⚠️ Warning: This is a draft ⚠️

This means it might contain formatting issues, incorrect code, conceptual problems, or other severe issues.

If you want to help to improve and eventually enable this page, please fork RosettaGit's repository and open a merge request on GitHub.

Computing the [[wp:Moving_average#Simple_moving_average|simple moving average]] of a series of numbers.

Create a [[wp:Stateful|stateful]] function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.

A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.

It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().

The word ''stateful'' in the task description refers to the need for SMA() to remember certain information between calls to it:

• The period, P
• An ordered container of at least the last P numbers from each of its individual calls.

''Stateful'' also means that successive calls to I(), the initializer, should return separate routines that do ''not'' share saved state so they could be used on two independent streams of data.

Pseudo-code for an implementation of SMA is:

```
function SMA(number: N):
stateful integer: P
stateful list:    stream
number:           average

stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average

```

## 11l

{{trans|D}}

```T SMA
[Float] data
sum = 0.0
index = 0
n_filled = 0
Int period

F (period)
.period = period
.data = [0.0] * period

.sum += v - .data[.index]
.data[.index] = v
.index = (.index + 1) % .period
.n_filled = min(.period, .n_filled + 1)
R .sum / .n_filled

V sma3 = SMA(3)
V sma5 = SMA(5)

L(e) [1, 2, 3, 4, 5, 5, 4, 3, 2, 1]
print(‘Added #., sma(3) = #.6, sma(5) = #.6’.format(e, sma3.add(e), sma5.add(e)))
```

{{out}}

```
Added 1, sma(3) = 1.000000, sma(5) = 1.000000
Added 2, sma(3) = 1.500000, sma(5) = 1.500000
Added 3, sma(3) = 2.000000, sma(5) = 2.000000
Added 4, sma(3) = 3.000000, sma(5) = 2.500000
Added 5, sma(3) = 4.000000, sma(5) = 3.000000
Added 5, sma(3) = 4.666667, sma(5) = 3.800000
Added 4, sma(3) = 4.666667, sma(5) = 4.200000
Added 3, sma(3) = 4.000000, sma(5) = 4.200000
Added 2, sma(3) = 3.000000, sma(5) = 3.800000
Added 1, sma(3) = 2.000000, sma(5) = 3.000000

```

## 360 Assembly

{{trans|PL/I}}

```*        Averages/Simple moving average  26/08/2015
AVGSMA   CSECT
USING  AVGSMA,R12
LR     R12,R15
ST     R14,SAVER14
ZAP    II,=P'0'           ii=0
LA     R7,1
LH     R3,NA
SRA    R3,1               na/2
LOOPA    CR     R7,R3              do i=1 to na/2
BH     ELOOPA
AP     II,=P'1000'        ii=ii+1000
LR     R1,R7              i
MH     R1,=H'6'
LA     R4,A-6(R1)
MVC    0(6,R4),II         a(i)=ii
LH     R1,NA              na
SR     R1,R7              -i
MH     R1,=H'6'
LA     R4,A(R1)
MVC    0(6,R4),II         a(na+1-i)=ii
LA     R7,1(R7)
B      LOOPA
ELOOPA   XPRNT  =CL30' n     sma3        sma5       ',30
XPRNT  =CL30' ----- ----------- -----------',30
LA     R7,1               i=1
LOOP     CH     R7,NA              do i=1 to na
BH     RETURN
STH    R7,N               n=i
XDECO  R7,C               i
MVC    BUF+1(5),C+7
MVC    P,=H'3'            p=3
BAL    R14,SMA
ED     C(13),SS           sma(3,i)
MVC    BUF+7(11),C+2
MVC    P,=H'5'            p=5
BAL    R14,SMA
ED     C(13),SS           sma(5,i)
MVC    BUF+19(11),C+2
XPRNT  BUF,30             output i,sma3,sma5
LA     R7,1(R7)
B      LOOP
*        *****  sub sma(p,n) returns(PL6)
SMA      LH     R5,N
SH     R5,P
A      R5,=F'1'           ia=n-p+1
C      R5,=F'1'
BH     OKIA
LA     R5,1               ia=1
OKIA     LH     R6,NA              ib=na
CH     R6,N
BL     OKIB
LH     R6,N               ib=n
OKIB     ZAP    II,=P'0'           ii=0
ZAP    SS,=P'0'           ss=0
LR     R3,R5              k=ia
LOOPK    CR     R3,R6              do k=ia to ib
BH     ELOOPK
AP     II,=P'1'           ii=ii+1
LR     R1,R3
MH     R1,=H'6'
LA     R4,A-6(R1)
MVC    C(6),0(R4)         ss=ss+a(k)
AP     SS,C(6)
LA     R3,1(R3)
B      LOOPK
ELOOPK   ZAP    C,SS
DP     C,II
ZAP    SS,C(10)           ss=ss/ii
BR     R14
RETURN   L      R14,SAVER14        restore caller address
XR     R15,R15
BR     R14
SAVER14  DS     F
NN       EQU    10
NA       DC     AL2(NN)
A        DS     (NN)PL6
II       DS     PL6
SS       DS     PL6
P        DS     H
N        DS     H
C        DS     CL16
BUF      DC     CL30'                              '  buffer
EDMASK   DC     X'4020202020202021204B202020'  CL13
YREGS
END    AVGSMA
```

{{out}}

```
n     sma3        sma5
----- ----------- -----------
1       1.000       1.000
2       1.500       1.500
3       2.000       2.000
4       3.000       2.500
5       4.000       3.000
6       4.666       3.800
7       4.666       4.200
8       4.000       4.200
9       3.000       3.800
10       2.000       3.000

```

```generic
Max_Elements : Positive;
type Number is digits <>;
package Moving is
procedure Add_Number (N : Number);
function Moving_Average (N : Number) return Number;
function Get_Average return Number;
end Moving;
```

```with Ada.Containers.Vectors;

package body Moving is

package Number_Vectors is new Ada.Containers.Vectors
(Element_Type => Number,
Index_Type   => Natural);

Current_List : Number_Vectors.Vector := Number_Vectors.Empty_Vector;

procedure Add_Number (N : Number) is
begin
if Natural (Current_List.Length) >= Max_Elements then
Current_List.Delete_First;
end if;
Current_List.Append (N);

function Get_Average return Number is
Average : Number := 0.0;
procedure Sum (Position : Number_Vectors.Cursor) is
begin
Average := Average + Number_Vectors.Element (Position);
end Sum;
begin
Current_List.Iterate (Sum'Access);
if Current_List.Length > 1 then
Average := Average / Number (Current_List.Length);
end if;
return Average;
end Get_Average;

function Moving_Average (N : Number) return Number is
begin
return Get_Average;
end Moving_Average;

end Moving;
```

```with Ada.Text_IO;
with Moving;
procedure Main is
package Three_Average is new Moving (Max_Elements => 3, Number => Float);
package Five_Average is new Moving (Max_Elements => 5, Number => Float);
begin
for I in 1 .. 5 loop
Ada.Text_IO.Put_Line ("Inserting" & Integer'Image (I) &
" into max-3: " & Float'Image (Three_Average.Moving_Average (Float (I))));
Ada.Text_IO.Put_Line ("Inserting" & Integer'Image (I) &
" into max-5: " & Float'Image (Five_Average.Moving_Average (Float (I))));
end loop;
for I in reverse 1 .. 5 loop
Ada.Text_IO.Put_Line ("Inserting" & Integer'Image (I) &
" into max-3: " & Float'Image (Three_Average.Moving_Average (Float (I))));
Ada.Text_IO.Put_Line ("Inserting" & Integer'Image (I) &
" into max-5: " & Float'Image (Five_Average.Moving_Average (Float (I))));
end loop;
end Main;
```

{{out}}

```Inserting 1 into max-3:  1.00000E+00
Inserting 1 into max-5:  1.00000E+00
Inserting 2 into max-3:  1.50000E+00
Inserting 2 into max-5:  1.50000E+00
Inserting 3 into max-3:  2.00000E+00
Inserting 3 into max-5:  2.00000E+00
Inserting 4 into max-3:  3.00000E+00
Inserting 4 into max-5:  2.50000E+00
Inserting 5 into max-3:  4.00000E+00
Inserting 5 into max-5:  3.00000E+00
Inserting 5 into max-3:  4.66667E+00
Inserting 5 into max-5:  3.80000E+00
Inserting 4 into max-3:  4.66667E+00
Inserting 4 into max-5:  4.20000E+00
Inserting 3 into max-3:  4.00000E+00
Inserting 3 into max-5:  4.20000E+00
Inserting 2 into max-3:  3.00000E+00
Inserting 2 into max-5:  3.80000E+00
Inserting 1 into max-3:  2.00000E+00
Inserting 1 into max-5:  3.00000E+00
```

## ALGOL 68

{{trans|C}}

{{works with|ALGOL 68|Standard - no extensions to language used}}

{{works with|ALGOL 68G|Any - tested with release [http://sourceforge.net/projects/algol68/files/algol68g/algol68g-1.18.0/algol68g-1.18.0-9h.tiny.el5.centos.fc11.i386.rpm/download 1.18.0-9h.tiny]}}

