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Technical Analysis Resource - Trend Indicators

Trend is a term used to describe the persistence of prices to move in one direction. Here is a list of Technical Analysis trend based indicators.


Aroon


Description


The Aroon indicator was developed by Tushar Chande. Aroon is a Sanskrit word meaning “dawn’s early light” or the change from night to day. The Aroon indicator allows you to anticipate changes in security prices from trending to trading range. For more information on the Aroon indicator see the article written by Tushar Chande in the September 1995 issue of Technical Analysis of Stocks & Commodities magazine.


These changes are anticipated by measuring the number of periods that have passed since the most recent x-period high and x-period low. Therefore, the Aroon indicator consists of two plots; one measuring the number of periods since the most recent x-period high (Aroon Up) and the other measuring the number of periods since the most recent x-period low (Aroon Down).


The actual plotted value is a “stochastic” like scale ranging from 0 to 100. Assuming a default time-period of 14 days, if a security makes a new 14-day high, the Aroon Up = 100; when the security makes a new 14-day low, the Aroon Down = 100. When the security has not made a new high for 14 days, the Aroon Up = 0; when the security has not made a new low for 14 days, the Aroon Down = 0.


As explained in the interpretation section for the VHF indicator the age-old problem for many trading systems is their inability to determine if a trending or trading range market is at hand. Trend-following indicators such as MACD and moving averages, tend to be whipsawed as markets enter a non-trending congestion phase. On the other hand, overbought/oversold oscillators (which work well during trading range markets) tend to overreact to price pull-backs during trending markets—thereby closing a position prematurely. The Aroon indicator attempts to remedy this by helping you determine when trend-following or overbought/oversold indicators are likely to succeed.


Interpretation


There are basically three conditions that you look for when interpreting the Aroon indicator: extremes at 0 and 100, parallel movement between Aroon Up and Aroon Down, and crossovers between Aroon Up and Aroon Down.


Extremes:
When the Aroon Up line reaches 100, strength is indicated. If the Aroon Up remains persistently between 70 and 100, a new uptrend is indicated. Likewise if the Aroon Down line reaches 100, potential weakness is indicated. If the Aroon Down remains persistently between 70 and 100, a new downtrend is indicated.


A strong uptrend is indicated when the Aroon Up line persistently remains between 70 and 100 while the Aroon Down line persistently remains between 0 and 30. Likewise a strong downtrend is indicated when the Aroon Down line persistently remains between 70 and 100 while the Aroon Up line persistently remains between 0 and 30.

  

Parallel Movement:
When the Aroon Up and Aroon Down Lines move parallel with each other (are roughly at the same level), then consolidation is indicated. Expect further consolidation until a directional move is indicated by an extreme level or a crossover.

  

Crossovers:
When the Aroon Down line crosses above the Aroon Up line, potential weakness is indicated. Expect prices to begin trending lower. When the Aroon Up line crosses above the Aroon Down line, potential strength is indicated. Expect prices to begin trending higher.


Commodity Selection Index


Description


The CSI is calculated using the ADXR component of the Directional Movement indicator (hyperlink). Refer to New Concepts in Technical Trading Systems for information on the calculation and interpretation of the indicator.


Interpretation


A high CSI rating indicates that the commodity has strong trending and volatility characteristics. The trending characteristics are brought out by the Directional Movement factor in the calculation--the volatility characteristic by the Average True Range factor.


Wilder's approach is to trade commodities with high CSI values (relative to other commodities). Because these commodities are highly volatile, they have the potential to make the "most money in the shortest period of time." High CSI values imply trending characteristics, which makes it easier to trade the security.


The CSI is designed for short-term traders who can handle the risks associated with highly volatile markets.


DEMA


Description


DEMA is a unique smoothing indicator developed by Patrick Mulloy. It was originally introduced in the February 1994 issue of Technical Analysis of Stocks & Commodities magazine.


As Mr. Mulloy explains in the article:


"Moving averages have a detrimental lag time that increases as the moving average length increases. The solution is a modified version of exponential smoothing with less lag time."


