trdwma: Weighted Moving Average

In FRAPO: Financial Risk Modelling and Portfolio Optimisation with R

Description

Calculation of a right ended weighted moving average with weights according to weights.

Usage

trdwma(y, weights, trim = FALSE)

Arguments

y	Objects of classes: numeric, matrix, data.frame, ts, mts, and timeSeries are supported.
weights	Numeric, a vector containing the weights.
trim	Logical, if FALSE (the default) the first value is set to NA, otherwise the object is trimmed by the first obeservation.

Details

If the sum of the weights is greater than unity, a warning is issued.

Value

An object of the same class as y, containing the computed weighted moving averages.

Methods

y = "data.frame"

The calculation is applied per column of the data.frame and only if all columns are numeric.

y = "matrix"

The calculation is applied per column of the matrix.

y = "mts"

The calculation is applied per column of the mts object. The attributes are preserved and an object of the same class is returned.

y = "numeric"

Calculation of the es trend.

y = "timeSeries"

The calculation is applied per column of the timeSeries object and an object of the same class is returned.

y = "ts"

Calculation of the es trend. The attributes are preserved and an object of the same class is returned.

y = "xts"

Calculation of the es trend. The attributes are preserved and an object of the same class is returned.

y = "zoo"

Calculation of the es trend. The attributes are preserved and an object of the same class is returned.

Author(s)

Bernhard Pfaff

See Also

filter, trdbilson, trdbinary, trdhp, trdes, trdsma, capser

Examples

data(StockIndex)
y <- StockIndex[, "SP500"]
wma <- trdwma(y, weights = c(0.4, 0.3, 0.2, 0.1))
head(wma, 30)

Example output

Loading required package: cccp
Loading required package: Rglpk
Loading required package: slam
Using the GLPK callable library version 4.52
Loading required package: timeSeries
Loading required package: timeDate
Financial Risk Modelling and Portfolio Optimisation with R (version 0.4-1)

 [1]      NA      NA      NA 391.205 384.938 395.898 402.343 408.264 408.556
[10] 410.505 412.633 411.220 416.691 416.362 417.228 418.201 423.188 430.449
[19] 435.296 439.885 445.320 444.131 446.805 448.474 448.468 454.988 457.319
[28] 462.336 463.207 465.452

FRAPO documentation built on May 2, 2019, 6:33 a.m.