Note: This following code is a direct translation of the [[Average/Simple_moving_average#C|C]] code sample. It mimics C's var_list implementation, and so it probably isn't the most natural way of dong this actual task in '''ALGOL 68'''.

```MODE SMAOBJ  = STRUCT(
LONG REAL sma,
LONG REAL sum,
INT period,
REF[]LONG REAL values,
INT lv
);

MODE SMARESULT = UNION (
REF SMAOBJ # handle #,
LONG REAL # sma #,
REF[]LONG REAL # values #
);

MODE SMANEW = INT,
SMAFREE = STRUCT(REF SMAOBJ free obj),
SMAVALUES = STRUCT(REF SMAOBJ values obj),
SMAADD = STRUCT(REF SMAOBJ add obj, LONG REAL v),
SMAMEAN = STRUCT(REF SMAOBJ mean obj, REF[]LONG REAL v);

MODE ACTION = UNION ( SMANEW, SMAFREE, SMAVALUES, SMAADD, SMAMEAN );

PROC sma = ([]ACTION action)SMARESULT:
(
SMARESULT result;
REF SMAOBJ obj;
LONG REAL v;

FOR i FROM LWB action TO UPB action DO
CASE action[i] IN
(SMANEW period):( # args: INT period #
HEAP SMAOBJ handle;
sma OF handle := 0.0;
period OF handle := period;
values OF handle := HEAP [period OF handle]LONG REAL;
lv OF handle := 0;
sum OF handle := 0.0;
result := handle
),
(SMAFREE args):( # args: REF SMAOBJ free obj #
free obj OF args := REF SMAOBJ(NIL) # let the garbage collector do it's job #
),
(SMAVALUES args):( # args: REF SMAOBJ values obj #
result := values OF values obj OF args
),
(SMAMEAN args):( # args: REF SMAOBJ mean obj #
result := sma OF mean obj OF args
),
(SMAADD args):( # args: REF SMAOBJ add obj, LONG REAL v #
obj := add obj OF args;
v := v OF args;
IF lv OF obj < period OF obj THEN
(values OF obj)[lv OF obj+:=1] := v;
sum OF obj +:= v;
sma OF obj := sum OF obj / lv OF obj
ELSE
sum OF obj -:= (values OF obj)[ 1+ lv OF obj MOD period OF obj];
sum OF obj +:= v;
sma OF obj := sum OF obj / period OF obj;
(values OF obj)[ 1+ lv OF obj  MOD  period OF obj ] := v; lv OF obj+:=1
FI;
result := sma OF obj
)
OUT
SKIP
ESAC
OD;
result
);

[]LONG REAL v = ( 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 );

main: (
INT i;

REF SMAOBJ h3 := ( sma(SMANEW(3)) | (REF SMAOBJ obj):obj );
REF SMAOBJ h5 := ( sma(SMANEW(5)) | (REF SMAOBJ obj):obj );

FOR i FROM LWB v TO UPB v DO
printf((\$"next number "g(0,6)", SMA_3 = "g(0,6)", SMA_5 = "g(0,6)l\$,
v[i], (sma(SMAADD(h3, v[i]))|(LONG REAL r):r), ( sma(SMAADD(h5, v[i])) | (LONG REAL r):r )
))
OD#;

sma(SMAFREE(h3));
sma(SMAFREE(h5))
#
)
```

{{out}}

```
next number 1.000000, SMA_3 = 1.000000, SMA_5 = 1.000000
next number 2.000000, SMA_3 = 1.500000, SMA_5 = 1.500000
next number 3.000000, SMA_3 = 2.000000, SMA_5 = 2.000000
next number 4.000000, SMA_3 = 3.000000, SMA_5 = 2.500000
next number 5.000000, SMA_3 = 4.000000, SMA_5 = 3.000000
next number 5.000000, SMA_3 = 4.666667, SMA_5 = 3.800000
next number 4.000000, SMA_3 = 4.666667, SMA_5 = 4.200000
next number 3.000000, SMA_3 = 4.000000, SMA_5 = 4.200000
next number 2.000000, SMA_3 = 3.000000, SMA_5 = 3.800000
next number 1.000000, SMA_3 = 2.000000, SMA_5 = 3.000000

```

## AutoHotkey

ahk forum: [http://www.autohotkey.com/forum/post-276695.html#276695 discussion] For Integers:

```MsgBox % MovingAverage(5,3)  ; 5, averaging length <- 3
MsgBox % MovingAverage(1)    ; 3
MsgBox % MovingAverage(-3)   ; 1
MsgBox % MovingAverage(8)    ; 2
MsgBox % MovingAverage(7)    ; 4

MovingAverage(x,len="") {    ; for integers (faster)
Static
Static sum:=0, n:=0, m:=10 ; default averaging length = 10
If (len>"")                ; non-blank 2nd parameter: set length, reset
sum := n := i := 0, m := len
If (n < m)                 ; until the buffer is not full
sum += x, n++           ;   keep summing data
Else                       ; when buffer is full
sum += x-v%i%           ;   add new, subtract oldest
v%i% := x, i := mod(i+1,m) ; remember last m inputs, cycle insertion point
Return sum/n
}
```

For floating point numbers:

```MovingAverage(x,len="") {    ; for floating point numbers
Static
Static n:=0, m:=10         ; default averaging length = 10
If (len>"")                ; non-blank 2nd parameter: set length, reset
n := i := 0, m := len
n += n < m, sum := 0
v%i% := x, i := mod(i+1,m) ; remember last m inputs, cycle insertion point
Loop %n%                   ; recompute sum to avoid error accumulation
j := A_Index-1, sum += v%j%
Return sum/n
}
```

## AWK

```#!/usr/bin/awk -f
# Moving average over the first column of a data file
BEGIN {
P = 5;
}

{
x = \$1;
i = NR % P;
MA += (x - Z[i]) / P;
Z[i] = x;
print MA;
}
```

## BBC BASIC

{{works with|BBC BASIC for Windows}}

```      MAXPERIOD = 10
FOR n = 1 TO 5
PRINT "Number = ";n TAB(12) " SMA3 = ";FNsma(n,3) TAB(30) " SMA5 = ";FNsma(n,5)
NEXT
FOR n = 5 TO 1 STEP -1
PRINT "Number = ";n TAB(12) " SMA3 = ";FNsma(n,3) TAB(30) " SMA5 = ";FNsma(n,5)
NEXT
END

DEF FNsma(number, period%)
PRIVATE nums(), accum(), index%(), window%()
DIM nums(MAXPERIOD,MAXPERIOD), accum(MAXPERIOD)
DIM index%(MAXPERIOD), window%(MAXPERIOD)
accum(period%) += number - nums(period%,index%(period%))
nums(period%,index%(period%)) = number
index%(period%) = (index%(period%) + 1) MOD period%
IF window%(period%)<period% window%(period%) += 1
= accum(period%) / window%(period%)
```

{{out}}

```
Number = 1   SMA3 = 1          SMA5 = 1
Number = 2   SMA3 = 1.5        SMA5 = 1.5
Number = 3   SMA3 = 2          SMA5 = 2
Number = 4   SMA3 = 3          SMA5 = 2.5
Number = 5   SMA3 = 4          SMA5 = 3
Number = 5   SMA3 = 4.66666667 SMA5 = 3.8
Number = 4   SMA3 = 4.66666667 SMA5 = 4.2
Number = 3   SMA3 = 4          SMA5 = 4.2
Number = 2   SMA3 = 3          SMA5 = 3.8
Number = 1   SMA3 = 2          SMA5 = 3

```

## Bracmat

```( ( I
=   buffer
.   (new\$=):?freshEmptyBuffer
&
' ( buffer avg
.   ( avg
=   L S n
.   0:?L:?S
&   whl
' ( !arg:%?n ?arg
& !n+!S:?S
& 1+!L:?L
)
& (!L:0&0|!S*!L^-1)
)
& (buffer=\$freshEmptyBuffer)
& !arg !(buffer.):?(buffer.)
& ( !(buffer.):?(buffer.) [(\$arg) ?
|
)
& avg\$!(buffer.)
)
)
=   len w
.   @(!arg:? [?len)
& @("     ":? [!len ?w)
& !w !arg
)
& I\$3:(=?sma3)
& I\$5:(=?sma5)
& 1 2 3 4 5 5 4 3 2 1:?K
&   whl
' ( !K:%?k ?K
&   out
\$ (str\$(!k " - sma3:" pad\$(sma3\$!k) "  sma5:" pad\$(sma5\$!k)))
)
);
```

{{out}}

```1 - sma3:    1  sma5:    1
2 - sma3:  3/2  sma5:  3/2
3 - sma3:    2  sma5:    2
4 - sma3:    3  sma5:  5/2
5 - sma3:    4  sma5:    3
5 - sma3: 14/3  sma5: 19/5
4 - sma3: 14/3  sma5: 21/5
3 - sma3:    4  sma5: 21/5
2 - sma3:    3  sma5: 19/5
1 - sma3:    2  sma5:    3
```

## Brat

Object version

```
SMA = object.new

SMA.init = { period |
my.period = period
my.list = []
my.average = 0
}

SMA.prototype.add = { num |
true? my.list.length >= my.period
{ my.list.deq }

my.list << num
my.average = my.list.reduce(:+) / my.list.length
}

sma3 = SMA.new 3
sma5 = SMA.new 5
[1, 2, 3, 4, 5, 5, 4, 3, 2, 1].each { n |
p n, " - SMA3: ", sma3.add(n), " SMA5: ", sma5.add(n)
}
```

Function version

```sma = { period |
list = []

{ num |
true? list.length >= period
{ list.deq }

list << num
list.reduce(:+) / list.length
}
}

sma3 = sma 3
sma5 = sma 5
[1, 2, 3, 4, 5, 5, 4, 3, 2, 1].each { n |
p n, " - SMA3: ", sma3(n), " SMA5: ", sma5(n)
}
```

{{out}}

```1 - SMA3: 1 SMA5: 1
2 - SMA3: 1.5 SMA5: 1.5
3 - SMA3: 2 SMA5: 2
4 - SMA3: 3 SMA5: 2.5
5 - SMA3: 4 SMA5: 3
5 - SMA3: 4.6666666666667 SMA5: 3.8
4 - SMA3: 4.6666666666667 SMA5: 4.2
3 - SMA3: 4 SMA5: 4.2
2 - SMA3: 3 SMA5: 3.8
1 - SMA3: 2 SMA5: 3
```

## C

```#include <stdio.h>
#include <stdlib.h>
#include <stdarg.h>

typedef struct sma_obj {
double sma;
double sum;
int period;
double *values;
int lv;
} sma_obj_t;

typedef union sma_result {
sma_obj_t *handle;
double sma;
double *values;
} sma_result_t;

enum Action { SMA_NEW, SMA_FREE, SMA_VALUES, SMA_ADD, SMA_MEAN };
sma_result_t sma(enum Action action, ...)
{
va_list vl;
sma_result_t r;
sma_obj_t *o;
double v;

va_start(vl, action);
switch(action) {
case SMA_NEW: // args: int period
r.handle = malloc(sizeof(sma_obj_t));
r.handle->sma = 0.0;
r.handle->period = va_arg(vl, int);
r.handle->values = malloc(r.handle->period * sizeof(double));
r.handle->lv = 0;
r.handle->sum = 0.0;
break;
case SMA_FREE: // args: sma_obj_t *handle
r.handle = va_arg(vl, sma_obj_t *);
free(r.handle->values);
free(r.handle);
r.handle = NULL;
break;
case SMA_VALUES: // args: sma_obj_t *handle
o = va_arg(vl, sma_obj_t *);
r.values = o->values;
break;
case SMA_MEAN: // args: sma_obj_t *handle
o = va_arg(vl, sma_obj_t *);
r.sma = o->sma;
break;
case SMA_ADD: // args: sma_obj_t *handle, double value
o = va_arg(vl, sma_obj_t *);
v = va_arg(vl, double);
if ( o->lv < o->period ) {
o->values[o->lv++] = v;
o->sum += v;
o->sma = o->sum / o->lv;
} else {
o->sum -= o->values[ o->lv % o->period];
o->sum += v;
o->sma = o->sum / o->period;
o->values[ o->lv % o->period ] = v; o->lv++;
}
r.sma = o->sma;
break;
}
va_end(vl);
return r;
}
```
```double v[] = { 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 };

int main()
{
int i;

sma_obj_t *h3 = sma(SMA_NEW, 3).handle;
sma_obj_t *h5 = sma(SMA_NEW, 5).handle;

for(i=0; i < sizeof(v)/sizeof(double) ; i++) {
printf("next number %lf, SMA_3 = %lf, SMA_5 = %lf\n",
v[i], sma(SMA_ADD, h3, v[i]).sma, sma(SMA_ADD, h5, v[i]).sma);
}

sma(SMA_FREE, h3);
sma(SMA_FREE, h5);
return 0;
}
```