DEMA is an acronym that stands for Double Exponential Moving Average. However, the name of this smoothing technique is a bit misleading in that it is not simply a moving average of a moving average. It is a unique composite of a single exponential moving average and a double exponential moving average that provides less lag than either of the two components individually.


Interpretation


DEMA can be used in place of traditional moving averages. You can use it to smooth price data or other indicators. Some of Mr. Mulloy's original testing of DEMA was done on the MACD. Oddly, he found that the faster responding DEMA-smoothed MACD produced fewer (yet more profitable) signals than the traditional 12/26 smoothed-MACD.


This type of smoothing is certainly not limited to the MACD. You may want to experiment on other indicators as well.


Directional Movement


Description


The Directional Movement System, developed by J. Welles Wilder, is explained thoroughly in his book, New Concepts in Technical Trading Systems. MetaStock calculates and plots all five of the indicators that comprise the Directional Movement System (i.e., CSI, +DI, -DI, ADX, and ADXR).


MetaStock also calculates a related indicator, the Commodity Selection Index.


Wilder's book gives complete step-by-step instructions (and examples) on calculating and interpreting each of the above indicators.


Interpretation


The basic Directional Movement trading system involves plotting the 14-period +DI and the 14-period -DI on top of each other in the same inner window. An improved method of displaying these two indicators is to plot their difference using the following formula:


pdi(14) - mdi(14)


Positions should be taken by buying when the +DI rises above the -DI (i.e., the formula shown above rises above zero) and selling when the +DI falls below the -DI (i.e., the formula falls below zero).


These simple trading rules are qualified with the "extreme point rule." This rule is designed to prevent whipsaws and reduce the number of trades.


The extreme point rule requires that on the day that the +DI and -DI cross, you note the "extreme price." If you are long, the extreme price is the low price on the day the lines cross. If you are short, the extreme price is the high price on the day the lines cross.


The extreme point is then used as a trigger point at which you should implement the trade. For example, after receiving a buy signal (the +DI rose above the -DI), you should then wait until the security's price rises above the extreme point (the high price on the day that the +DI and -DI lines crossed) before buying. If the price fails to rise above the extreme point, you should continue to hold your short position.


In Wilder's book, he notes that this system works best on securities that have a high Commodity Selection Index (CSI) value. He says, "as a rule of thumb, the system will be profitable on commodities that have an ADXR value above 25. When the ADXR drops below 20, then do not use a trend-following system."


Forecast Oscillator


Description


The Forecast Oscillator is an extension of the linear regression based indicators made popular by Tushar Chande. The Forecast Oscillator plots the percentage difference between the forecast price (generated by an x-period linear regression line) and the actual price. The oscillator is above zero when the forecast price is greater than the actual price. Conversely, it's less than zero if its below. In the rare case when the forecast price and the actual price are the same, the oscillator would plot zero.


Interpretation


Actual prices that are persistently below the forecast price suggest lower prices ahead. Likewise, actual prices that are persistently above the forecast price suggest higher prices ahead. Short-term traders should use shorter time periods and perhaps more relaxed standards for the required length of time above or below the forecast price. Long-term traders should use longer time periods and perhaps stricter standards for the required length of time above or below the forecast price.


Chande also suggests plotting a three-day moving average trigger line of the Forecast Oscillator to generate early warnings of changes in trend. When the oscillator crosses below the trigger line, lower prices are suggested. When the oscillator crosses above the trigger line, higher prices are suggested.



Linear Regression Indicator


Description


The Linear Regression indicator is based on the trend of a security's price over a specified time period. The trend is determined by calculating a linear regression trendline using the "least squares fit" method. The least squares fit technique fits a trendline to the data in the chart by minimizing the distance between the data points and the linear regression trendline.

 

Any point along the Linear Regression indicator is equal to the ending value of a Linear Regression trendline. For example, the ending value of a Linear Regression trendline that covers 10 days will have the same value as a 10-day Linear Regression indicator. This differs slightly from the Time Series Forecast indicator in that the TSF adds the slope to the ending value of the regression line. This makes the TSF a bit more responsive to short term price changes. If you plot the TSF and the Linear Regression indicator side-by-side, you’ll notice that the TSF hugs the prices more closely than the Linear Regression indicator.


