Trend Indicators Overview
Trend indicators tell you which direction the market is moving in, if there is a trend at all. Trend indicators we’ll discuss include moving averages, Parabolic SAR, SuperTrend, and Ichimoku Kinkō Hyō.
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Note: In case you like to trade with free trend-based indicators, this guide will cover the most notable ones.
Moving Averages (MA)
Moving Average (MA) is a price based, lagging (or reactive) indicator that displays the average price of a security over a set period of time. A Moving Average is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance.
Essentially, Moving Averages smooth out the “noise” when trying to interpret charts. Noise is made up of fluctuations of both price and volume. Because a Moving Average is a lagging indicator and reacts to events that have already happened, it is not used as a predictive indicator but rather an interpretive one, used for confirmations and analysis.
In fact, Moving Averages form the basis of several other well-known technical analysis tools such as Bollinger Bands and the MACD. There are a few different types of Moving Averages which all take the same basic premise and add a variation.
There are many different types of moving averages and listing them all is not possible in one guide. All of them are considered as lagging indicators. In this guide, we will introduce all the popular ones that you need to be aware of.
Note: Remember that your trading strategy should not use too many moving averages. Most traders use one to three moving averages at most depending on their strategy.
Simple Moving Average (SMA)
Simple Moving Average is an unweighted Moving Average. This means that each day in the data set has equal importance and is weighted equally. As each new day ends, the oldest data point is dropped and the newest one is added to the beginning.
// SMA Of Specific Period = Sum of Period Values / Number of Periods SMA(Period) = Sum(Period) / Period
Exponential Moving Average (EMA)
The Exponential Moving Average (EMA) is a specific type of moving average that points towards the importance of the most recent data and information from the market.
The Exponential Moving Average is just like it’s name says – it’s exponential, weighting the most recent prices more than the less recent prices. The EMA can be compared and contrasted with the simple moving average.
/* t = today y = yesterday p = number of days in EMA k = 2 / (P + 1) */ EMA = Price(t) * k + EMA(y) * (1 − k)
Double Exponential Moving Average (DEMA)
The double exponential moving average (DEMA) is a technical indicator that was devised to reduce the lag in the results produced by a traditional moving average. Technical traders use it to lessen the amount of "noise" that can distort the movements on a price chart.
Like any moving average, the DEMA is used to indicate the trend in the price of a stock or other asset. By tracking its price over time, the trader can spot an uptrend, when the price moves above its average, or a downtrend, when the price moves below its average. When the price crosses the average, it may signal a sustained change in the trend.
As its name implies, the DEMA uses two exponential moving averages (EMAs) in order to eliminate lag in the charts.
This variation on the moving average was introduced by Patrick Mulloy in a 1994 article "Smoothing Data With Faster Moving Averages" in Technical Analysis of Stocks & Commodities magazine.
// It uses two EMAs in its calculations as follows: DEMA = 2 * EMA(Period) - EMA of EMA(Period)
Triple Exponential Moving Average (TEMA)
The triple exponential moving average (TEMA) was designed to smooth price fluctuations, thereby making it easier to identify trends without the lag associated with traditional moving averages (MA). It does this by taking multiple exponential moving averages (EMA) of the original EMA and subtracting out some of the lag.
The TEMA is used like other MAs. It can help identify trend direction, signal potential short-term trend changes or pullbacks, and provide support or resistance.
/* EMA1 = EMA of Specific Period EMA2 = EMA of EMA1 EMA3 = EMA of EMA2 */ TEMA = (EMA1 * 3) - (EMA2 * 3) + EMA3
Smoothed Moving Average (SMMA)
The Smoothed Moving Average compares recent prices to historical ones and makes sure they are weighed and considered equally. The calculation of this indicator does not reference a specific or fixed period, rather uses all available data in the series for analysis.
The Smoothed Moving Average differs from the Exponential Moving Average (EMA) because it’s generally used with a longer time period.
The calculation for the Smoothed Moving Average, as mentioned above, does not refer to a fixed period, rather uses all data available in the series. The following steps are used to calculate the indicator.
- Subtract the previous day’s Smoothed Moving Average from the current day’s price.