## C++

```
#include <iostream>
#include <stddef.h>
#include <assert.h>

using std::cout;
using std::endl;

class SMA {
public:
SMA(unsigned int period) :
period(period), window(new double[period]), head(NULL), tail(NULL),
total(0) {
assert(period >= 1);
}
~SMA() {
delete[] window;
}

// Adds a value to the average, pushing one out if nescessary
void add(double val) {
// Special case: Initialization
if (head == NULL) {
inc(tail);
total = val;
return;
}

// Were we already full?
if (head == tail) {
// Fix total-cache
// Make room
}

// Write the value in the next spot.
*tail = val;
inc(tail);

// Update our total-cache
total += val;
}

// Returns the average of the last P elements added to this SMA.
// If no elements have been added yet, returns 0.0
double avg() const {
ptrdiff_t size = this->size();
if (size == 0) {
return 0; // No entries => 0 average
}
return total / (double) size; // Cast to double for floating point arithmetic
}

private:
unsigned int period;
double * window; // Holds the values to calculate the average of.

// Logically, head is before tail
double * head; // Points at the oldest element we've stored.
double * tail; // Points at the newest element we've stored.

double total; // Cache the total so we don't sum everything each time.

// Bumps the given pointer up by one.
// Wraps to the start of the array if needed.
void inc(double * & p) {
if (++p >= window + period) {
p = window;
}
}

// Returns how many numbers we have stored.
ptrdiff_t size() const {
if (head == NULL)
return 0;
if (head == tail)
return period;
return (period + tail - head) % period;
}
};

int main(int argc, char * * argv) {
SMA foo(3);
SMA bar(5);

int data[] = { 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 };
for (int * itr = data; itr < data + 10; itr++) {
cout << "Added " << *itr << " avg: " << foo.avg() << endl;
}
cout << endl;
for (int * itr = data; itr < data + 10; itr++) {
cout << "Added " << *itr << " avg: " << bar.avg() << endl;
}

return 0;
}

```

## C#

{{works with|C sharp|C#|3}}

```using System;
using System.Collections.Generic;
using System.Linq;

namespace SMA {
class Program {
static void Main(string[] args) {
var nums = Enumerable.Range(1, 5).Select(n => (double)n);
nums = nums.Concat(nums.Reverse());

var sma3 = SMA(3);
var sma5 = SMA(5);

foreach (var n in nums) {
Console.WriteLine("{0}    (sma3) {1,-16} (sma5) {2,-16}", n, sma3(n), sma5(n));
}
}

static Func<double, double> SMA(int p) {
Queue<double> s = new Queue<double>(p);
return (x) => {
if (s.Count >= p) {
s.Dequeue();
}
s.Enqueue(x);
return s.Average();
};
}
}
}
```

{{out}}

```
1    (sma3) 1                (sma5) 1
2    (sma3) 1.5              (sma5) 1.5
3    (sma3) 2                (sma5) 2
4    (sma3) 3                (sma5) 2.5
5    (sma3) 4                (sma5) 3
5    (sma3) 4.66666666666667 (sma5) 3.8
4    (sma3) 4.66666666666667 (sma5) 4.2
3    (sma3) 4                (sma5) 4.2
2    (sma3) 3                (sma5) 3.8
1    (sma3) 2                (sma5) 3

```

## Clojure

This version uses a persistent queue to hold the most recent ''p'' values. Each function returned from ''init-moving-average'' has its state in an atom holding a queue value.

```(import '[clojure.lang PersistentQueue])

(defn enqueue-max [q p n]
(let [q (conj q n)]
(if (<= (count q) p) q (pop q))))

(defn avg [coll] (/ (reduce + coll) (count coll)))

(defn init-moving-avg [p]
(let [state (atom PersistentQueue/EMPTY)]
(fn [n]
(avg (swap! state enqueue-max p n)))))
```

## CoffeeScript

```
I = (P) ->
# The cryptic name "I" follows the problem description;
# it returns a function that computes a moving average
# of successive values over the period P, using closure
# variables to maintain state.
cq = circular_queue(P)
num_elems = 0
sum = 0

SMA = (n) ->
sum += n
if num_elems < P
num_elems += 1
sum / num_elems
else
old = cq.replace(n)
sum -= old
sum / P

circular_queue = (n) ->
# queue that only ever stores up to n values;
# Caller shouldn't call replace until n values
# have been added.
i = 0
arr = []

arr.push elem
replace: (elem) ->
# return value whose age is "n"
old_val = arr[i]
arr[i] = elem
i = (i + 1) % n
old_val

# The output of the code below should convince you that
# calling I multiple times returns functions with independent
# state.
sma3 = I(3)
sma7 = I(7)
sma11 = I(11)
for i in [1..10]
console.log i, sma3(i), sma7(i), sma11(i)

```

{{out}}

```
> coffee moving_average.coffee
1 1 1 1
2 1.5 1.5 1.5
3 2 2 2
4 3 2.5 2.5
5 4 3 3
6 5 3.5 3.5
7 6 4 4
8 7 5 4.5
9 8 6 5
10 9 7 5.5

```

## Common Lisp

This implementation uses a circular list to store the numbers within the window; at the beginning of each iteration pointer refers to the list cell which holds the value just moving out of the window and to be replaced with the just-added value.

```(defun simple-moving-average (period &aux
(sum 0) (count 0) (values (make-list period)) (pointer values))
(setf (rest (last values)) values)  ; construct circularity
(lambda (n)
(when (first pointer)
(decf sum (first pointer)))     ; subtract old value
(incf sum n)                      ; add new value
(incf count)
(setf (first pointer) n)
(setf pointer (rest pointer))     ; advance pointer
(/ sum (min count period))))
```

## D

### Using a Closure

Currently this `sma` can't be @nogc because it allocates a closure on the heap. Some escape analysis could remove the heap allocation.

```import std.stdio, std.traits, std.algorithm;

auto sma(T, int period)() pure nothrow @safe {
T[period] data = 0;
T sum = 0;
int index, nFilled;

return (in T v) nothrow @safe @nogc {
sum += -data[index] + v;
data[index] = v;
index = (index + 1) % period;
nFilled = min(period, nFilled + 1);
return CommonType!(T, float)(sum) / nFilled;
};
}

void main() {
immutable s3 = sma!(int, 3);
immutable s5 = sma!(double, 5);

foreach (immutable e; [1, 2, 3, 4, 5, 5, 4, 3, 2, 1])
writefln("Added %d, sma(3) = %f, sma(5) = %f", e, s3(e), s5(e));
}
```

{{out}}

```Added 1, sma(3) = 1.000000, sma(5) = 1.000000
Added 2, sma(3) = 1.500000, sma(5) = 1.500000
Added 3, sma(3) = 2.000000, sma(5) = 2.000000
Added 4, sma(3) = 3.000000, sma(5) = 2.500000
Added 5, sma(3) = 4.000000, sma(5) = 3.000000
Added 5, sma(3) = 4.666667, sma(5) = 3.800000
Added 4, sma(3) = 4.666667, sma(5) = 4.200000
Added 3, sma(3) = 4.000000, sma(5) = 4.200000
Added 2, sma(3) = 3.000000, sma(5) = 3.800000
Added 1, sma(3) = 2.000000, sma(5) = 3.000000
```

### Using a Struct

This version avoids the heap allocation of the closure keeping the data in the stack frame of the main function. Same output:

```import std.stdio, std.traits, std.algorithm;

struct SMA(T, int period) {
T[period] data = 0;
T sum = 0;
int index, nFilled;

auto opCall(in T v) pure nothrow @safe @nogc {
sum += -data[index] + v;
data[index] = v;
index = (index + 1) % period;
nFilled = min(period, nFilled + 1);
return CommonType!(T, float)(sum) / nFilled;
}
}

void main() {
SMA!(int, 3) s3;
SMA!(double, 5) s5;

foreach (immutable e; [1, 2, 3, 4, 5, 5, 4, 3, 2, 1])
writefln("Added %d, sma(3) = %f, sma(5) = %f", e, s3(e), s5(e));
}
```

To avoid the floating point approximations keep piling up and growing, the code could perform a periodic sum on the whole circular queue array.

## Dyalect

{{trans|C#}}

```func avg(xs) {
var acc = 0.0
var c = 0
for x in xs {
c += 1
acc += x
}
acc / c
}

func sma(p) {
var s = []
x => {
if s.len() >= p {
s.removeAt(0)
}
s.insert(s.len(), x)
avg(s)
};
}

var nums = Iterator.concat(1.0..5.0, 5.0..1.0)
var sma3 = sma(3)
var sma5 = sma(5)

for n in nums {
print("\(n)\t(sma3) \(sma3(n))\t(sma5) \(sma5(n))")
}
```

## E

This implementation produces two (function) objects sharing state. It is idiomatic in E to separate input from output (read from write) rather than combining them into one object.

