past several years. Here is a short description of some of the more popular studies.
Bollinger Bands
Developed by John Bollinger, Bollinger Bands are an indicator that allows users to compare volatility and relative prices levels over a period of time. The indicator consists of three bands designed to encompass the majority of a currency's price action.
A simple moving average ("SMA") in the middle
An upper band (SMA plus 2 standard deviations)
A lower band (SMA minus 2 standard deviations)
Standard deviation is a statistical term that provides a good indication of volatility. Using the standard deviation ensures that the bands will react quickly to price movements and reflect periods of high and low volatility. Sharp increases or decreases in prices, and hence volatility, will lead to a widening of the bands. Long periods of sideways movements will lead to a narrowing. Bollinger Bands are designed to capture the majority of price movement. When prices move beyond the upper or lower band, they are considered high (overbought) or low (oversold) on a relative basis.
Moving Average
Moving Averages are one of the most popular and easy to use tools available to the technical analyst. By using an average of prices, moving averages smooth a data series and make it easier to spot trends. This can be especially helpful in volatile markets.
A moving average (MA) is an average of data for a certain number of time periods. It "moves" because for each calculation, we use the latest x number of time periods' data. There are two major types of Moving Averages: "Simple" and "Exponential".
Simple Moving Average
A simple moving average (SMA) is formed by finding the average price of a currency or commodity over a set number of periods. Most often, the closing price is used to compute the moving average. For example: a 5-day moving average would be calculated by adding the closing prices for the last 5 days and dividing the total by 5.
A moving average moves because as the newest period is added, the oldest period is dropped. If the next closing price in the average is 15, then this new period would be added and the oldest day, which is 10, would be dropped. The new 5-day moving average would be calculated as follows:
Over the last 2 days, the moving average moved from 12 to 13. As new days are added, the old days will be subtracted and the moving average will continue to move over time. Moving averages are lagging indicators and will always be behind the price. Because moving averages are lagging indicators, they fit in the category of trend following. When prices are trending, moving averages work well. However, when prices are not trending, moving averages do not work
Exponential Moving Average
In order to reduce the lag in simple moving averages, technicians sometimes use exponential moving averages, or exponentially weighted moving averages. Exponential moving averages reduce the lag by applying more weight to recent prices relative to older prices. The weighting applied to the most recent price depends on the length of the moving average. The shorter the exponential moving average is, the more weight that will be applied to the most recent price. For example: a 10-period exponential moving average weighs the most recent price 18.18% and a 20-period exponential moving average weighs the most recent price 9.52%. The method for calculating the exponential moving average is fairly complicated. The important thing to remember is that the exponential moving average puts more weight on recent prices. As such, it will react quicker to recent price changes than a simple moving average. For those who wish to see an example formula for an exponential moving average, one is provided below. Others may prefer to skip this section and move on the comparison of the moving averages.
Exponential Moving Average Calculation
The formula for an exponential moving average is:
X = (K x (C - P)) + P
X = Current EMA
C = Current Price
P = Previous period's EMA*
K = Smoothing constant
(*A SMA is used for first period's calculation)
The smoothing constant applies the appropriate weighting to the most recent price relative to the previous exponential moving average. The formula for the smoothing constant is:
K = 2/(1+N)
N = Number of periods for EMA
For a 10-period EMA, the smoothing constant would be .1818.
The EMA formula works by weighting the difference between the current period's price and the previous period's EMA and adding the result to the previous period's EMA. There are two possible outcomes: the weighted difference is either positive or negative.
If the current price (C) is higher than the previous period's EMA (P), the difference will be positive (C - P). The positive difference is weighted by multiplying it by the constant ((C - P) x K) and the answer is added to the previous period's EMA, resulting in a new EMA that is higher ((C - P) x K) + P.
If the current price is lower than the previous period's EMA, the difference will be negative (C - P). The negative difference is weighted by multiplying it by the constant ((C - P) x K) and the final result is added to the previous period's EMA, resulting in a new EMA that is lower ((C - P) x K) + P.
Parabolic SAR
Developed by Welles Wilder, creator of RSI and DMI, the Parabolic SAR sets trailing price stops for long or short positions. Also referred to as the stop-and-reversal indicator (SAR stands for "stop and reversal"), Parabolic SAR is more popular for setting stops than for establishing direction or trend. Wilder recommended establishing the trend first, and then trading with Parabolic SAR in the direction of the trend. If the trend is up, buy when the indicator moves below the price. If the trend is down, sell when the indicator moves above the price.