Rather than plotting a straight Linear Regression trendline, the Linear Regression indicator plots the ending values of multiple Linear Regression trendlines.


Interpretation


The interpretation of a Linear Regression indicator is similar to a moving average. However, the Linear Regression indicator has two advantages over moving averages.


Unlike a moving average, a Linear Regression indicator does not exhibit as much "delay." Since the indicator is "fitting" a line to the data points rather than averaging them, the Linear Regression line is more responsive to price changes.


The indicator is actually a forecast of the next periods (tomorrow’s) price plotted today. The Forecast Oscillator plots the percentage difference between the forecast price and the actual price. Tushar Chande suggests that when prices are persistently above or below the forecast price, prices can be expected to snap back to more realistic levels. In other words the Linear Regression indicator shows where prices should be trading on a statistical basis. Any excessive deviation from the regression line should be short-lived.



Linear Regression Slope


Description


The Linear Regression method provides several useful outputs for technical analysts, including the Slope. The Slope shows how much prices are expected to change per unit of time. Some may remember this as “rise over run.”


Interpretation


It is helpful to consider Slope in relation to r-squared. While Slope gives you the general direction of the trend (positive or negative), r-squared gives you the strength of the trend. A high r-squared value can be associated with a high positive or negative Slope.


When the Slope of the trend first becomes significantly positive, you could open a long position. You could sell, or open a short position when the Slope first becomes significantly negative. You should refer to the table below to determine when a trend is deemed “significant.” For example, if the 14-period Slope has recently turned from negative to positive (i.e., crossed above zero), you may consider buying when r-squared crosses above the 0.27 level.


To determine if the trend is statistically significant for a given x-period linear regression line, plot the r-squared indicator and refer to the following table. This table shows the values of r-squared required for 95% confidence level at various time periods. If the value is less than the critical values shown, you should assume that prices show no statistically significant trend.


You may even consider opening a short-term position opposite the prevailing trend when you observe the Slope rounding off at extreme levels. For example, if the Slope is at a relatively high level and begins to turn down, you may consider selling or opening a short position.


There are numerous ways to use the linear regression outputs of Slope and r-squared in trading systems. For more detailed coverage, refer to the book The New Technical Trader by Tushar Chande and Stanley Kroll.


Linear Regression Trendline


Description


Linear regression is a statistical tool used to predict future values from past values. In the case of security prices, it is commonly used as a quantitative way to determine the underlying trend and when prices are overextended.


A Linear Regression trendline uses the least squares method to plot a straight line through prices so as to minimize the distances between the prices and the resulting trendline.



Interpretation


If you had to guess what a particular security's price would be tomorrow, a logical guess would be “fairly close to today’s price.” If prices are trending up, a better guess might be “fairly close to today’s price with an upward bias.” Linear regression analysis is the statistical confirmation of these logical assumptions.


A Linear Regression trendline is simply a trendline drawn between two points using the least squares fit method. The trendline is displayed in the exact middle of the prices. If you think of this trendline as the “equilibrium" price, any move above or below the trendline indicates overzealous buyers or sellers.


A Linear Regression trendline shows where equilibrium exists. Raff Regression Channels show the range prices can be expected to deviate from a Linear Regression trendline.


The Time Series Forecast indicator displays the same information as a Linear Regression trendline. Any point along the Time Series Forecast is equal to the ending value of a Linear Regression Trendline plus its slope. For example, the ending value of a Linear Regression trendline (plus its slope) that covers 10 days will have the same value as a 10-day Time Series Forecast.


Linear Regression Trendlines is used to construct Raff Regression, Projection Bands, Projection Oscillator and the Linear Regression indicator.


MACD


Description


The Moving Average Convergence/Divergence indicator (MACD) is calculated by subtracting the value of a 0.075 (26-period) exponential moving average from a 0.15 (12-period) exponential moving average. A 9-period dotted exponential moving average (the "signal line") is automatically displayed on top of the MACD indicator line.