The structure is the same as the implementation of [[Standard Deviation#E]].

```pragma.enable("accumulator")
def makeMovingAverage(period) {
def values := ([null] * period).diverge()
var index := 0
var count := 0

def insert(v) {
values[index] := v
index := (index + 1) %% period
count += 1
}

/** Returns the simple moving average of the inputs so far, or null if there
have been no inputs. */
def average() {
if (count > 0) {
return accum 0 for x :notNull in values { _ + x } / count.min(period)
}
}

return [insert, average]
}
```
```e ? for period in [3, 5] { > def [insert, average] := makeMovingAverage(period) > println(`Period \$period:`) > for value in [1,2,3,4,5,5,4,3,2,1] { > insert(value) > println(value, "\t", average()) > } > println() > }

Period 3: 1 1.0 2 1.5 3 2.0 4 3.0 5 4.0 5 4.666666666666667 4 4.666666666666667 3 4.0 2 3.0 1 2.0

Period 5: 1 1.0 2 1.5 3 2.0 4 2.5 5 3.0 5 3.8 4 4.2 3 4.2 2 3.8 1 3.0

```</div>

## EchoLisp

```scheme

(lib 'tree) ;; queues operations

(define (make-sma p)
(define Q (queue (gensym)))
(lambda (item)
(q-push Q item)
(when (> (queue-length Q) p) (q-pop Q))
(// (for/sum ((x (queue->list Q))) x)  (queue-length Q))))

```

{{out}}

```
(define serie '(1 2 3 4 5 5 4 3 2 1))
(define sma-3 (make-sma 3))
(define sma-5 (make-sma 5))

(for ((x serie)) (printf "%3d %10d %10d" x (sma-3 x) (sma-5 x)))

1          1          1
2        1.5        1.5
3          2          2
4          3        2.5
5          4          3
5     4.6667        3.8
4     4.6667        4.2
3          4        4.2
2          3        3.8
1          2          3

```

## Elena

ELENA 4.x :

```import system'routines;
import system'collections;
import extensions;

class SMA
{
object thePeriod;
object theList;

constructor new(period)
{
thePeriod := period;
theList :=new List();
}

append(n)
{
theList.append(n);

var count := theList.Length;
count =>
0 { ^0.0r }
: {
if (count > thePeriod)
{
theList.removeAt:0;

count := thePeriod
};

var sum := theList.summarize(new Real());

^ sum / count
}
}
}

public program()
{
var SMA3 := SMA.new:3;
var SMA5 := SMA.new:5;

for (int i := 1, i <= 5, i += 1) {
console.printPaddingRight(30, "sma3 + ", i, " = ", SMA3.append:i);
console.printLine("sma5 + ", i, " = ", SMA5.append:i)
};

for (int i := 5, i >= 1, i -= 1) {
console.printPaddingRight(30, "sma3 + ", i, " = ", SMA3.append:i);
console.printLine("sma5 + ", i, " = ", SMA5.append:i)
};

}
```

{{out}}

```
sma3 + 1 = 1.0                sma5 + 1 = 1.0
sma3 + 2 = 1.5                sma5 + 2 = 1.5
sma3 + 3 = 2.0                sma5 + 3 = 2.0
sma3 + 4 = 3.0                sma5 + 4 = 2.5
sma3 + 5 = 4.0                sma5 + 5 = 3.0
sma3 + 5 = 4.666666666667     sma5 + 5 = 3.8
sma3 + 4 = 4.666666666667     sma5 + 4 = 4.2
sma3 + 3 = 4.0                sma5 + 3 = 4.2
sma3 + 2 = 3.0                sma5 + 2 = 3.8
sma3 + 1 = 2.0                sma5 + 1 = 3.0

```

## Elixir

The elixir program below generates an anonymous function with an embedded period `p`, which is used as the period of the simple moving average. The `run` function reads numeric input and passes it to the newly created anonymous function, and then "inspects" the result to STDOUT.

```\$ cat simple-moving-avg.exs
#!/usr/bin/env elixir

defmodule Math do
def average([]), do: nil
def average(enum) do
Enum.sum(enum) / length(enum)
end
end

defmodule SMA do

def sma(l, p \\ 10) do
IO.puts("\nSimple moving average(period=#{p}):")
Enum.chunk(l, p, 1)
|> Enum.map(&(%{"input": &1, "avg": Float.round(Math.average(&1), 3)}))
end

defmacro gen_func(p) do
quote do
fn l -> SMA.sma(l, unquote(p)) end
end
end

IO.stream(:stdio, :line)
|> Enum.map(&(String.split(&1, ~r{\s+})))
|> List.flatten()
|> Enum.reject(&(is_nil(&1) || String.length(&1) == 0))
|> Enum.map(&(Integer.parse(&1) |> elem(0)))
end

def run do
sma_func_10 = gen_func(10)
sma_func_15 = gen_func(15)
sma_func_10.(numbers) |> IO.inspect
sma_func_15.(numbers) |> IO.inspect
end
end

SMA.run
```
```#!/bin/bash
elixir ./simple-moving-avg.exs <<EOF
1 2 3 4 5 6 7 8 9 8 7 6 5 4 3 2 1
2 4 6 8 10 12 14 12 10 8 6 4 2
EOF
```

The output is shown below, with the average, followed by the grouped input, forming the basis of each moving average.

```\$ ./simple-moving-avg.sh

Simple moving average(period=10):
[%{avg: 5.3, input: [1, 2, 3, 4, 5, 6, 7, 8, 9, 8]},
%{avg: 5.9, input: [2, 3, 4, 5, 6, 7, 8, 9, 8, 7]},
%{avg: 6.3, input: [3, 4, 5, 6, 7, 8, 9, 8, 7, 6]},
%{avg: 6.5, input: [4, 5, 6, 7, 8, 9, 8, 7, 6, 5]},
%{avg: 6.5, input: [5, 6, 7, 8, 9, 8, 7, 6, 5, 4]},
%{avg: 6.3, input: [6, 7, 8, 9, 8, 7, 6, 5, 4, 3]},
%{avg: 5.9, input: [7, 8, 9, 8, 7, 6, 5, 4, 3, 2]},
%{avg: 5.3, input: [8, 9, 8, 7, 6, 5, 4, 3, 2, 1]},
%{avg: 4.7, input: [9, 8, 7, 6, 5, 4, 3, 2, 1, 2]},
%{avg: 4.2, input: [8, 7, 6, 5, 4, 3, 2, 1, 2, 4]},
%{avg: 4.0, input: [7, 6, 5, 4, 3, 2, 1, 2, 4, 6]},
%{avg: 4.1, input: [6, 5, 4, 3, 2, 1, 2, 4, 6, 8]},
%{avg: 4.5, input: [5, 4, 3, 2, 1, 2, 4, 6, 8, 10]},
%{avg: 5.2, input: [4, 3, 2, 1, 2, 4, 6, 8, 10, 12]},
%{avg: 6.2, input: [3, 2, 1, 2, 4, 6, 8, 10, 12, 14]},
%{avg: 7.1, input: [2, 1, 2, 4, 6, 8, 10, 12, 14, 12]},
%{avg: 7.9, input: [1, 2, 4, 6, 8, 10, 12, 14, 12, 10]},
%{avg: 8.6, input: [2, 4, 6, 8, 10, 12, 14, 12, 10, 8]},
%{avg: 9.0, input: [4, 6, 8, 10, 12, 14, 12, 10, 8, 6]},
%{avg: 9.0, input: [6, 8, 10, 12, 14, 12, 10, 8, 6, 4]},
%{avg: 8.6, input: [8, 10, 12, 14, 12, 10, 8, 6, 4, 2]}]

Simple moving average(period=15):
[%{avg: 5.2, input: [1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3]},
%{avg: 5.267, input: [2, 3, 4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2]},
%{avg: 5.2, input: [3, 4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1]},
%{avg: 5.133, input: [4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1, 2]},
%{avg: 5.133, input: [5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 4]},
%{avg: 5.2, input: [6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 4, 6]},
%{avg: 5.333, input: [7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 4, 6, 8]},
%{avg: 5.533, input: [8, 9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 4, 6, 8, 10]},
%{avg: 5.8, input: [9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 4, 6, 8, 10, 12]},
%{avg: 6.133, input: [8, 7, 6, 5, 4, 3, 2, 1, 2, 4, 6, 8, 10, 12, 14]},
%{avg: 6.4, input: [7, 6, 5, 4, 3, 2, 1, 2, 4, 6, 8, 10, 12, 14, 12]},
%{avg: 6.6, input: [6, 5, 4, 3, 2, 1, 2, 4, 6, 8, 10, 12, 14, 12, 10]},
%{avg: 6.733, input: [5, 4, 3, 2, 1, 2, 4, 6, 8, 10, 12, 14, 12, 10, 8]},
%{avg: 6.8, input: [4, 3, 2, 1, 2, 4, 6, 8, 10, 12, 14, 12, 10, 8, 6]},
%{avg: 6.8, input: [3, 2, 1, 2, 4, 6, 8, 10, 12, 14, 12, 10, 8, 6, 4]},
%{avg: 6.733, input: [2, 1, 2, 4, 6, 8, 10, 12, 14, 12, 10, 8, 6, 4, 2]}]
```

## Erlang

```main() ->
SMA3 = sma(3),
SMA5 = sma(5),
Ns = [1, 2, 3, 4, 5, 5, 4, 3, 2, 1],
lists:foreach(
fun (N) ->
io:format("Added ~b, sma(3) -> ~f, sma(5) -> ~f~n",[N,next(SMA3,N),next(SMA5,N)])
end, Ns),
stop(SMA3),
stop(SMA5).

sma(W) ->
{sma,spawn(?MODULE,loop,[W,[]])}.

loop(Window, Numbers) ->
{_Pid, stop} ->
ok;
{Pid, N} when is_number(N) ->
case length(Numbers) < Window of
true ->
Next = Numbers++[N];
false ->
Next = tl(Numbers)++[N]
end,
Pid ! {average, lists:sum(Next)/length(Next)},
loop(Window,Next);
_ ->
ok
end.

stop({sma,Pid}) ->
Pid ! {self(),stop},
ok.

next({sma,Pid},N) ->
Pid ! {self(), N},
{average, Ave} ->
Ave
end.
```

{{out}}

``` sma:main().
Added 1, sma(3) -> 1.000000, sma(5) -> 1.000000
Added 2, sma(3) -> 1.500000, sma(5) -> 1.500000
Added 3, sma(3) -> 2.000000, sma(5) -> 2.000000
Added 4, sma(3) -> 3.000000, sma(5) -> 2.500000
Added 5, sma(3) -> 4.000000, sma(5) -> 3.000000
Added 5, sma(3) -> 4.666667, sma(5) -> 3.800000
Added 4, sma(3) -> 4.666667, sma(5) -> 4.200000
Added 3, sma(3) -> 4.000000, sma(5) -> 4.200000
Added 2, sma(3) -> 3.000000, sma(5) -> 3.800000
Added 1, sma(3) -> 2.000000, sma(5) -> 3.000000
ok
```

Erlang has closures, but immutable variables. A solution then is to use processes and a simple message passing based API.

