tsmovavg - Moving average

Syntax

output = tsmovavg(tsobj, 's', lag)             (Simple)
output = tsmovavg(vector, 's', lag, dim)
output = tsmovavg(tsobj, 'e', timeperiod)      (Exponential)
output = tsmovavg(vector, 'e', timeperiod, dim)
output = tsmovavg(tsobj, 't', numperiod)       (Triangular)
output = tsmovavg(vector, 't', numperiod, dim)
output = tsmovavg(tsobj, 'w', weights)         (Weighted)  
output = tsmovavg(vector, 'w', weights, dim) 
output = tsmovavg(tsobj, 'm', numperiod)       (Modified)
output = tsmovavg(vector, 'm', numperiod, dim)

Arguments

`tsobj`	Financial time series object.
`lag`	Number of previous data points.
`vector`	Row vector or row-oriented matrix. Each row is a set of observations.
`dim`	(Optional) Specifies dimension when `input` is a vector or matrix. Default = 2 (row-oriented matrix: each row is a variable, and each column is an observation). If `dim = 1`, input is assumed to be a column vector or column-oriented matrix (each column is a variable and each row an observation). `output` is identical in format to `input`.
`timeperiod`	Length of time period.
`numperiod`	Number of periods considered.
`weights`	Weights for each element in the window.

Description

output = tsmovavg(tsobj, 's', lag) and
output = tsmovavg(vector, 's', lag, dim) compute the simple moving average. lag indicates the number of previous data points used with the current data point when calculating the moving average.

output = tsmovavg(tsobj, 'e', timeperiod) and
output = tsmovavg(vector, 'e', timeperiod, dim) compute the exponential weighted moving average. The exponential moving average is a weighted moving average, where timeperiod specifies the time period. Exponential moving averages reduce the lag by applying more weight to recent prices. For example, a 10-period exponential moving average weights the most recent price by 18.18%. (2/(timeperiod + 1)).

output = tsmovavg(tsobj, 't', numperiod) and
output = tsmovavg(vector, 't', numperiod, dim) compute the triangular moving average. The triangular moving average double-smooths the data. tsmovavg calculates the first simple moving average with window width of ceil(numperiod + 1)/2. Then it calculates a second simple moving average on the first moving average with the same window size.

output = tsmovavg(tsobj, 'w', weights) and
output = tsmovavg(vector, 'w', weights, dim) calculate the weighted moving average by supplying weights for each element in the moving window. The length of the weight vector determines the size of the window. If larger weight factors are used for more recent prices and smaller factors for previous prices, the trend is more responsive to recent changes.

output = tsmovavg(tsobj, 'm', numperiod) and
output = tsmovavg(vector, 'm', numperiod, dim) calculate the modified moving average. The modified moving average is similar to the simple moving average. Consider the argument numperiod to be the lag of the simple moving average. The first modified moving average is calculated like a simple moving average. Subsequent values are calculated by adding the new price and subtracting the last average from the resulting sum.

References

Achelis, Steven B., Technical Analysis from A to Z, Second printing, McGraw-Hill, 1995, pp. 184-192.

tsmovavg - Moving average

Syntax

Arguments

Description

References

See Also