- Next, add the result from Step 1 to the previous day’s Smoothed Moving Average.
- The results will yield the current day’s Moving Average.
Volume-Weighted Moving Average (VWMA)
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. This the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Volume weighting can be applied to almost any type of moving average. You could have a simple volume weighted moving average, an exponential volume weighted moving average, a moving average which is both front weighted and volume weighted or even a volume weighted moving linear regression.
Note as well that there is a Volume-Weighted Exponential Moving Average (VWEMA) which is the same as VWMA but uses EMA instead of SMA.
The basic calculation is to apply the moving average (of whatever type) to both price (or whatever else is being averaged) times volume and to just volume by itself. You then divide the moving average of price times volume by the moving average of just volume to get the volume weighted moving average.
VWMA = SMA(source * volume, period) / SMA(volume, period) VWEMA = EMA(source * volume, period) / EMA(volume, period)
Hull Moving Average (HMA)
The Hull Moving Average (HMA) is a quick and smooth moving average that is distinct in its own nature. The HMA attempts to remove lag in its entirety, while simultaneously improving upon smoothing.
The Hull Moving Average was developed and first introduced by Alan Hull as a new moving average that focuses on smoothness, efficiency, and lag elimination.
To calculate the Hull Moving Average, follow the steps below.
- Begin by calculating a Weighted Moving Average with period (p) divided by 2 and then multiply this value by 2.
- Next, go ahead and calculate another Weighted Moving Average for period (p) and then subtract this value from the result in Step 1.
- Finally, calculate a Weighted Moving Average with period sqrt(p) by using the data you’ve collected from the results of Step 2.
/* WMA = The Weighted Moving Average Function sqrt = Square Root Function */ HMA = WMA(2 * WMA(period/2) − WMA(period)), sqrt(period))
Arnaud Legoux Moving Average (ALMA)
The Arnaud Legoux Moving Average (ALMA) is different from other moving averages because of its specific design to use Gaussian distribution that is shifted with a calculated offset in order for the average to be biased towards more recent days, instead of more evenly centered on the window. Built on the generalized Moving Average Framework, ALMA is able to use various indicators in conjunction with its own capabilities and run on multiple time frames, with the inclusion of custom bar types.
The Arnaud Legoux Moving Average (ALMA) indicator was developed by both Arnaud Legoux and Dimitrios Douzis-Loukas while trying to create a new and improved moving average that would showcase advanced smoothness and responsiveness in comparison to other moving averages at the time of its development.
Legoux claimed the ALMA moving average was inspired significantly by the Gaussian Filter and often compares his developed moving average to the Hull Moving Average (HMA), which is said to be outperformed by the ALMA in effectiveness and smoothness.
- To calculate the Arnaud Legoux Moving Average (ALMA) you’ll first need to compute a weighted sum of the window’s size using your input series and weights given by a Gaussian function with a peak value determined by the offset, and a width determined by sigma.
- This weighted sum is then divided by the total sum of the weights.
Least Squares Moving Average (LSMA)
The least square moving average (LSMA) calculates the least squares regression line for the preceding time periods, thus leading to forward projections from the current period. Accordingly, the indicator has the ability to identify what could happen if the regression line continued.
Tillson Moving Average (T3)
The T3 moving average was developed by Tom Tilson. It is designed to be smoother and more responsive than traditional moving averages, but like a moving linear regression, it can overshoot price or the indicator to which it is applied.
The McGinley Dynamic adjusts for market speed shifts, which sets it apart from other moving averages, in addition to providing clear moving average lines. The McGinley Dynamic is a technical indicator based on a moving average that was initially designed to track market trends.
John R. McGinley created the McGinley Dynamic to produce a moving average that could drastically reduce the lag between an indicator and the market in general. If successful, the moving average would better be faster and more reliable than other moving averages available.
Be mindful of lag when using the moving averages, as no moving average is exempt from the effects of being set in fixed time lengths. The McGinley Dynamic indicator, on the other hand, has already considered this issue, and uses its adjustment of market speed shifts to project smoother and more accurate lines.
Market speed often fluctuates, moving slower or faster at times. The McGinley Dynamic indicator acknowledges this market trait and incorporates an automatic smoothing factor into its formula in order to accommodate market speeds.