Interpretation


The basic MACD trading rule is to sell when the MACD falls below its 9-period signal line. Similarly, a buy signal occurs when the MACD rises above its signal line.


A variation of the MACD can be created by plotting the following formula:


macd() - mov(macd(), 9, E)


Then change the indicator line style to a histogram and plot a 9-period dotted moving average of the indicator.

In a system test of this indicator, sell arrows are drawn when the histogram peaks and turns down and buy arrows are drawn when the histogram forms a trough and turns up.


Moving Average (all methods)


Description


A moving average is a method of calculating the average value of a security's price, or indicator, over a period of time. The term "moving" implies, and rightly so, that the average changes or moves. When calculating a moving average, a mathematical analysis of the security's average value over a predetermined time period is made. As the security's price changes over time, its average price moves up or down.


MetaStock calculates and displays six different types of moving averages: simple (also referred to as arithmetic), exponential, time series, triangular, variable, and weighted. In addition, MetaStock will calculate moving averages of the security's open, high, low, close, median price, typical price, volume, open interest, or indicator.


The only significant difference between the various types of moving averages is the weight assigned to the most recent data. Once this "weighting" scheme has been determined, it is held static over the range of calculations. The exceptions are the variable moving average and volume adjusted moving average. The variable moving average automatically adjusts its weighting based on market conditions. A variable moving average becomes more sensitive to recent data as volatility increases and less sensitive to recent data as volatility decreases. Similarly, the volume adjusted moving average automatically adjusts as the security's volume increases and decreases.


Moving Average Calculation Methods


Exponential

Simple

Time Series

Triangular

Variable

Volume Adjusted

Weighted

Interpretation


The most popular method of interpreting a moving average is to compare the relationship between a moving average of the security's closing price and the security's closing price itself. A sell signal is generated when the security's price falls below its moving average and a buy signal is generated when the security's price rises above its moving average.


This type of moving average trading system is not intended to get you in at the exact bottom and out at the exact top. Rather, it is designed to keep you in line with the security's price trend by buying shortly after the security's price bottoms and selling shortly after it tops.


The critical element in a moving average is the number of time periods used in calculating the average. When using hindsight, you can always find a moving average that would have been profitable. The key is to find a moving average that will be consistently profitable. The most popular moving average is the 39-week (or 200-day) moving average. This moving average has a good track record in timing the major (long- term) market cycles. The length of a moving average should fit the market cycle you wish to follow:


You can convert a daily moving average quantity into a weekly moving average quantity by dividing the number of days by 5 (e.g., a 200-day moving average is almost identical to a 40-week moving average). To convert a daily moving average quantity into a monthly quantity, divide the number of days by 21 (e.g., a 200-day moving average is very similar to a 9-month moving average).


MetaStock enables you to plot moving averages of securities and any of the indicators tracked by the program. The interpretation of an indicator's moving average is similar to the interpretation of a security's moving average: when the indicator rises above its moving average, it signifies a continued upward movement by the indicator; when the indicator falls below its moving average, it signifies a continued downward movement by the indicator.


Indicators which are especially well-suited for use with moving average penetration systems include the MACD, Price R.O.C., Momentum, and Stochastics.


Some indicators, such as short-term Stochastics, fluctuate so erratically that it is difficult to tell what their trend really is. By removing the indicator (i.e., setting the Indicator Style to Invisible in the Indicator's Properties dialog) and then plotting a moving average of the indicator, we can see the general trend of the indicator rather than its day-to-day fluctuations.


Whipsaws can be reduced, at the expense of a slightly later signal, by plotting a short-term moving average (e.g., 2-10 day) of oscillating indicators such as the 12-day R.O.C., Stochastics, or the RSI. For example, rather than selling when the Stochastic Oscillator falls below 80, you might sell only when a 5-period moving average of the Stochastic Oscillator falls below 80.


