# tsmovavg

Moving average

`tsmovavg`

is not
recommended. Use `movavg`

instead. For more information, see Convert Financial Time Series Objects fints to Timetables.

## Syntax

## Description

`tsmovavg`

calculates the simple, exponential, triangular,
weighted, and modified moving average of a vector
or `fints`

object of data. For
information on working with financial time series
(`fints`

objects) data, see Working with Financial Time Series Objects.

returns the exponential weighted moving average
for financial time series object,
`output`

= tsmovavg(`tsobj`

,`'e'`

,`timeperiod`

)`tsobj`

. 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%.
```
Exponential Percentage = 2/(TIMEPER + 1)
or 2/(WINDOW_SIZE + 1)
```

.

returns the exponential weighted moving average
for a vector. The exponential moving average is a
weighted moving average, where
`output`

= tsmovavg(`vector`

,`'e'`

,`timeperiod`

,`dim`

)`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)`

).

returns the triangular moving average for
financial time series object,
`output`

= tsmovavg(`tsobj`

,`'t'`

,`numperiod`

)`tsobj`

. 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.

returns the triangular moving average for a
vector. The triangular moving average
double-smooths the data.
`output`

= tsmovavg(`vector`

,`'t'`

,`numperiod`

,`dim`

)`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.

returns the weighted moving average for the
financial time series object,
`output`

= tsmovavg(`tsobj`

,`'w'`

,`weights`

)`tsobj`

, 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.

returns the weighted moving average for the vector
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(`vector`

,`'w'`

,`weights`

,`dim`

)

returns the modified moving average for the
financial time series object,
`output`

= tsmovavg(`tsobj`

,`'m'`

,`numperiod`

)`tsobj`

. 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 for the
modified moving average are calculated by adding
the new price from the `tsobj`

input to the last modified moving average and then
subtracting that last modified moving average from
the sum.

returns the modified moving average for the
vector. The modified moving average is similar to
the simple moving average. Consider the argument
`output`

= tsmovavg(`vector`

,`'m'`

,`numperiod`

,`dim`

)`numperiod`

to be the lag of the
simple moving average. The first modified moving
average is calculated like a simple moving
average. Subsequent values for the modified moving
average are calculated by adding the new price
from the `vector`

input to the
last modified moving average and then subtracting
that last modified moving average from the
sum.

## Examples

## Input Arguments

## Output Arguments

## References

[1] Achelis, Steven B.
*Technical Analysis from A to
Z.* Second Edition. McGraw-Hill, 1995,
pp. 184–192.

## Version History

**Introduced before R2006a**