Guppy Moving Averages (GMMA)
The Guppy Multiple Moving Average (GMMA) is a technical indicator that aims to anticipate a potential breakout in the price of an asset. The term gets its name from Daryl Guppy, an Australian financial columnist and book author who developed the concept in his book, "Trading Tactics."
The GMMA uses the exponential moving average (EMA) to capture the difference between price and value in a stock. A convergence in these factors is associated with a significant trend change. Guppy maintains that the GMMA is not a lagging indicator but a prior warning of a developing change in price and value.
The GMMA consists of a short-term group of MAs and a long-term group of MAs, both containing six MAs, for a total of 12, and is overlaid on the price chart of an asset.
The short-term MAs are typically set at 3, 5, 8, 10, 12, and 15 periods. The longer-term MAs are typically set at 30, 35, 40, 45, 50, and 60.
When the short-term group of averages moves above the longer-term group, it indicates a price uptrend in the asset could be emerging. Conversely, when the short-term group falls below the longer-term group of MAs, a price downtrend in the asset could be starting.
Kaufman’s Adaptive Moving Average (KAMA)
Kaufman’s Adaptive Moving Average (KAMA) was developed by American quantitative financial theorist Perry J. Kaufman in 1998. The technique began in 1972 but Kaufman officially presented it to the public much later through his book, “Trading Systems and Methods.” Unlike other moving averages, Kaufman’s Adaptive Moving Average accounts not only for price action but also for market volatility.
When market volatility is low, Kaufman’s Adaptive Moving Average remains near the current market price, but when volatility increases, it will lag behind. What the KAMA indicator aims to do is filter out “market noise” – insignificant, temporary surges in price action. One of the primary weaknesses of traditional moving averages is that when used for trading signals, they tend to generate many false signals. The KAMA indicator seeks to lessen this tendency – generate fewer false signals – by not responding to short-term, insignificant price movements.
Traders generally use the moving average indicator to identify market trends and reversals.
How KAMA Works?
When traders use Kaufman’s Adaptive Moving Average indicator, they get a clear picture of the market’s behavior, which they can use to make trading decisions. The indicator uses historical data to obtain the final values. Traders make a decision on the basis of the theory that future trends will continue to develop in the same direction as the past trends.
The KAMA indicator can easily be applied to a chart. The trader enjoys the option of customizing the indicator by specifying its parameters in the properties dialog box. The main parameters to be customized include the calculation periods and appearance of the indicator. Traders can specify the number of periods to apply Kaufman’s Adaptive Moving Average to in the calculation parameter. The default number of periods is 14, but traders can select any value between 2 and 1000.
When the Kaufman’s Adaptive Moving Average indicator is represented on a chart, traders can use it to analyze the behavior of a market and predict future price movement. The KAMA indicator can be used to identify existing trends, indications of a possible impending trend change, and market reversal points that can be used for trade entries or exits.
Using the KAMA
One of the uses of Kaufman’s Adaptive Moving Average is to identify the general trend of current market price action. Basically, when the KAMA indicator line is moving lower, it indicates the existence of a downtrend. On the other hand, when the KAMA line is moving higher, it shows an uptrend. As compared to the Simple Moving Average, the KAMA indicator is less likely to generate false signals that may cause a trader to incur losses.
Kaufman’s Adaptive Moving Average can also be used to spot the beginning of new trends and pinpoint trend reversal points. One way to do this is by plotting two KAMA lines on a chart – one with a more short-term moving average and another with a longer-term moving average. When a faster KAMA line crosses above a slower KAMA line, this indicates a change from a downtrend to an uptrend. The trader can take a long position and close the trade when the faster MA line crosses back to beneath the slower MA line.
Trading signals can also be derived by the movement of market price in relation to Kaufman’s Adaptive Moving Average. If price crosses from below to above the KAMA line, that is a bullish (buy) signal. Conversely, price falling from above the KAMA line to below it is a bearish (sell) signal.
Popular Moving Averages
- The most popular type of moving averages are: SMA and EMA
- The most popular periods for moving averages are: 20, 50, and 200
Note 1: Some traders use Fibonacci periods: 8, 21, 34, 55, 89. 144, 233.