Parabolic SAR


Description


The Parabolic Time/Price System, developed by J. Welles Wilder, is explained thoroughly in his book, New Concepts in Technical Trading. This indicator is used to set price stops and is often called the stop-and-reversal (SAR) indicator.


Interpretation


If you are long (i.e., the price is above the SAR), the SAR will move up every day, regardless of the direction the price is moving. The amount the SAR moves up depends on the amount that prices move.


The Parabolic SAR provides excellent stops. You should close long positions when the price falls below the SAR and close short positions when the price rises above the SAR.


The Parabolic SAR is plotted as shown in Wilder's book (above). Each SAR stop level point is displayed on the day in which it is in effect. Note that the SAR value is today's, not tomorrow's stop level.


Performance


Description


The Performance indicator displays a security's price performance as a percentage. This is sometimes called a "normalized" chart.


Interpretation


The numeric value of the Performance indicator is the percentage that the security has changed since the first period loaded. For example, a value of 10 would mean that the security's price has increased 10% since the first period loaded on the left side of the chart. Similarly, a value of -10% would mean that the security's price has fallen by 10% since the first period.


MetaStock Tip


The Performance indicator calculates the percent that prices have changed since the first day loaded in the chart. Therefore, if you want to calculate a security's performance from a specific date (e.g., the day you bought it), you should first use the X-Axis Properties dialog to change the first date loaded.


Polarized Fractal Efficiency


Description


The Polarized Fractal Efficiency indicator (PFE) was developed by Hans Hannula. It was introduced in the January 1994 issue of Technical Analysis of Stocks & Commodities magazine. As an engineer, programmer, and trader with over 30 years market experience, Mr. Hannula developed a unique approach to applying the laws of fractal geometry and chaos to the markets.


Interpretation


Drawing upon the pioneering works of mathematician Benoit Mendelbrot, Mr. Hannula developed an indicator to gauge the efficiency that prices travel between two points in time.


The more linear and efficient price movement, the shorter the distance the prices must travel between two points. The more "squiggly" the price movement, the less efficient it's travel.


The primary use of the PFE indicator is as a measure of how trendy or congested price action is. PFE readings above zero mean that the trend is up. The higher the reading the "trendier" and more efficient the upward movement. PFE readings below zero mean that the trend is down. The lower the reading the "trendier" and more efficient the downward movement. Readings around zero indicate choppy, less efficient movement, with a balance between the forces of supply and demand.


Several interesting phenomenon have been observed by Mr. Hannula:



Price Oscillator


Description


The Price Oscillator displays the difference between two moving averages of the security's price. The difference between the averages can be expressed in either points or percentages.


Interpretation


Moving average analysis often generates buy signals when a short-term moving average (or the security's price) rises above a longer-term moving average. Conversely, sell signals are generated when a shorter-term moving average falls below a longer-term moving average. The Price Oscillator illustrates the cyclical (and often profitable) signals generated by one or two moving average systems.


Qstick Indicator


Description


The Qstick indicator was developed by Tushar Chande. Qstick provides a way to quantify candlesticks. The distance between the open and close prices lies at the heart of candlestick charting. For those unfamiliar with candlestick charting, the body of a candlestick is black if today’s close is less than the open; it is white if today’s close is greater than the open. A majority of white candlesticks over a specified range is considered bullish. Whereas a majority of black candlesticks over a specified range is considered bearish.

The Qstick indicator is simply a moving average of the difference between open and close prices.


For more information on the Qstick indicator, refer to the book The New Technical Trader by Tushar Chande and Stanley Kroll.


Interpretation


Qstick values below zero indicate a majority of black candlesticks (over the time periods specified) and therefore a bearish bias for the security. Values above zero indicate a majority of white candlesticks (over the time periods specified) and therefore a bullish bias for the security.


There are several ways to trade the Qstick indicator:


Crossovers: Buy when the indicator crosses above zero. Sell when it crosses below zero.

Extreme Levels: Buy when the Qstick indicator is at an extremely low level and turning up. Sell when the Qstick indicator is at an extremely high level and turning down. You may even want to plot a short-term moving average on the Qstick to serve as a trigger line.