## Euler Math Toolbox

Matrix languages have routines to compute the gliding avarages for a given sequence of items.

```
>n=1000; m=100; x=random(1,n);
>x10=fold(x,ones(1,m)/m);
>x10=fftfold(x,ones(1,m)/m)[m:n]; // more efficient

```

It is less efficient to loop as in the following commands.

```
>function store (x:number, v:vector, n:index) ...
\$if cols(v)<n then return v|x;
\$else
\$  v=rotleft(v); v[-1]=x;
\$  return v;
\$endif;
\$endfunction
>v=zeros(1,0); for k=1:20; v=store(k,v,10); mean(v), end;
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
13.5
14.5
15.5
>v
[ 11  12  13  14  15  16  17  18  19  20 ]

```

```let sma period f (list:float list) =
let sma_aux queue v =
let q = Seq.truncate period (v :: queue)
Seq.average q, Seq.toList q
List.fold (fun s v ->
let avg,state = sma_aux s v
f avg
state) [] list

printf "sma3: "
[ 1.;2.;3.;4.;5.;5.;4.;3.;2.;1.] |> sma 3 (printf "%.2f ")
printf "\nsma5: "
[ 1.;2.;3.;4.;5.;5.;4.;3.;2.;1.] |> sma 5 (printf "%.2f ")
printfn ""
```

{{out}}

```sma3: 1.00 1.50 2.00 3.00 4.00 4.67 4.67 4.00 3.00 2.00
sma5: 1.00 1.50 2.00 2.50 3.00 3.80 4.20 4.20 3.80 3.00
```

## Factor

The `I` word creates a quotation (anonymous function) that closes over a sequence and a period. This quotation handles adding/removing numbers to the simple moving average (SMA). We can then add a number to the SMA using `sma-add` and get the SMA's sequence and mean with `sma-query`. Quotations adhere to the `sequence` protocol so we can obtain the sequence of numbers simply by calling `first` on the SMA quotation.

```USING: kernel interpolate io locals math.statistics prettyprint
random sequences ;
IN: rosetta-code.simple-moving-avg

:: I ( P -- quot )
V{ } clone :> v!
[ v swap suffix! P short tail* v! ] ;

: sma-add ( quot n -- quot' ) swap tuck call( x x -- x ) ;

: sma-query ( quot -- avg v ) first concat dup mean swap ;

: simple-moving-average-demo ( -- )
5 I 10 <iota> [
over sma-query unparse
[I After \${2} numbers Sequence is \${0} Mean is \${1}I] nl
] each drop ;

MAIN: simple-moving-average-demo
```

{{out}}

```
After 0 numbers Sequence is V{ } Mean is 0
After 1 numbers Sequence is V{ 41 } Mean is 41
After 2 numbers Sequence is V{ 41 31 } Mean is 36
After 3 numbers Sequence is V{ 41 31 2 } Mean is 24+2/3
After 4 numbers Sequence is V{ 41 31 2 24 } Mean is 24+1/2
After 5 numbers Sequence is V{ 41 31 2 24 70 } Mean is 33+3/5
After 6 numbers Sequence is V{ 31 2 24 70 80 } Mean is 41+2/5
After 7 numbers Sequence is V{ 2 24 70 80 96 } Mean is 54+2/5
After 8 numbers Sequence is V{ 24 70 80 96 84 } Mean is 70+4/5
After 9 numbers Sequence is V{ 70 80 96 84 7 } Mean is 67+2/5

```

## Fantom

```
class MovingAverage
{
Int period
Int[] stream

new make (Int period)
{
this.period = period
stream = [,]
}

// add number to end of stream and remove numbers from start if
// stream is larger than period
public Void addNumber (Int number)
{
while (stream.size > period)
{
stream.removeAt (0)
}
}

// compute average of numbers in stream
public Float average ()
{
if (stream.isEmpty)
return 0.0f
else
1.0f * (Int)(stream.reduce(0, |a,b| { (Int) a + b })) / stream.size
}
}

class Main
{
public static Void main ()
{ // test by adding random numbers and printing average after each number
av := MovingAverage (5)

10.times |i|
{
echo ("After \$i numbers list is \${av.stream} average is \${av.average}")
}
}
}

```

{{out}} for a period of 5:

```
After 0 numbers list is [,] average is 0.0
After 1 numbers list is [64] average is 64.0
After 2 numbers list is [64, 50] average is 57.0
After 3 numbers list is [64, 50, 26] average is 46.666666666666664
After 4 numbers list is [64, 50, 26, 77] average is 54.25
After 5 numbers list is [64, 50, 26, 77, 82] average is 59.8
After 6 numbers list is [50, 26, 77, 82, 95] average is 66.0
After 7 numbers list is [26, 77, 82, 95, 11] average is 58.2
After 8 numbers list is [77, 82, 95, 11, 23] average is 57.6
After 9 numbers list is [82, 95, 11, 23, 50] average is 52.2

```

## Forth

```: f+! ( f addr -- ) dup f@ f+ f! ;
: ,f0s ( n -- ) falign 0 do 0e f, loop ;

: period @ ;
: used cell+ ;
: head 2 cells + ;
: sum  3 cells + faligned ;
: ring ( addr -- faddr )
dup sum float+ swap head @ floats + ;

: update ( fvalue addr -- addr )
dup ring f@ fnegate dup sum f+!
fdup dup ring f!         dup sum f+!
dup head @ 1+  over period mod  over head ! ;

: moving-average
create ( period -- ) dup , 0 , 0 , 1+ ,f0s
does>  ( fvalue -- avg )
update
dup used @ over period < if 1 over used +! then
dup sum f@ used @ 0 d>f f/ ;

3 moving-average sma
1e sma f.  \ 1.
2e sma f.  \ 1.5
3e sma f.  \ 2.
4e sma f.  \ 3.
```

## Fortran

{{works with|Fortran|90 and later}}

```program Movavg
implicit none

integer :: i

write (*, "(a)") "SIMPLE MOVING AVERAGE: PERIOD = 3"

do i = 1, 5
write (*, "(a, i2, a, f8.6)") "Next number:", i, "   sma = ", sma(real(i))
end do
do i = 5, 1, -1
write (*, "(a, i2, a, f8.6)") "Next number:", i, "   sma = ", sma(real(i))
end do

contains

function sma(n)
real :: sma
real, intent(in) :: n
real, save :: a(3) = 0
integer, save :: count = 0

if (count < 3) then
count = count + 1
a(count) = n
else
a = eoshift(a, 1, n)
end if

sma = sum(a(1:count)) / real(count)
end function

end program Movavg
```

## FreeBASIC

```' FB 1.05.0 Win64

Type FuncType As Function(As Double) As Double

' These 'shared' variables are available to all functions defined below
Dim Shared p As UInteger
Dim Shared list() As Double

Function sma(n As Double) As Double
Redim Preserve list(0 To UBound(list) + 1)
list(UBound(list)) = n
Dim start As Integer = 0
Dim length As Integer = UBound(list) + 1
If length > p Then
start = UBound(list) - p + 1
length = p
End If
Dim sum As Double = 0.0
For i As Integer = start To UBound(list)
sum += list(i)
Next
Return sum / length
End Function

Function initSma(period As Uinteger) As FuncType
p = period
Erase list '' ensure the array is empty on each initialization
Return @sma
End Function

Dim As FuncType ma = initSma(3)
Print "Period = "; p
Print
For i As Integer = 0 To 9
Print "Add"; i; " => moving average ="; ma(i)
Next
Print
ma = initSma(5)
Print "Period = "; p
Print
For i As Integer = 9 To 0 Step -1
Print "Add"; i; " => moving average ="; ma(i)
Next
Print
Print "Press any key to quit"
Sleep
```

{{out}}

```
Period = 3

Add 0 => moving average = 0
Add 1 => moving average = 0.5
Add 2 => moving average = 1
Add 3 => moving average = 2
Add 4 => moving average = 3
Add 5 => moving average = 4
Add 6 => moving average = 5
Add 7 => moving average = 6
Add 8 => moving average = 7
Add 9 => moving average = 8

Period = 5

Add 9 => moving average = 9
Add 8 => moving average = 8.5
Add 7 => moving average = 8
Add 6 => moving average = 7.5
Add 5 => moving average = 7
Add 4 => moving average = 6
Add 3 => moving average = 5
Add 2 => moving average = 4
Add 1 => moving average = 3
Add 0 => moving average = 2

```

## GAP

```MovingAverage := function(n)
local sma, buffer, pos, sum, len;
buffer := List([1 .. n], i -> 0);
pos := 0;
len := 0;
sum := 0;
sma := function(x)
pos := RemInt(pos, n) + 1;
sum := sum + x - buffer[pos];
buffer[pos] := x;
len := Minimum(len + 1, n);
return sum/len;
end;
return sma;
end;

f := MovingAverage(3);
f(1);  #  1
f(2);  #  3/2
f(3);  #  2
f(4);  #  3
f(5);  #  4
f(5);  #  14/3
f(4);  #  14/3
f(3);  #  4
f(2);  #  3
f(1);  #  2
```

## Go

```package main

import "fmt"

func sma(n int) func(float64) float64 {
s := make([]float64, 0, n)
i, sum, rn := 0, 0., 1/float64(n)
return func(x float64) float64 {
if len(s) < n {
sum += x
s = append(s, x)
return sum / float64(len(s))
}
s[i] = x
i++
if i == n {
i = 0
}
sum = 0
for _, x = range s {
sum += x
}
return sum * rn
}
}

func main() {
sma3 := sma(3)
sma5 := sma(5)
fmt.Println("x       sma3   sma5")
for _, x := range []float64{1, 2, 3, 4, 5, 5, 4, 3, 2, 1} {
fmt.Printf("%5.3f  %5.3f  %5.3f\n", x, sma3(x), sma5(x))
}
}
```

{{out}}

```
x       sma3   sma5
1.000  1.000  1.000
2.000  1.500  1.500
3.000  2.000  2.000
4.000  3.000  2.500
5.000  4.000  3.000
5.000  4.667  3.800
4.000  4.667  4.200
3.000  4.000  4.200
2.000  3.000  3.800
1.000  2.000  3.000

```

## Groovy

{{trans|Ruby}}

```def simple_moving_average = { size ->
def nums = []
double total = 0.0
return { newElement ->
nums += newElement
oldestElement = nums.size() > size ? nums.remove(0) : 0
total += newElement - oldestElement
total / nums.size()
}
}

ma5 = simple_moving_average(5)

(1..5).each{ printf( "%1.1f ", ma5(it)) }
(5..1).each{ printf( "%1.1f ", ma5(it)) }
```

{{out}}

```1.0 1.5 2.0 2.5 3.0 3.8 4.2 4.2 3.8 3.0
```

Conform version to the requirement, function SMA called multiple times with just a number: {{works with|GHC|6.10.4}}

```{-# LANGUAGE BangPatterns #-}

import Data.List
import Data.IORef

data Pair a b = Pair !a !b

mean :: Fractional a => [a] -> a
mean = divl . foldl' (\(Pair s l) x -> Pair (s+x) (l+1)) (Pair 0.0 0)
where divl (_,0) = 0.0
divl (s,l) = s / fromIntegral l

series = [1,2,3,4,5,5,4,3,2,1]

mkSMA :: Int -> IO (Double -> IO Double)
mkSMA period = avgr <\$> newIORef []
where avgr nsref x = readIORef nsref >>= (\ns ->
let xs = take period (x:ns)
in writeIORef nsref xs \$> mean xs)

main = mkSMA 3 >>= (\sma3 -> mkSMA 5 >>= (\sma5 ->
mapM_ (str <\$> pure n <*> sma3 <*> sma5) series))
where str n mm3 mm5 =
concat ["Next number = ",show n,", SMA_3 = ",show mm3,", SMA_5 = ",show mm5]
```