Note 2: Some traders also use the MACD periods: 12, 26.
Using Moving Averages
When examining some of these common uses for Moving Averages, keep in mind that that it is the trader’s discretion which Moving Average in particular they wish to use.
Basic Trend Identification
Using a Moving Average to confirm a trend in price is really one of the most basic, yet effecting ways of using the indicator. Consider that by design, Moving Averages “report” on what has already happened and that they also take into consideration a whole range of past events when calculating their formula. This is what makes a Moving Average such a good technical analysis tool for trend confirmations.
The general rules of thumb are as follows:
- A Long-Term Moving Average that is clearly on the upswing is confirmation of a Bullish Trend.
- A Long-Term Moving Average that is clearly on the downswing is confirmation of a Bearish Trend.
Because of the large amounts of data considered when calculating a Long-Term Moving Average, it takes a considerable amount of movement in the market to cause the MA to change its course. A Long-Term MA is not very susceptible to rapid price changes in regards to the overall trend.
Note: Many traders use the 200 SMA or 200 EMA as a way to check the overall trend.
Support and Resistance
Another fairly basic use for Moving Averages is identifying areas of support and resistance. Generally speaking, Moving averages can provide support in an uptrend and also they can provide resistance in a downtrend. While this can work for shorter term periods (20 days or less), the support and resistance provided by Moving Averages, can become even more readily apparent in longer term situations.
Crossovers require the use of two Moving Averages of varying length on the same chart. The two Moving averages should be of two different term lengths.
For example a 50 Day Simple Moving Average (medium-term) and a 200 Day Simple Moving Average (long-term) The signals or potential trading opportunities occur when the shorter term SMA crosses above or below the longer term SMA.
Bullish Crossover – Occurs when the shorter term SMA crosses above the longer term SMA. Also known as a Golden Cross.
Bearish Crossover – Occurs when the shorter term SMA crosses below the longer term SMA. Also known as a Dead Cross.
It is imperative however, that the trader realizes the inherent shortcomings in these signals. This is a system that is created by combining not just one but two lagging indicators. Both of these indicators react only to what has already happened and are not designed to make predictions. A system like this one definitely works best in a very strong trend. While in a strong trend, this system or a similar one can actually be quite valuable.
If you take the two Moving Averages setup that was discussed in the previous section and add in the third element of price, there is another type of setup called a Price Crossover.
With a Price Crossover you start with two Moving Averages of different term lengths (just like with the previously mentioned Crossover).
You basically use the longer term Moving Average to confirm long term trend. The signals then occur when Price crosses above or below the shorter term Moving Average going in the same direction of the main, longer term trend.
Bullish Price Crossover – Price crosses above the 50 SMA while the 50 SMA is above the 200 SMA. The 200 SMA is confirming the trend. Price and short term SMA are generating signals in the same direction as the trend.
Bearish Price Crossover – Price crosses below the 50 SMA while the 50 SMA is below the 200 SMA. The 200 SMA is confirming the trend. Price and short term SMA are generating signals in the same direction as the trend.
Parabolic Stop & Reverse (PSAR or SAR)
The parabolic SAR indicator, developed by J. Wells Wilder, is used by traders to determine trend direction and potential reversals in price. The indicator uses a trailing stop and reverse method called "SAR," or stop and reverse, to identify suitable exit and entry points. Traders also refer to the indicator as to the parabolic stop and reverse, parabolic SAR, or PSAR.
The parabolic SAR indicator appears on a chart as a series of dots, either above or below an asset’s price, depending on the direction the price is moving. A dot is placed below the price when it is trending upward, and above the price when it is trending downward.
A reversal occurs when these dots flip, but a reversal signal in the SAR does not necessarily mean a reversal in the price. A PSAR reversal only means that the price and indicator have crossed.
The parabolic indicator generates buy or sell signals when the position of the dots moves from one side of the asset’s price to the other. For example, a buy signal occurs when the dots move from above the price to below the price, while a sell signal occurs when the dots move from below the price to above the price.