The formula is quite complex and beyond the scope of this definition, but interpretation is relatively straightforward. The dotted lines below the price establish the trailing stop for a long position and the lines above establish the trailing stop for a short position. At the beginning of the move, the Parabolic SAR will provide a greater cushion between the price and the trailing stop. As the move gets underway, the distance between the price and the indicator will shrink, thus making for a tighter stop-loss as the price moves in a favorable direction. There are two variables: the step and the maximum step. The higher the step is set, the more sensitive the indicator will be to price changes. If the step is set too high, the indicator will fluctuate above and below the price too often, making interpretation difficult. The maximum step controls the adjustment of the SAR as the price moves. The lower the maximum step is set, the further the trailing stop will be from the price. Wilder recommends setting the step at .02 and the maximum step at .20.
Moving Average Convergence/Divergence (MACD)
Developed by Gerald Appel, Moving Average Convergence Divergence (MACD) is one of the simplest and most reliable indicators available. The Moving Average Convergence/Divergence (MACD) indicator is calculated by subtracting the 12-period exponential moving average of a given currency or commodity from its 26-period exponential moving average. A 9-period exponential moving average of the MACD itself is usually plotted over this line as a signal or trigger line. By using moving averages, MACD has trend following characteristics. In addition, by plotting the difference of the moving averages as an oscillator, MACD also has momentum characteristics.
There are three techniques commonly used to interpret the MACD:
Divergence: When MACD moves counter to the direction of the currency itself, it is a warning that the currency's trend may change.
Centerline Crossover: Some analysts choose to buy or sell when the MACD goes above or below zero (the centerline).
Trigger line: When the MACD crosses above the slower trigger line, this is a bullish signal. When the MACD goes below the trigger line, it's a bearish signal.
RSI
The Relative Strength Index (RSI) is a bounded momentum oscillator that compares the magnitude of a currency's recent gains with the magnitude of its recent losses. The RSI ranges between 0 and 100 with 70 and 30 commonly used as overbought/oversold levels. It takes a single parameter, the number of time periods that should be used in the calculation; 14 is commonly used. The RSI was created by J. Welles Wilder. The RSI's full name is actually rather unfortunate as it is easily confused with other forms of Relative Strength analysis such as John Murphy's "Relative Strength" charts and IBD's "Relative Strength" rankings. Most other kinds of "Relative Strength" stuff involve using more than one stock in the calculation. Like most true indicators, the RSI only needs one stock to be computed. In order to avoid confusion, many people avoid using the RSI's full name and just call it "the RSI."
Momentum
As a leading indicator, momentum measures a currency's rate-of-change. The ongoing plot forms an oscillator that moves above and below 100. Bullish and bearish interpretations are found by looking for divergences, centerline crossovers and extreme readings.
Price
Momentum = --------------------------------- times 100
Price (n periods ago)
Momentum can also refer to a particular investing or trading style. The rational is that the hot get hotter and the cold get colder. Bullish momentum players buy currency pairs or commodities that are popular or that they believe will become popular. As the word spreads and popularity grows, the advance will accelerate. Price acceleration is the same as an increase in momentum.
Stochastic Oscillator
Developed by George Lane, the Stochastic Oscillator is a momentum indicator that measures the price of a currency or commodity relative to the high/low range over a set period of time. The indicator oscillates between 0 and 100, with readings below 20 considered oversold and readings above 80 considered overbought. A 14-period Stochastic Oscillator reading of 30 would indicate that the current price was 30% above the lowest low of the last 14 days and 70% below the highest high. The Stochastic Oscillator can be used like any other oscillator by looking for overbought/oversold readings, positive/negative divergences and centerline crossovers.
Recent Close - Lowest
Low (n)
%k= 100times (
Highest High (n)
Lowest Low (n)
%D= 3 Period Moving Average of %K
(n) = Number of Periods Used in Calculation
A 14-day %K (14-period Stochastic Oscillator) would use the most recent close, the highest high over the last 14 days and the lowest low over the last 14 days. The number of periods will vary according to the sensitivity and the type of signals desired. As with RSI, 14 is a popular number of periods for calculation.
CCI ("Commodity Channel Index")
Developed by Donald Lambert, the Commodity Channel Index (CCI) is an indicator designed to identify cyclical turns in currencies or commodities. There are 4 steps involved in the calculation of the CCI: Calculate today's Typical Price (TP) = (H+L+C)/3 where H = high; L = low, and C = close.