{{out}}

```Next number = 1.0, SMA_3 = 1.0, SMA_5 = 1.0
Next number = 2.0, SMA_3 = 1.5, SMA_5 = 1.5
Next number = 3.0, SMA_3 = 2.0, SMA_5 = 2.0
Next number = 4.0, SMA_3 = 3.0, SMA_5 = 2.5
Next number = 5.0, SMA_3 = 4.0, SMA_5 = 3.0
Next number = 5.0, SMA_3 = 4.666666666666667, SMA_5 = 3.8
Next number = 4.0, SMA_3 = 4.666666666666667, SMA_5 = 4.2
Next number = 3.0, SMA_3 = 4.0, SMA_5 = 4.2
Next number = 2.0, SMA_3 = 3.0, SMA_5 = 3.8
Next number = 1.0, SMA_3 = 2.0, SMA_5 = 3.0
```

{{works with|GHC|6.10.4}}

```import Data.List
import Control.Arrow

sMA p = map (head *** head ).tail.
scanl (\(y,_) -> (id &&& return. av) . (: if length y == p then init y else y)) ([],[])
where av = liftM2 (/) sum (fromIntegral.length)

printSMA n p = mapM_ (\(n,a) -> putStrLn \$ "Next number: " ++ show n ++ "  Average: " ++ show a)
. take n . sMA p \$ [1..5]++[5,4..1]++[3..]
```

Stateful function using the state monad to keep track of state

{{works with|GHC|7.8.3}}

```

period :: Int
period = 3

type SMAState = [Float]

computeSMA :: Float -> State SMAState Float
computeSMA x = do
previousValues <- get
let values = previousValues ++ [x]
let newAverage = if length values <= period then (sum values) / (fromIntegral \$ length remainingValues :: Float)
else (sum remainingValues) / (fromIntegral \$ length remainingValues :: Float)
where remainingValues = dropIf period values
put \$ dropIf period values
return newAverage

dropIf :: Int -> [a] -> [a]
dropIf x xs = drop ((length xs) - x) xs

demostrateSMA :: State SMAState [Float]
demostrateSMA = mapM computeSMA [1, 2, 3, 4, 5, 5, 4, 3, 2, 1]

main :: IO ()
main = print \$ evalState demostrateSMA []

```

{{out}}

```
[1.0,1.5,2.0,3.0,4.0,4.6666665,4.6666665,4.0,3.0,2.0]

```

## HicEst

```REAL :: n=10, nums(n)

nums = (1,2,3,4,5, 5,4,3,2,1)
DO i = 1, n
WRITE() "num=", i, "SMA3=", SMA(3,nums(i)), "SMA5=",SMA(5,nums(i))
ENDDO

END ! of "main"

FUNCTION SMA(period, num) ! maxID independent streams
REAL :: maxID=10, now(maxID), Periods(maxID), Offsets(maxID), Pool(1000)

ID = INDEX(Periods, period)
IF( ID == 0) THEN ! initialization
IDs = IDs + 1
ID = IDs
Offsets(ID) = SUM(Periods) + 1
Periods(ID) = period
ENDIF

now(ID) = now(ID) + 1
ALIAS(Pool,Offsets(ID),   Past,Periods(ID)) ! renames relevant part of data pool
Past = Past(\$+1) ! shift left
Past(Periods(ID)) = num
SMA = SUM(Past) / MIN( now(ID), Periods(ID) )
END
```
```num=1 SMA3=1 SMA5=1
num=2 SMA3=1.5 SMA5=1.5
num=3 SMA3=2 SMA5=2
num=4 SMA3=3 SMA5=2.5
num=5 SMA3=4 SMA5=3
num=6 SMA3=4.666666667 SMA5=3.8
num=7 SMA3=4.666666667 SMA5=4.2
num=8 SMA3=4 SMA5=4.2
num=9 SMA3=3 SMA5=3.8
num=10 SMA3=2 SMA5=3
```

```procedure main(A)
sma := buildSMA(3)  # Use better name than "I".
every write(sma(!A))
end

procedure buildSMA(P)
local stream
c := create {
stream := []
while n := (avg@&source)[1] do {
put(stream, n)
if *stream > P then pop(stream)
every (avg := 0.0) +:= !stream
avg := avg/*stream
}
}
return (@c, c)
end
```

Note: This program uses Unicon specific co-expression calling syntax. It can be easily modified to run under Icon.

and a sample run:

```->ravg 3 1 4 1 5 9 2 6 3 8
3.0
2.0
2.666666666666667
2.0
3.333333333333333
5.0
5.333333333333333
5.666666666666667
3.666666666666667
5.666666666666667
->

```

If the Utils package is imported from the [https://tapestry.tucson.az.us/unilib Unicon code library] then a (Unicon only) solution is:

```import Utils

procedure main(A)
sma1 := closure(SMA,[],3)
sma2 := closure(SMA,[],4)
every every n := !A do write(left(sma1(n),20), sma2(n))
end

procedure SMA(stream,P,n)
put(stream, n)
if *stream > P then pop(stream)
every (avg := 0.0) +:= !stream
return avg / *stream
end
```

with the sample run:

```->ravg 3 1 4 1 5 9 2 6 3 8
3.0                 3.0
2.0                 2.0
2.666666666666667   2.666666666666667
2.0                 2.25
3.333333333333333   2.75
5.0                 4.75
5.333333333333333   4.25
5.666666666666667   5.5
3.666666666666667   5.0
5.666666666666667   4.75
->

```

## J

'''Note''': J is block-oriented, not stream oriented. That is, J expresses algorithms with the semantics that all the data is available at once (rather than maintaining state and waiting for the next item).