Traders also use the PSAR dots to set trailing stop loss orders. For example, if the price is rising, and the PSAR is also rising, the PSAR can be used as a possible exit if long. If the price drops below the PSAR, exit the long trade.
The PSAR moves regardless of whether the price moves. This means that if the price is rising initially, but then moves sideways, the PSAR will keep rising despite the sideways movement in price. A reversal signal will be generated at some point, even if the price hasn’t dropped. The PSAR only needs to catch up to price to generate a reversal signal. For this reason, a reversal signal on the indicator doesn’t necessarily mean the price is reversing.
The parabolic indicator generates a new signal each time it moves to the opposite side of an asset’s price. This ensures a position in the market always, which makes the indicator appealing to active traders. The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
For best results, traders should use the parabolic indicator with other technical indicators that indicate whether a market is trending or not.
The Parabolic SAR vs. Moving Average (MA)
The PSAR and MAs both track the price and help show the trend, but they do it using different formulas.
An MA takes the average price over a selected number of periods and then plots it on the chart. The PSAR looks at extreme highs and lows and then applies an acceleration factor. These varying formulas look very different on the chart and will provide different analytical insights and trade signals.
Limitations Of Using The Parabolic SAR Indicator
The parabolic SAR is always on, and constantly generating signals, whether there is a quality trend or not. Therefore, many signals may be of poor quality because no significant trend is present or develops following a signal.
Reversal signals are also generated, eventually, regardless of whether the price actually reverses. This is because a reversal is generated when the SAR catches up to the price due to the acceleration factor in the formula. Therefore, a reversal signal may get a trader out of a trade even though the price hasn’t technically reversed.
Supertrend indicator was created by Olivier Seban to work on different time frames and all assets.
The Supertrend indicator is an overlay built into technical analysis charting platforms that change color based on the trend detected. It is a lagging indicator (trend-following indicator) that generates a buy or sell signal after a trend or reversal is underway. Supertrend works well in trending markets but could be inaccurate when the market is trading in a range.
For example, if the cryptocurrency closing price is found to be above the line, the indicator turns green, indicating that the trader should open a buy signal. Once the closing price falls and closes below the line, the Supertrend indicator turns red, showing that the trader should open a sell signal.
A Supertrend indicator is plotted on the price of an asset and relies on two parameters: a period for the average true range (ATR) and the multiplier. The ATR indicates the overall price range for an asset, offering an indication of its volatility. It provides information on the trading range and how far an asset has been moving. We’ll examine below how to calculate the indicator.
Characteristics Of The Supertrend Indicator
The Supertrend indicator is characterized by red and green lines. A green indicator represents a buy signal, while a red indicator is a sell signal. The point where the indicator line changes color is called the crossover point. You’ll gauge the market’s bullishness by the green line, the bearishness by the red, and determine support and resistance zones that can be used to determine the stop-loss levels.
Traders find that the Supertrend indicator works well in a trending market, but false signals are often generated when the market moves sideways.
To calculate the Supertrend, you need to input the period, which specifies the number of days of the average true range (ATR) used. Then the period is multiplied by the multiplier.
The ATR period defines the range of the asset price used to calculate the trend line. For example, when the ATR period is set to 10, the indicator analyzes the highs and lows of the last ten days. The combined high and low prices are divided by a value of 2, from which the product of the multiplier and the ATR value is subtracted for uptrends and added for downtrends.
Uptrends (shown in green): [(High Price + Low Price) ÷ 2] − (multiplier × ATR)
Downtrends (shown in red): [(High Price + Low Price) ÷ 2] + (multiplier × ATR)
The Supertrend indicator usually comes with a default setting of ATR 10 and a multiplier of 3. For different assets, there is no single best parameter to use. The ATR observed will depend on the cryptocurrency and the period watched during intraday trading.
Lowering the parameters of the Supertrend indicator increases the number of buys and sell signals. Most traders find themselves lowering the parameters when they want faster signals. However, lowering the parameters also increases the number of false signals, otherwise known as whipsaws, making confirming trends with other indicators absolutely essential.
Ichimoku Kinkō Hyō
Ichimoku Kinkō Hyō Overview
The Ichimoku Kinko Hyo, or Ichimoku for short, is a technical indicator that is used to gauge momentum along with future areas of support and resistance. The all-in-one technical indicator is comprised of five lines called the tenkan-sen, kijun-sen, senkou span A, senkou span B and chikou span.