  

Divergences: Buy when the Qstick is moving up and prices are moving down. Sell when the Qstick is moving down and prices are moving up. You may want to consider waiting for the price to confirm the new direction before placing the trade.


Raff Regression Channel


Description


Developed by Gilbert Raff, the regression channel is a line study that plots directly on the price chart. The Regression Channel provides a precise quantitative way to define a price trend and its boundaries.


The Regression Channel is constructed by plotting two parallel, equidistant lines above and below a Linear Regression trendline. The distance between the channel lines to the regression line is the greatest distance that any one high or low price is from the regression line.


For more detailed information on using the Raff Regression Channel, we recommend the book Trading the Regression Channel by Gilbert Raff.


For information on other channel-based line studies, see Envelopes, Standard Deviation Channels, Standard Error Bands, and Standard Error Channels.


Interpretation


Raff Regression Channels contain price movement, with the bottom channel line providing support and the top channel line providing resistance. Prices may extend outside of the channel for a short period of time. However, if prices remain outside the channel for a long period of time, a reversal in trend may be imminent.


r-squared


Description


The Linear Regression method provides several useful outputs for technical analysts, including the r-squared. R-squared shows the strength of trend. The more closely prices move in a linear relationship with the passing of time, the stronger the trend.


Interpretation


r-squared values show the percentage of movement that can be explained by linear regression. For example, if the r-squared value over 20 days is at 70%, this means that 70% of the movement of the security is explained by linear regression. The other 30% is unexplained random noise.


It is helpful to consider r-squared in relation to Slope. While Slope gives you the general direction of the trend (positive or negative), r-squared gives you the strength of the trend. A high r-squared value can be associated with a high positive or negative Slope.


Although it is useful to know the r-squared value, ideally, you should use r-squared in tandem with Slope. High r-squared values accompanied by a small Slope may not interest short term traders. However, high r-squared values accompanied by a large Slope value may be of huge interest to traders.


One of the most useful way to use r-squared is as a confirming indicator. Momentum based indicators (e.g., Stochastics, RSI, CCI, etc.) and moving average systems require a confirmation of trend in order to be consistently effective. R-squared provides a means of quantifying the “trendiness” of prices. If r-squared is above its critical value and heading up, you can be 95% confident that a strong trend is present.


When using momentum based indicators, only trade overbought/oversold levels if you have determined that prices are trendless or weakening (i.e., a low or lowering r-squared value). Because in a strong trending market, prices can remain overbought or oversold for extended periods. Therefore, you may want to reconsider trading on strict overbought/oversold levels used by many indicators. An “overbought” market can remain overbought for extended periods in a trending market. However, a signal generated by a moving average crossover system may be worth following, since these systems work best in strong trending markets.


To determine if the trend is statistically significant for a given x-period linear regression line, plot the r-squared indicator and refer to the following table. This table shows the values of r-squared required for a 95% confidence level at various time periods. If the r-squared value is less than the critical values shown, you should assume that prices show no statistically significant trend.


You may even consider opening a short-term position opposite the prevailing trend when you observe r-squared rounding off at extreme levels. For example, if the slope is positive and r-squared is above 0.80 and begins to turn down, you may consider selling or opening a short position.


There are numerous ways to use the linear regression outputs of r-squared and Slope in trading systems. For more detailed coverage, refer to the book The New Technical Trader by Tushar Chande and Stanley Kroll.


Standard Deviation Channel


Description


Standard Deviation Channels are calculated by plotting two parallel lines above and below an x-period linear regression trendline. The lines are plotted x standard deviations away from the linear regression trendline.


For information on other channel-based line studies, see Envelopes, Raff Regression Channels, Standard Error Bands, and Standard Error Channels.


Interpretation


Price movements are characterized by swings from one extreme to the other. Markets reflect the collective mood if its participants. When market participants are overly optimistic, prices are driven up at an unsustainable rate. Likewise, when market participants are overly pessimistic, prices are beaten down at an unsustainable rate. The keywords here are "extreme" and "unsustainable." Even the most raging bull markets or violent bear markets will either pause for a breather or reverse temporarily.


