zenduck.me: R Moving Averages

Oktober 12, 2023Agustus 11, 2023 falma.u99 averages, moving, zenduckme

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Description

Calculate various moving averages (MA) of a series.

Usage

SMA(x, n = 10, ...)

EMA(x, n = 10, wilder = FALSE, ratio = NULL, ...)

DEMA(x, n = 10, v = 1, wilder = FALSE, ratio = NULL)

WMA(x, n = 10, wts = 1:n, ...)

EVWMA(price, volume, n = 10, ...)

ZLEMA(x, n = 10, ratio = NULL, ...)

VWAP(price, volume, n = 10, ...)

VMA(x, w, ratio = 1, ...)

HMA(x, n = 20, ...)

ALMA(x, n = 9, offset = 0.85, sigma = 6, ...)

Arguments

`x`	Price, volume, etc. series that is coercible to xts or matrix.
`n`	Number of periods to average over. Must be between 1 and `nrow(x)`, inclusive.
`...`	any other passthrough parameters
`wilder`	logical; if `TRUE`, a Welles Wilder type EMA will be calculated; see notes.
`ratio`	A smoothing/decay ratio. `ratio` overrides `wilder` in `EMA`, and provides additional smoothing in `VMA`.
`v`	The ‘volume factor’ (a number in [0,1]). See Notes.
`wts`	Vector of weights. Length of `wts` vector must equal the length of `x`, or `n` (the default).
`price`	Price series that is coercible to xts or matrix.
`volume`	Volume series that is coercible to xts or matrix, that corresponds to price series, or a constant. See Notes.
`w`	Vector of weights (in [0,1]) the same length as `x`.
`offset`	Percentile at which the center of the distribution should occur.
`sigma`	Standard deviation of the distribution.

Details

SMA calculates the arithmetic mean of the series over the past
n observations.

EMA calculates an exponentially-weighted mean, giving more weight to
recent observations. See Warning section below.

WMA is similar to an EMA, but with linear weighting if the length of
wts is equal to n. If the length of wts is equal to the
length of x, the WMA will use the values of wts as weights.

DEMA is calculated as: DEMA = (1 + v) * EMA(x,n) - EMA(EMA(x,n),n) * v (with the corresponding wilder and ratio
arguments).

EVWMA uses volume to define the period of the MA.

ZLEMA is similar to an EMA, as it gives more weight to recent
observations, but attempts to remove lag by subtracting data prior to
(n-1)/2 periods (default) to minimize the cumulative effect.

VWMA and VWAP calculate the volume-weighted moving average
price.

VMA calculate a variable-length moving average based on the absolute
value of w. Higher (lower) values of w will cause VMA
to react faster (slower).

HMA a WMA of the difference of two other WMAs, making it very
reponsive.

ALMA inspired by Gaussian filters. Tends to put less weight on most
recent observations, reducing tendency to overshoot.

Value

A object of the same class as x or price or a vector
(if try.xts fails) containing the columns:

SMA: Simple moving average.
EMA: Exponential moving average.
WMA: Weighted moving average.
DEMA: Double-exponential moving average.
EVWMA: Elastic, volume-weighted moving average.
ZLEMA: Zero lag exponential moving average.
VWMA: Volume-weighed moving average (same as VWAP).
VWAP: Volume-weighed average price (same as VWMA).
VWA: Variable-length moving average.
HMA: Hull moving average.
ALMA: Arnaud Legoux moving average.

Warning

Some indicators (e.g. EMA, DEMA, EVWMA, etc.) are
calculated using the indicators’ own previous values, and are therefore
unstable in the short-term. As the indicator receives more data, its output
becomes more stable. See example below.

Note

For EMA, wilder=FALSE (the default) uses an exponential
smoothing ratio of 2/(n+1), while wilder=TRUE uses Welles
Wilder’s exponential smoothing ratio of 1/n. The EMA result
is initialized with the n-period sample average at period n.
The exponential decay is applied from that point forward.

Since WMA can accept a weight vector of length equal to the length of
x or of length n, it can be used as a regular weighted moving
average (in the case wts=1:n) or as a moving average weighted by
volume, another indicator, etc.

Since DEMA allows adjusting v, it is technically Tim Tillson’s
generalized DEMA (GD). When v=1 (the default), the result is the
standard DEMA. When v=0, the result is a regular EMA. All other
values of v return the GD result. This function can be used to
calculate Tillson’s T3 indicator (see example below). Thanks to John Gavin
for suggesting the generalization.

For EVWMA, if volume is a series, n should be chosen so
the sum of the volume for n periods approximates the total number of
outstanding shares for the security being averaged. If volume is a
constant, it should represent the total number of outstanding shares for the
security being averaged.

Author(s)

Joshua Ulrich, Ivan Popivanov (HMA, ALMA)

References

The following site(s) were used to code/document this
indicator:
https://www.fmlabs.com/reference/ExpMA.htm
https://www.fmlabs.com/reference/WeightedMA.htm
https://www.fmlabs.com/reference/DEMA.htm
https://www.fmlabs.com/reference/T3.htm
https://www.linnsoft.com/techind/evwma-elastic-volume-weighted-moving-average
https://www.fmlabs.com/reference/ZeroLagExpMA.htm
https://www.fmlabs.com/reference/VIDYA.htm
https://www.traderslog.com/hullmovingaverage
https://web.archive.org/web/20180222085959/http://arnaudlegoux.com/

Examples


data(ttrc)
ema.20 <->

[Package TTR version 0.24.3 Index]