Understanding Ichimoku Kinkō Hyō
The Ichimoku Kinko Hyo indicator was originally developed by a Japanese newspaper writer to combine various technical strategies into a single indicator that could be easily implemented and interpreted. In Japanese, "ichimoku" translates to "one look," meaning traders only have to take one look at the chart to determine momentum, support, and resistance.
Ichimoku may look very complicated to novice traders that haven’t seen it before, but the complexity quickly disappears with an understanding of what the various lines mean and why they are used.
The Ichimoku indicator is best used in conjunction with other forms of technical analysis despite its goal of being an all-in-one indicator.
Ichimoku Kinkō Hyō Interpretation
There are five key components to the Ichimoku indicator:
Tenkan-sen: The tenkan-sen, or conversion line, is calculated by adding the highest high and the lowest low over the past nine periods and then dividing the result by two. The resulting line represents a key support and resistance level, as well as a signal line for reversals.
Kijun-sen: The kijun-sen, or base line, is calculated by adding the highest high and the lowest low over the past 26 periods and dividing the result by two. The resulting line represents a key support and resistance level, a confirmation of a trend change, and can be used as a trailing stop-loss point.
Senkou Span A: The senkou span A, or leading span A, is calculated by adding the tenkan-sen and the kijun-sen, dividing the result by two, and then plotting the result 26 periods ahead. The resulting line forms one edge of the kumo – or cloud – that’s used to identify future areas of support and resistance.
Senkou Span B: The senkou span B, or leading span B, is calculated by adding the highest high and the lowest low over the past 52 periods, dividing it by two, and then plotting the result 26 periods ahead. The resulting line forms the other edge of the kumo that’s used to identify future areas of support and resistance.
Chikou Span: The chikou span, or lagging span, is the current period’s closing price plotted 26 days back on the chart. This line is used to show possible areas of support and resistance.
Using The Ichimoku Kinkō Hyō
The technical indicator shows relevant information at a glance by using averages.
The overall trend is up when the price is above the cloud, down when the price is below the cloud, and trendless or transitioning when the price is in the cloud.
When Leading Span A is rising and above Leading Span B, this helps to confirm the uptrend and the space between the lines is typically colored green. When Leading Span A is falling and below Leading Span B, this helps confirm the downtrend. The space between the lines is typically colored red in this case.
Traders will often use the Ichimoku Cloud as an area of support and resistance depending on the relative location of the price. The cloud provides support/resistance levels that can be projected into the future. This sets the Ichimoku Cloud apart from many other technical indicators that only provide support and resistance levels for the current date and time.
Traders should use the Ichimoku Cloud in conjunction with other technical indicators to maximize their risk-adjusted returns. For example, the indicator is often paired with the relative strength index (RSI), which can be used to confirm momentum in a certain direction. It’s also important to look at the bigger trends to see how the smaller trends fit within them. For example, during a very strong downtrend, the price may push into the cloud or slightly above it, temporarily, before falling again. Only focusing on the indicator would mean missing the bigger picture that the price was under strong longer-term selling pressure.
Crossovers are another way that the indicator can be used. Watch for the conversion line to move above the base line, especially when the price is above the cloud. This can be a powerful buy signal. One option is to hold the trade until the conversion line drops back below the base line. Any of the other lines could be used as exit points as well.
Limitations Of Using The Ichimoku Kinkō Hyō
The indicator can make a chart look busy with all the lines. To remedy this, most charting software allows certain lines to be hidden. For example, all of the lines can be hidden except for Leading Span A and Leading Span B, which create the cloud. Each trader needs to focus on which lines provide the most information, then consider hiding the rest if all of the lines are distracting.
Another limitation of the Ichimoku Cloud is that it is based on historical data. While two of these data points are plotted in the future, there is nothing in the formula that is inherently predictive. Averages are simply being plotted in the future.
The cloud can also become irrelevant for long periods of time, as the price remains way above or way below it. At times like these, the conversion line, the base line, and their crossovers become more important, as they generally stick closer to the price.