Markets tend to have an equilibrium point (i.e., a point towards which prices tend to be drawn). Linear regression analysis is helpful in determining where this "balancing point" lies. On the other hand, standard deviation analysis is helpful in determining where the "extremes" lie.


Elementary statistical analysis states that approximately 67% of future price movement should be contained within one standard deviation and approximately 95% within two standard deviations. However, this assumes random, trendless data. Since most markets show overwhelming evidence of non-random, trending behaviour, these 67% and 95% values are not as accurate. Standard Deviation channels, however, incorporate the trend (as measured by the middle linear regression plot). Therefore, they provide a trend-biased assessment of expected price movement.


Standard Deviation Channels can be used to enhance several types of technical analysis techniques. Here are some ideas:


Validate candlestick patterns:
Enter long on bullish engulfing lines only if they form below the bottom channel line.

Validate overbought/oversold signals:
Close long (or enter short) when the Stochastic falls below 80, volume is above average, and prices have recently fallen below the top channel line.

Validate support/resistance breakouts: If prices have broken above a long-term resistance level, yet volume is suspiciously light, wait until the prices break above the upper channel on above average volume.


Standard Error


Description


Standard Error measures how closely prices congregate around a linear regression line. The closer prices are to the linear regression line, the higher the r-squared value and the stronger the trend.


For example, if each day’s closing price was equal to that day’s regression line value, then the standard error would be zero. The more variance or “noise” around the regression value, the larger the standard error and the less reliable the trend.


Interpretation


High standard error values indicate that the security’s prices are very volatile around the regression line. Changes in the prevailing trend (over the number of time periods specified) are usually preceded by a rapidly increasing standard error.


Standard error can be used effectively in combination with the r-squared indicator. Changes in trend are often signalled by a high downward moving r-squared, a low upward moving standard error, or a low upward moving r-squared and a high downward moving standard error. In other words, when the two are at extreme levels and begin to converge, look for a change in trend.


Note that a change in trend does not necessarily mean that an upward trend will reverse to a downward trend. Sideways movement is also considered a "change".


Standard Error Bands


Description


Standard Error Bands are a type of Envelope developed by Jon Andersen. They are similar to Bollinger Bands in appearance, but they are calculated and interpreted quite differently. Where Bollinger Bands are plotted at standard deviation levels above and below a moving average, Standard Error Bands are plotted at standard error levels above and below a linear regression plot. Click here for a definition of standard error.


For information on other channel-based line studies, see Envelopes, Raff Regression Channels, Standard Deviation Channels, and Standard Error Channels.


Interpretation

When displaying Standard Error Bands, you are prompted to enter the number of periods in the bands and the number of standard errors between the bands and the linear regression line. Mr. Andersen recommends default values of "21" for the number of periods, a 3-day simple moving average for the smoothing, and "2" standard errors. He also notes that very short time frames tend to produce unreliable results.


MetaStock plots Standard Error Bands on the security's prices or indicator. These interpretational comments refer to bands on the security's closing price.


Because the spacing between Standard Error Bands is based on the standard error of the security, the bands widen when the volatility around the current trend increases, and contract when volatility around the current trend decreases.


Since Standard Error Bands are statistically based, other statistical indicators such as r-squared, Standard Error, Linear Regression, etc. work well for trade confirmation.


Mr. Andersen notes the following characteristics of Standard Error Bands.



Standard Error Channel


Description


Standard Error Channels are calculated by plotting two parallel lines above and below an x-period linear regression trendline. The lines are plotted a specified number of standard errors away from the linear regression trendline.


For information on other channel-based line studies, see Envelopes, Raff Regression Channels, Standard Deviation Channels, and Standard Error Bands.


Interpretation


Price movements are characterized by swings from one extreme to the other. Markets reflect the collective mood if its participants. When market participants are overly optimistic, prices are driven up at an unsustainable rate. Likewise, when market participants are overly pessimistic, prices are beaten down at an unsustainable rate. The keywords here are "extreme" and "unsustainable." Even the most raging bull markets or violent bear markets will either pause for a breather or reverse temporarily.