Calculate today's 20-day Simple Moving Average of the Typical Price (SMATP).
Calculate today's Mean Deviation. First, calculate the absolute value of the difference between today's SMATP and the typical price for each of the past 20 days. Add all of these absolute values together and divide by 20 to find the Mean Deviation.
The final step is to apply the Typical Price (TP), the Simple Moving Average of the Typical Price (SMATP), the Mean Deviation and a Constant (.015) to the following formula:
CCI =
(Typical Price) - (SMATP)
(.0015) times (Mean Deviation)
For scaling purposes, Lambert set the constant at .015 to ensure that approximately 70 to 80 percent of CCI values would fall between -100 and +100. The CCI fluctuates above and below zero. The percentage of CCI values that fall between +100 and -100 will depend on the number of periods used. A shorter CCI will be more volatile with a smaller percentage of values between +100 and -100. Conversely, the more periods used to calculate the CCI, the higher the percentage of values between +100 and -100.
Lambert's trading guidelines for the CCI focused on movements above +100 and below -100 to generate buy and sell signals. Because about 70 to 80 percent of the CCI values are between +100 and -100, a buy or sell signal will be in force only 20 to 30 percent of the time. When the CCI moves above +100, a currency is considered to be entering into a strong uptrend and a buy signal is given. The position should be closed when the CCI moves back below +100. When the CCI moves below -100, the security is considered to be in a strong downtrend and a sell signal is given. The position should be closed when the CCI moves back above -100.
Since Lambert's original guidelines, traders have also found the CCI valuable for identifying reversals. The CCI is a versatile indicator capable of producing a wide array of buy and sell signals.
CCI can be used to identify overbought and oversold levels. A currency would be deemed oversold when the CCI dips below -100 and overbought when it exceeds +100. From oversold levels, a buy signal might be given when the CCI moves back above -100. From overbought levels, a sell signal might be given when the CCI moved back below +100.
As with most oscillators, divergences can also be applied to increase the robustness of signals. A positive divergence below -100 would increase the robustness of a signal based on a move back above -100. A negative divergence above +100 would increase the robustness of a signal based on a move back below +100.
Trendline breaks can be used to generate signals. Trendlines can be drawn connecting the peaks and troughs. From oversold levels, an advance above -100 and trendline breakout could be considered bullish. From overbought levels, a decline below +100 and a trendline break could be considered bearish. Rex Takasugi has used this type of system to trade the Russell 2000.
Traders and investors use the CCI to help identify price reversals, price extremes and trend strength. As with most indicators, the CCI should be used in conjunction with other aspects of technical analysis. CCI fits into the momentum category of oscillators. In addition to momentum, volume indicators and the price chart may also influence a technical assessment.
Rate-of-change (percent)
The Rate-of-Change (percent) is a momentum oscillator that measures the percent change in price from one period to the next. A 10 period rate of change would be calculated as follows: ROC = 100*(Close-Close 10 periods ago)/(Close 10 periods ago)
The plot forms an oscillator that fluctuates above and below the zero line as the rate-of-change moves from positive to negative. The oscillator can be used as any other momentum oscillator by looking for higher lows, lower highs, positive and negative divergences, and crosses above and below zero for signals.
Standard Deviation
Standard deviation is a statistical term that provides a good indication of volatility. It measures how widely values (closing prices for instance) are dispersed from the average. Dispersion is difference between the actual value (closing price) and the average value (mean closing price). The larger the difference between the closing prices and the average price, the higher the standard deviation will be and the higher the volatility. The closer the closing prices are to the average price, the lower the standard deviation and the lower the volatility. The calculation for the standard deviation is based on the number of periods chosen. 20 days, which represents about a month, is a popular number of periods to use and will be used in the example below.
The steps for a 20-period standard deviation formula are as follows: Calculate the mean price. Sum the 20 periods and divide by 20. This is also the average price over 20 periods. (2246.06/20 = 112.30)
For each period, subtract the mean price from the close. This gives us the deviation for each period (-3.30, -9.24….).
Square each period's deviation (10.91, 85.38…).
Add together the squared deviations for periods 1 through 20 (921.28).
Divide the sum of the squared deviations by 20 (921.28/20 = 46.06).
Calculate the square root of the sum of the squared deviations. The square root of 46.06 equals 6.787.
The standard deviation for the 20 periods is 6.787.
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