We show those averages as a line on the chart. The indicator below calculates two EMAs: a 10-bar and a 30-bar one, both based on close prices. Let’s look at how a full script can use Exponential Moving Averages (EMAs). Of course, compared with this custom function the built-in ta.ema() function is easier to work with. Sum := na(sum ) ? ta.sma(source, length) :Īlpha * source + ( 1 - alpha) * nz(sum ) In terms of code, here’s how the EMA is computed in Pine Script The formula for how TradingView calculates the Exponential Moving Average is To test for a cross above we use the ta.crossover() function, and the ta.crossunder() function finds out if there’s a cross below. To find out if a series crossed the Exponential Moving Average we use the ta.cross() function.And the ta.falling() function tells if the average decreased a particular number of bars in a row. We use the ta.rising() function to see if the average increased a certain number of consecutive bars. To see if the Exponential Moving Average rises or falls, we compare its current value with the previous bar value.Since TradingView charts don’t have a fixed time range, but can start on a different date depending on when we open the chart, repainting on past historical bars can happen. That makes the EMA sensitive to which price bar is the first that the script calculates on. To calculate the EMA, Pine Script uses data from the previous bar. The ta.ema() function can cause script repainting.That gives the highest weight to recent bars, while previous bars get significantly less weight the older they get. The weighting factors that the Exponential Moving Average uses decrease exponentially.If we don’t need the Momentum as a separate value, then to simplify the code we call ta.ema() directly on the ta.mom() function: // First calculate the momentum, then take its EMA valueĪvgMomentum = ta.ema( ta.mom( close, 10), 5) For example: // First calculate the momentum, then take its EMA value To smooth that indicator, we call ta.ema() on its value. Say we compute the Momentum with the ta.mom() function. If we, for example, want the one-bar difference in close price, we code: // Get the 20-bar exponential average of close price differencesĪverageCloseDiff = ta.ema( close - close, 20)īecause ta.ema() can work with any series of numerical values, it can also process data that another function calculated. It’s also possible to get the average from any numerical expression that returns a series of data. In that case we do: // Get the 10-bar EMA of volume In addition to bar prices, we can calculate an Exponential Moving Average of any series of values. Plot(slowAverage, color = color.fuchsia) For that we have the function run on the close variable, like so: // Calculate the 10-bar and 50-bar Exponential Moving Average (EMA) Let’s see how that works in practice.Ī common job is to calculate the Exponential Moving Average of closing prices. So there are two things ta.ema() needs: a series of values to process and the number of bars to calculate on. Ta.ema() returns a floating-point value with the exponential average of source for length bars back length is an integer that sets the moving average length in bars. This is the data we want the EMA to calculate on. source is the series of numerical values to process.This makes the average respond quicker to new prices than, say, a simple moving average. It uses exponential weighting to favourite recent over older data. In Pine Script, we calculate an Exponential Moving Average (EMA) with the ta.ema() functionĪn Exponential Moving Average adds more weight to recent data (which makes older data less important).
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