Markets tend to have an equilibrium point (i.e., a point towards which prices tend to be drawn). Linear regression analysis is helpful in determining where this "balancing point" lies. On the other hand, standard error analysis is helpful in determining where the "extremes" lie.


Standard Error Channels can be used to enhance several types of technical analysis techniques. Here are some ideas:


 

TEMA


Description


TEMA is a unique smoothing indicator developed by Patrick Mulloy. It was originally introduced in the January 1994 issue of Technical Analysis of Stocks & Commodities magazine.

As Mr. Mulloy explains in the article:


"Moving averages have a detrimental lag time that increases as the moving average length increases. The solution is a modified version of exponential smoothing with less lag time."


TEMA is an acronym that stands for Triple Exponential Moving Average. However, the name of this smoothing technique is a bit misleading in that it is not simply a moving average of a moving average of a moving average. It is a unique composite of a single exponential moving average, a double exponential moving average, and a triple exponential moving average that provides less lag than either of the three components individually.


Interpretation


TEMA can be used in place of traditional moving averages. You can use it to smooth price data or other indicators. Some of Mr. Mulloy's original testing of TEMA was done on the MACD. Oddly, he found that the faster responding TEMA-smoothed MACD produced fewer (yet more profitable) signals than the traditional 12/26 smoothed- MACD. A custom indicator named "MACD (TEMA-smoothed)" is included with MetaStock Pro.

This type of smoothing is certainly not limited to the MACD. You may want to experiment on other indicators as well.



Time Series Forecast


Description


The Time Series Forecast indicator is based on the trend of a security's price over a specified time period. The trend is determined by calculating a linear regression trendline using the "least squares fit" method. The least squares fit technique fits a trendline to the data in the chart by minimizing the distance between the data points and the linear regression trendline.


Any point along the Time Series Forecast is equal to the ending value of a Linear Regression trendline plus its slope. For example, the ending value of a Linear Regression trendline (plus its slope) that covers 10 days will have the same value as a 10-day Time Series Forecast. This differs slightly from the Linear Regression indicator in that the Linear Regression indicator does not add the slope to the ending value of the regression line. This makes the TSF a bit more responsive to short term price changes. If you plot the TSF and the Linear Regression indicator side-by-side, you’ll notice that the TSF hugs the prices more closely than the Linear Regression indicator.


Rather than plotting a straight Linear Regression trendline, the Time Series Forecast indicator plots the ending values of multiple Linear Regression trendlines. The resulting Time Series Forecast indicator is sometimes referred to as a "moving linear regression" study or a "regression oscillator."


Interpretation


The interpretation of a Time Series Forecast is similar to a moving average. However, the Time Series Forecast indicator has two advantages over moving averages.


Unlike a moving average, a Time Series Forecast does not exhibit as much "delay." Since the indicator is "fitting" a line to the data points rather than averaging them, the Time Series line is more responsive to price changes.


As the name suggests, the indicator can be used to forecast the next period's price. This estimate is based on the trend of the security's prices over the period specified (e.g., 20 periods). If the trend continues, the last point of the trendline (the value of the Time Series Forecast) is forecasting the next period's price.



Vertical Horizontal Filter


Description


The Vertical Horizontal Filter (VHF) determines whether prices are in a trending phase or a congestion phase. The VHF compares the sum of a one period rate-of-change to the range between high and low prices over the specified period.


The age-old problem for many trading systems is their inability to determine if a trending or trading range market is at hand. Trend-following indicators such as MACD and moving averages, tend to be whipsawed as markets enter a non-trending congestion phase. On the other hand, oscillators (which work well during trading range markets) tend to overreact to price pull-backs during trending markets. The VHF indicator attempts to remedy this by measuring the "trendiness" of a market.


MetaStock prompts you to enter the number of periods to use in the calculation. The default value is 28.


Interpretation


There are three ways to use the VHF indicator:


  

  





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