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An exponential moving average (EMA), also known as an exponentially weighted moving average (EWMA), [5] is a first-order infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero. This formulation is according to Hunter (1986). [6]
The idea is do a regular exponential moving average (EMA) calculation but on a de-lagged data instead of doing it on the regular data. Data is de-lagged by removing the data from "lag" days ago thus removing (or attempting to) the cumulative effect of the moving average.
Note that the distribution's mode will lie with p N-2 's weight, i.e. in the graph above p 8 carries the highest weighting. An N of 1 is invalid. The easiest way to calculate the triple EMA based on successive values is just to apply the EMA three times, creating single-, then double-, then triple-smoothed series. The triple EMA can also be expressed directly in terms of the prices as below ...
The Double Exponential Moving Average (DEMA) indicator was introduced in January 1994 by Patrick G. Mulloy, in an article in the "Technical Analysis of Stocks & Commodities" magazine: "Smoothing Data with Faster Moving Averages" [1] [2] It attempts to remove the inherent lag associated with Moving Averages by placing more weight on recent values.
As plain crossovers can occur frequently, one typically waits for crossovers occurring together with an extreme pullback, after a peak or trough in the %D line. If price volatility is high, an exponential moving average of the %D indicator may be taken, which tends to smooth out rapid fluctuations in price.
The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. The name suggests this is achieved by applying a triple exponential smoothing which is not the case.
Momentum is the change in an N-day simple moving average (SMA) between yesterday and today, with a scale factor N+1, i.e. + = This is the slope or steepness of the SMA line, like a derivative. This relationship is not much discussed generally, but it's of interest in understanding the signals from the indicator.
The SMA value of a set of values (or a continuous-time waveform) is the normalized integral of the original values. [1] [2]In the case of a set of n values {,, …,} matching a time length T, the SMA