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movavg

Moving average of a financial time series

movavg is updated to accept data input as a matrix, table, or timetable.

The syntax for movavg has changed. There is no longer support for the input arguments Lead and Lag, only a single windowSize is supported, and there is only one output argument (ma). If you want to compute the leading and lagging moving averages, you need to run movavg twice and adjust the windowSize.

Description

example

ma = movavg(Data,type,windowSize) computes the moving average (MA) of a financial time series.

example

ma = movavg(___,Initialpoints) adds an optional argument for Initialpoints.

example

ma = movavg(Data,type,weights) computes the moving average (MA) of a financial time series using a 'custom' type and weights.

example

ma = movavg(___,Initialpoints) adds an optional argument for Initialpoints.

Examples

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Load the file SimulatedStock.mat, which provides a timetable (TMW) for financial data.

load SimulatedStock.mat
type = 'linear';
windowSize = 14;
ma = movavg(TMW_CLOSE,type,windowSize)
ma = 1000×1

  100.2500
  100.3433
  100.8700
  100.4916
   99.9937
   99.3603
   98.8769
   98.6364
   98.4348
   97.8491
      ⋮

Load the file SimulatedStock.mat, which provides a timetable (TMW) for financial data.

load SimulatedStock.mat 
type = 'linear';
malag=movavg(TMW_CLOSE,type,20) % Lagging moving average
malag = 1000×1

  100.2500
  100.3423
  100.8574
  100.4943
  100.0198
   99.4230
   98.9728
   98.7509
   98.5688
   98.0554
      ⋮

malead=movavg(TMW_CLOSE,type,3) % Leading moving average
malead = 1000×1

  100.2500
  100.3580
  101.0900
  100.4300
   99.3183
   97.8217
   97.0833
   97.1950
   97.4133
   96.1133
      ⋮

Plot the leading and lagging moving averages.

plot(TMW_CLOSE(1:100))
hold on
plot(malead(1:100))
plot(malag(1:100))
hold off
legend('Actual','Lead','Lag')

Figure contains an axes object. The axes object contains 3 objects of type line. These objects represent Actual, Lead, Lag.

Input Arguments

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Data for a financial series, specified as a column-oriented matrix, table, or timetable. Timetables and tables must contain variables with only a numeric type.

Data Types: double | table | timetable

Type of moving average to compute, specified as a character vector or string with an associated value.

Data Types: char | string

Number of observations of the input series to include in moving average, specified as a scalar positive integer. The observations include (windowSize - 1) previous data points and the current data point.

Note

The windowSize argument applies only to moving averages whose type is 'simple', 'square-root', 'linear', 'square', 'exponential', 'triangular', or 'modified'.

Data Types: double

Custom weights used to compute the moving average, specified as a vector.

Note

The length of weights (N) determines the size of the moving average window (windowSize). The weights argument applies only to a 'custom' type of moving average.

To compute moving average with custom weights, the weights (w) are first normalized such that they sum to one:

W(i) = w(i)/sum(w), for i = 1,2,...,N

The normalized weights (W) are then used to form the N-point weighted moving average (y) of the input Data (x):

y(t) = W(1)*x(t) + W(2)*x(t-1) + ... + W(N)*x(t-N)

The initial moving average values within the window size are then adjusted according to the method specified in the name-value pair argument Initialpoints.

Data Types: double

(Optional) Indicates how the moving average is calculated at initial points (before there is enough data to fill the window), specified as a character vector or string using one of the following values:

  • 'shrink' - Initializes the moving average such that the initial points include only observed data

  • 'zero' - Initializes the initial points with 0

  • 'fill' - Fills initial points with NaNs

Note

The Initialpoints argument applies to all type specifications except for the 'exponential' and 'modified' options.

Data Types: char | string

Output Arguments

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Moving average series, returned with the same number of rows (M) and the same type (matrix, table, or timetable) as the input Data.

References

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

Introduced before R2006a