In that context, moving average is expressed very concisely in J as '''`(+/%#)</code>''', though it is worth noting that this approach does not provide averages for the initial cases where not all data would be available yet:`

``` 5 (+/%#)\ 1 2 3 4 5 5 4 3 2 1 NB. not a solution for this task 3 3.8 4.2 4.2 3.8 3 In the context of the task, we need to produce a stateful function to consume streams. Since J does not have native lexical closure, we need to [http://www.jsoftware.com/jwiki/Guides/Lexical%20Closure implement it]. Thus the [[Talk:Averages/Simple_moving_average#J_Implementation|streaming solution]] is more complex: lex =: 1 :'(a[n__a=.m#_.[a=.18!:3\$~0)&(4 :''(+/%#)(#~1-128!:5)n__x=.1|.!.y n__x'')' '''Example:''' sma =: 5 lex sma&> 1 2 3 4 5 5 4 3 2 1 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3 Here, the &> is analogous to the "for each" of other languages. Or, a more traditional approach could be used: avg=: +/ % # SEQ=:'' moveAvg=:4 :0"0 SEQ=:SEQ,y avg ({.~ x -@<. #) SEQ ) 5 moveAvg 1 2 3 4 5 5 4 3 2 1 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3 Java {{works with|Java|1.5+}} import java.util.LinkedList; import java.util.Queue; public class MovingAverage { private final Queue<Double> window = new LinkedList<Double>(); private final int period; private double sum; public MovingAverage(int period) { assert period > 0 : "Period must be a positive integer"; this.period = period; } public void newNum(double num) { sum += num; window.add(num); if (window.size() > period) { sum -= window.remove(); } } public double getAvg() { if (window.isEmpty()) return 0.0; // technically the average is undefined return sum / window.size(); } public static void main(String[] args) { double[] testData = {1, 2, 3, 4, 5, 5, 4, 3, 2, 1}; int[] windowSizes = {3, 5}; for (int windSize : windowSizes) { MovingAverage ma = new MovingAverage(windSize); for (double x : testData) { ma.newNum(x); System.out.println("Next number = " + x + ", SMA = " + ma.getAvg()); } System.out.println(); } } } {{out}} Next number = 1.0, SMA = 1.0 Next number = 2.0, SMA = 1.5 Next number = 3.0, SMA = 2.0 Next number = 4.0, SMA = 3.0 Next number = 5.0, SMA = 4.0 Next number = 5.0, SMA = 4.666666666666667 Next number = 4.0, SMA = 4.666666666666667 Next number = 3.0, SMA = 4.0 Next number = 2.0, SMA = 3.0 Next number = 1.0, SMA = 2.0 Next number = 1.0, SMA = 1.0 Next number = 2.0, SMA = 1.5 Next number = 3.0, SMA = 2.0 Next number = 4.0, SMA = 2.5 Next number = 5.0, SMA = 3.0 Next number = 5.0, SMA = 3.8 Next number = 4.0, SMA = 4.2 Next number = 3.0, SMA = 4.2 Next number = 2.0, SMA = 3.8 Next number = 1.0, SMA = 3.0 JavaScript Using for loop function simple_moving_averager(period) { var nums = []; return function(num) { nums.push(num); if (nums.length > period) nums.splice(0,1); // remove the first element of the array var sum = 0; for (var i in nums) sum += nums[i]; var n = period; if (nums.length < period) n = nums.length; return(sum/n); } } var sma3 = simple_moving_averager(3); var sma5 = simple_moving_averager(5); var data = [1,2,3,4,5,5,4,3,2,1]; for (var i in data) { var n = data[i]; // using WSH WScript.Echo("Next number = " + n + ", SMA_3 = " + sma3(n) + ", SMA_5 = " + sma5(n)); } {{out}} Next number = 1, SMA_3 = 1, SMA_5 = 1 Next number = 2, SMA_3 = 1.5, SMA_5 = 1.5 Next number = 3, SMA_3 = 2, SMA_5 = 2 Next number = 4, SMA_3 = 3, SMA_5 = 2.5 Next number = 5, SMA_3 = 4, SMA_5 = 3 Next number = 5, SMA_3 = 4.666666666666667, SMA_5 = 3.8 Next number = 4, SMA_3 = 4.666666666666667, SMA_5 = 4.2 Next number = 3, SMA_3 = 4, SMA_5 = 4.2 Next number = 2, SMA_3 = 3, SMA_5 = 3.8 Next number = 1, SMA_3 = 2, SMA_5 = 3 Using reduce/filter [http://jsfiddle.net/79xe381e/ JS Fiddle] // single-sided Array.prototype.simpleSMA=function(N) { return this.map( function(el,index, _arr) { return _arr.filter( function(x2,i2) { return i2 <= index && i2 > index - N; }) .reduce( function(current, last, index, arr){ return (current + last); })/index || 1; }); }; g=[0,1,2,3,4,5,6,7,8,9,10]; console.log(g.simpleSMA(3)); console.log(g.simpleSMA(5)); console.log(g.simpleSMA(g.length)); {{out}} [1, 1, 1.5, 2, 2.25, 2.4, 2.5, 2.5714285714285716, 2.625, 2.6666666666666665, 2.7] [1, 1, 1.5, 2, 2.5, 3, 3.3333333333333335, 3.5714285714285716, 3.75, 3.888888888888889, 4] [1, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5] Julia The function wants specified the type of the data in the buffer and, if you want, the limit of the buffer. ```julia function movingaverage(::Type{T} = Float64; lim::Integer = -1) where T<:Real buffer = Vector{T}(0) if lim == -1 # unlimited buffer return (y::T) -> begin push!(buffer, y) return mean(buffer) end else # limited size buffer return (y) -> begin push!(buffer, y) if length(buffer) > lim shift!(buffer) end return mean(buffer) end end end test = movingaverage() @show test(1.0) # mean([1]) @show test(2.0) # mean([1, 2]) @show test(3.0) # mean([1, 2, 3]) {{out}} test(1.0) = 1.0 test(2.0) = 1.5 test(3.0) = 2.0 K Non-stateful: v:v,|v:1+!5 v 1 2 3 4 5 5 4 3 2 1 avg:{(+/x)%#x} sma:{avg'x@(,\!y),(1+!y)+\:!y} sma[v;5] 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3 Stateful: sma:{n::x#_n; {n::1_ n,x; {avg x@&~_n~'x} n}} sma[5]' v 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3 Kotlin // version 1.0.6 fun initMovingAverage(p: Int): (Double) -> Double { if (p < 1) throw IllegalArgumentException("Period must be a positive integer") val list = mutableListOf<Double>() return { list.add(it) if (list.size > p) list.removeAt(0) list.average() } } fun main(args: Array<String>) { val sma4 = initMovingAverage(4) val sma5 = initMovingAverage(5) val numbers = listOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0) println("num\tsma4\tsma5\n") for (number in numbers) println("\${number}\t\${sma4(number)}\t\${sma5(number)}") } {{out}} num sma4 sma5 1.0 1.0 1.0 2.0 1.5 1.5 3.0 2.0 2.0 4.0 2.5 2.5 5.0 3.5 3.0 5.0 4.25 3.8 4.0 4.5 4.2 3.0 4.25 4.2 2.0 3.5 3.8 1.0 2.5 3.0 Lasso {{incorrect|Lasso|routine is called with a list of multiple numbers rather than being called with individual numbers in succession.}} define simple_moving_average(a::array,s::integer)::decimal => { #a->size == 0 ? return 0.00 #s == 0 ? return 0.00 #a->size == 1 ? return decimal(#a->first) #s == 1 ? return decimal(#a->last) local(na = array) if(#a->size <= #s) => { #na = #a else local(ar = #a->ascopy) #ar->reverse loop(#s) => { #na->insert(#ar->get(loop_count)) } } #s > #na->size ? #s = #na->size return (with e in #na sum #e) / decimal(#s) } // tests: 'SMA 3 on array(1,2,3,4,5,5,4,3,2,1): ' simple_moving_average(array(1,2,3,4,5,5,4,3,2,1),3) '\rSMA 5 on array(1,2,3,4,5,5,4,3,2,1): ' simple_moving_average(array(1,2,3,4,5,5,4,3,2,1),5) '\r\rFurther example: \r' local(mynumbers = array, sma_num = 5) loop(10) => {^ #mynumbers->insert(integer_random(1,100)) #mynumbers->size + ' numbers: ' + #mynumbers ' SMA3 is: ' + simple_moving_average(#mynumbers,3) ', SMA5 is: ' + simple_moving_average(#mynumbers,5) '\r' ^} {{out}} SMA 3 on array(1,2,3,4,5,5,4,3,2,1): 2.000000 SMA 5 on array(1,2,3,4,5,5,4,3,2,1): 3.000000 Further example: 1 numbers: array(91) SMA3 is: 91.000000, SMA5 is: 91.000000 2 numbers: array(91, 30) SMA3 is: 60.500000, SMA5 is: 60.500000 3 numbers: array(91, 30, 99) SMA3 is: 73.333333, SMA5 is: 73.333333 4 numbers: array(91, 30, 99, 73) SMA3 is: 67.333333, SMA5 is: 73.250000 5 numbers: array(91, 30, 99, 73, 22) SMA3 is: 64.666667, SMA5 is: 63.000000 6 numbers: array(91, 30, 99, 73, 22, 35) SMA3 is: 43.333333, SMA5 is: 51.800000 7 numbers: array(91, 30, 99, 73, 22, 35, 93) SMA3 is: 50.000000, SMA5 is: 64.400000 8 numbers: array(91, 30, 99, 73, 22, 35, 93, 24) SMA3 is: 50.666667, SMA5 is: 49.400000 9 numbers: array(91, 30, 99, 73, 22, 35, 93, 24, 8) SMA3 is: 41.666667, SMA5 is: 36.400000 10 numbers: array(91, 30, 99, 73, 22, 35, 93, 24, 8, 80) SMA3 is: 37.333333, SMA5 is: 48.000000 Liberty BASIC The interesting thing here is how to implement an equivalent of a stateful function. For sample output see http://libertybasic.conforums.com/index.cgi?board=open&action=display&num=1322956720 dim v\$( 100) ' Each array term stores a particular SMA of period p in p*10 bytes nomainwin WindowWidth =1080 WindowHeight = 780 graphicbox #w.gb1, 20, 20, 1000, 700 open "Running averages to smooth data" for window as #w #w "trapclose quit" #w.gb1 "down" old.x = 0 old.y.orig =500 ' black old.y.3.SMA =350 ' red old.y.20.SMA =300 ' green for i =0 to 999 step 1 scan v =1.1 +sin( i /1000 *2 *3.14159265) + 0.2 *rnd( 1) ' sin wave with added random noise x =i /6.28318 *1000 y.orig =500 -v /2.5 *500 #w.gb1 "color black ; down ; line "; i-1; " "; old.y.orig; " "; i; " "; y.orig; " ; up" y.3.SMA =500 -SMA( 1, v, 3) /2.5 *500 ' SMA given ID of 1 is to do 3-term running average #w.gb1 "color red ; down ; line "; i-1; " "; old.y.3.SMA +50; " "; i; " "; y.3.SMA +50; " ; up" y.20.SMA =500 -SMA( 2, v, 20) /2.5 *500 ' SMA given ID of 2 is to do 20-term running average #w.gb1 "color green ; down ; line "; i-1; " "; old.y.20.SMA +100; " "; i; " "; y.20.SMA +100; " ; up" 'print "Supplied with "; v; ", so SMAs are now "; using( "###.###", SMA( 1, v, 3)); " over 3 terms or "; using( "###.###", SMA( 2, v, 5)) ; " over 5 terms." ' ID, latest data, period old.y.orig =y.orig old.y.3.SMA =y.3.SMA old.y.20.SMA =y.20.SMA next i wait sub quit j\$ close #w end end sub function SMA( ID, Number, Period) v\$( ID) =right\$( " " +str\$( Number), 10) +v\$( ID) ' add new number at left, lose last number on right v\$( ID) =left\$( v\$( ID), Period *10) 'print "{"; v\$( ID); "}", k =0 ' number of terms read total =0 ' sum of terms read do p\$ =mid\$( v\$( ID), 1 +k *10, 10) if p\$ ="" then exit do vv =val( p\$) total =total +vv k =k +1 loop until p\$ ="" if k <Period then SMA =total / k else SMA =total /Period end function Logo Although Logo does not support closures, some varieties of Logo support enough metaprogramming to accomplish this task. {{works with|UCB Logo}} UCB Logo has a DEFINE primitive to construct functions from structured instruction lists. In addition, UCB Logo supports a compact template syntax for quoting lists (backquote "`") and replacing components of quoted lists (comma ","). These facilities can be used together in order to create templated function-defining-functions. to average :l output quotient apply "sum :l count :l end to make.sma :name :period localmake "qn word :name ".queue make :qn [] define :name `[ [n] ; parameter list [if equal? count :,:qn ,:period [ignore dequeue ",:qn]] [queue ",:qn :n] [output average :,:qn] ] end make.sma "avg3 3 show map "avg3 [1 2 3 4 5] ; [1 1.5 2 3 4] show text "avg3 ; examine what substitutions took place [[n] [if equal? count :avg3.queue 3 [ignore dequeue "avg3.queue]] [queue "avg3.queue :n] [output average :avg3.queue]] ; the internal queue is in the global namespace, easy to inspect show :avg3.queue ; [3 4 5] If namespace pollution is a concern, UCB Logo supplies a GENSYM command to obtain unique names in order to avoid collisions. ... localmake "qn word :name gensym ... ; list user-defined functions and variables show procedures ; [average avg3 make.sma] show names ; [[[] [avg3.g1]] Lua function sma(period) local t = {} function sum(a, ...) if a then return a+sum(...) else return 0 end end function average(n) if #t == period then table.remove(t, 1) end t[#t + 1] = n return sum(unpack(t)) / #t end return average end sma5 = sma(5) sma10 = sma(10) print("SMA 5") for v=1,15 do print(sma5(v)) end print("\nSMA 10") for v=1,15 do print(sma10(v)) end =={{header|Mathematica}} / {{header|Wolfram Language}}== This version uses a list entry so it can use the built-in function. MA[x_List, r_] := Join[Table[Mean[x[[1;;y]]],{y,r-1}], MovingAverage[x,r]] This version is stateful instead. MAData = {{}, 0}; MAS[x_, t_: Null] := With[{r = If[t === Null, MAData[[2]], t]}, Mean[MAData[[1]] = If[Length[#] > (MAData[[2]] = r), #[[-r ;; -1]], #] &@ Append[MAData[[1]], x]]] Tests: MA[{1, 2, 3, 4, 5, 5, 4, 3, 2, 1}, 5] => {1, 3/2, 2, 5/2, 3, 19/5, 21/5, 21/5, 19/5, 3} MAS[1, 5] => 1 MAS[2] => 3/2 MAS[3] => 2 MAS[4] => 5/2 MAS[5] => 3 MAS[5] => 19/5 MAS[4] => 21/5 MAS[3] => 21/5 MAS[2] => 19/5 MAS[1] => 3 =={{header|MATLAB}} / {{header|Octave}}== Matlab and Octave provide very efficient and fast functions, that can be applied to vectors (i.e. series of data samples) [m,z] = filter(ones(1,P),P,x); m is the moving average, z returns the state at the end of the data series, which can be used to continue the moving average. [m,z] = filter(ones(1,P),P,x,z); Mercury In Mercury, an idiomatic "moving averages" function would be 'stateless' - or rather, it would have ''explicit state'' that its callers would have to thread through uses of it: % state(period, list of floats from [newest, ..., oldest]) :- type state ---> state(int, list(float)). :- func init(int) = state. init(Period) = state(Period, []). :- pred sma(float::in, float::out, state::in, state::out) is det. sma(N, Average, state(P, L0), state(P, L)) :- take_upto(P, [N|L0], L), Average = foldl((+), L, 0.0) / float(length(L)). Some notes about this solution: unless P = 0, length(L) can never be 0, as L always incorporates at least N (a step that is accomplished in the arguments to list.take_upto/3). If the implementation of the 'state' type is hidden, and if init/1 checks for P = 0, users of this code can never cause a division-by-zero error in sma/4. Although this solution doesn't try to be as stateful as the task description would like, explicit state is by far simpler and more natural and more straightforward than the alternative in Mercury. Finally, [http://www.mercury.csse.unimelb.edu.au/information/doc-release/mercury_ref/State-variables.html#State-variables state variables] (and higher-order functions that anticipate threaded state) remove much of the potential ugliness or error in threading the same state through many users. MiniScript We define an SMA class, which can be configured with the desired window size (P). SMA = {} SMA.P = 5 // (a default; may be overridden) SMA.buffer = null SMA.next = function(n) if self.buffer == null then self.buffer = [] self.buffer.push n if self.buffer.len > self.P then self.buffer.pull return self.buffer.sum / self.buffer.len end function sma3 = new SMA sma3.P = 3 sma5 = new SMA for i in range(10) num = round(rnd*100) print "num: " + num + " sma3: " + sma3.next(num) + " sma5: " + sma5.next(num) end for {{out}} num: 81 sma3: 81 sma5: 81 num: 82 sma3: 81.5 sma5: 81.5 num: 78 sma3: 80.333333 sma5: 80.333333 num: 54 sma3: 71.333333 sma5: 73.75 num: 94 sma3: 75.333333 sma5: 77.8 num: 8 sma3: 52 sma5: 63.2 num: 40 sma3: 47.333333 sma5: 54.8 num: 98 sma3: 48.666667 sma5: 58.8 num: 48 sma3: 62 sma5: 57.6 num: 41 sma3: 62.333333 sma5: 47 num: 94 sma3: 61 sma5: 64.2 NetRexx {{trans|Java}} /* NetRexx */ options replace format comments java crossref symbols nobinary numeric digits 20 class RAvgSimpleMoving public properties private window = java.util.Queue period sum properties constant exMsg = 'Period must be a positive integer' -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method RAvgSimpleMoving(period_) public if \period_.datatype('w') then signal RuntimeException(exMsg) if period_ <= 0 then signal RuntimeException(exMsg) window = LinkedList() period = period_ sum = 0 return -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method newNum(num) public sum = sum + num window.add(num) if window.size() > period then do rmv = (Rexx window.remove()) sum = sum - rmv end return -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method getAvg() public returns Rexx if window.isEmpty() then do avg = 0 end else do avg = sum / window.size() end return avg -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method run_samples(args = String[]) public static testData = [Rexx 1, 2, 3, 4, 5, 5, 4, 3, 2, 1] windowSizes = [Rexx 3, 5] loop windSize over windowSizes ma = RAvgSimpleMoving(windSize) loop xVal over testData ma.newNum(xVal) say 'Next number =' xVal.right(5)', SMA =' ma.getAvg().format(10, 9) end xVal say end windSize return -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method main(args = String[]) public static run_samples(args) return {{out}} Next number = 1.0, SMA = 1.000000000 Next number = 2.0, SMA = 1.500000000 Next number = 3.0, SMA = 2.000000000 Next number = 4.0, SMA = 3.000000000 Next number = 5.0, SMA = 4.000000000 Next number = 5.0, SMA = 4.666666667 Next number = 4.0, SMA = 4.666666667 Next number = 3.0, SMA = 4.000000000 Next number = 2.0, SMA = 3.000000000 Next number = 1.0, SMA = 2.000000000 Next number = 1.0, SMA = 1.000000000 Next number = 2.0, SMA = 1.500000000 Next number = 3.0, SMA = 2.000000000 Next number = 4.0, SMA = 2.500000000 Next number = 5.0, SMA = 3.000000000 Next number = 5.0, SMA = 3.800000000 Next number = 4.0, SMA = 4.200000000 Next number = 3.0, SMA = 4.200000000 Next number = 2.0, SMA = 3.800000000 Next number = 1.0, SMA = 3.000000000 ``` ## Nim ```nim import queues proc simplemovingaverage(period: int): auto = assert period > 0 var summ, n = 0.0 values = initQueue[float]() for i in 1..period: values.add(0) proc sma(x: float): float = values.add(x) summ += x - values.dequeue() n = min(n+1, float(period)) result = summ / n return sma var sma = simplemovingaverage(3) for i in 1..5: echo sma(float(i)) for i in countdown(5,1): echo sma(float(i)) echo "" var sma2 = simplemovingaverage(5) for i in 1..5: echo sma2(float(i)) for i in countdown(5,1): echo sma2(float(i)) ``` {{out}} ```txt 1.0000000000000000e+00 1.5000000000000000e+00 2.0000000000000000e+00 3.0000000000000000e+00 4.0000000000000000e+00 4.6666666666666670e+00 4.6666666666666670e+00 4.0000000000000000e+00 3.0000000000000000e+00 2.0000000000000000e+00 1.0000000000000000e+00 1.5000000000000000e+00 2.0000000000000000e+00 2.5000000000000000e+00 3.0000000000000000e+00 3.7999999999999998e+00 4.2000000000000002e+00 4.2000000000000002e+00 3.7999999999999998e+00 3.0000000000000000e+00 ``` ## Objeck {{trans|Java}} ```objeck ﻿use Collection; class MovingAverage { @window : FloatQueue; @period : Int; @sum : Float; New(period : Int) { @window := FloatQueue->New(); @period := period; } method : NewNum(num : Float) ~ Nil { @sum += num; @window->Add(num); if(@window->Size() > @period) { @sum -= @window->Remove(); }; } method : GetAvg() ~ Float { if(@window->IsEmpty()) { return 0; # technically the average is undefined }; return @sum / @window->Size(); } function : Main(args : String[]) ~ Nil { testData := [1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0]; windowSizes := [3.0, 5.0]; each(i : windowSizes) { windSize := windowSizes[i]; ma := MovingAverage->New(windSize); each(j : testData) { x := testData[j]; ma->NewNum(x); avg := ma->GetAvg(); "Next number = {\$x}, SMA = {\$avg}"->PrintLine(); }; IO.Console->PrintLine(); }; } } ``` {{out}} ```txt Next number = 1.0, SMA = 1.0 Next number = 2.0, SMA = 1.500 Next number = 3.0, SMA = 2.0 Next number = 4.0, SMA = 3.0 Next number = 5.0, SMA = 4.0 Next number = 5.0, SMA = 4.667 Next number = 4.0, SMA = 4.667 Next number = 3.0, SMA = 4.0 Next number = 2.0, SMA = 3.0 Next number = 1.0, SMA = 2.0 Next number = 1.0, SMA = 1.0 Next number = 2.0, SMA = 1.500 Next number = 3.0, SMA = 2.0 Next number = 4.0, SMA = 2.500 Next number = 5.0, SMA = 3.0 Next number = 5.0, SMA = 3.800 Next number = 4.0, SMA = 4.200 Next number = 3.0, SMA = 4.200 Next number = 2.0, SMA = 3.800 Next number = 1.0, SMA = 3.0 ``` =={{header|Objective-C}}== ```objc>#import period) { NSNumber *n = window[0]; sum -= [n doubleValue]; [window removeObjectAtIndex:0]; } } // get the average value - (double)avg { if([window count] == 0) { return 0; // technically the average is undefined } return sum / [window count]; } // set the period, resizes current window - (void)setPeriod:(unsigned int)thePeriod { // make smaller? if(thePeriod < [window count]) { for(int i = 0; i < thePeriod; ++i) { NSNumber *n = window[0]; sum -= [n doubleValue]; [window removeObjectAtIndex:0]; } } period = thePeriod; } // get the period (window size) - (unsigned int)period { return period; } // clear the window and current sum - (void)clear { [window removeAllObjects]; sum = 0; } @end int main (int argc, const char * argv[]) { @autoreleasepool { double testData[10] = {1,2,3,4,5,5,4,3,2,1}; int periods[2] = {3,5}; for(int i = 0; i < 2; ++i) { MovingAverage *ma = [[MovingAverage alloc] initWithPeriod:periods[i]]; for(int j = 0; j < 10; ++j) { [ma add:testData[j]]; NSLog(@"Next number = %f, SMA = %f", testData[j], [ma avg]); } NSLog(@"\n"); } } return 0; } ``` {{out}} ```txt Next number = 1.000000, SMA = 1.000000 Next number = 2.000000, SMA = 1.500000 Next number = 3.000000, SMA = 2.000000 Next number = 4.000000, SMA = 3.000000 Next number = 5.000000, SMA = 4.000000 Next number = 5.000000, SMA = 4.666667 Next number = 4.000000, SMA = 4.666667 Next number = 3.000000, SMA = 4.000000 Next number = 2.000000, SMA = 3.000000 Next number = 1.000000, SMA = 2.000000 Next number = 1.000000, SMA = 1.000000 Next number = 2.000000, SMA = 1.500000 Next number = 3.000000, SMA = 2.000000 Next number = 4.000000, SMA = 2.500000 Next number = 5.000000, SMA = 3.000000 Next number = 5.000000, SMA = 3.800000 Next number = 4.000000, SMA = 4.200000 Next number = 3.000000, SMA = 4.200000 Next number = 2.000000, SMA = 3.800000 Next number = 1.000000, SMA = 3.000000 ``` ## OCaml ```ocaml let sma (n, s, q) x = let l = Queue.length q and s = s +. x in Queue.push x q; if l < n then (n, s, q), s /. float (l + 1) else ( let s = s -. Queue.pop q in (n, s, q), s /. float l ) let _ = let periodLst = [ 3; 5 ] in let series = [ 1.; 2.; 3.; 4.; 5.; 5.; 4.; 3.; 2.; 1. ] in List.iter (fun d -> Printf.printf "SIMPLE MOVING AVERAGE: PERIOD = %d\n" d; ignore ( List.fold_left (fun o x -> let o, m = sma o x in Printf.printf "Next number = %-2g, SMA = %g\n" x m; o ) (d, 0., Queue.create ()) series; ); print_newline (); ) periodLst ``` {{out}} ```txt SIMPLE MOVING AVERAGE: PERIOD = 3 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 3 Next number = 5 , SMA = 4 Next number = 5 , SMA = 4.66667 Next number = 4 , SMA = 4.66667 Next number = 3 , SMA = 4 Next number = 2 , SMA = 3 Next number = 1 , SMA = 2 SIMPLE MOVING AVERAGE: PERIOD = 5 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 2.5 Next number = 5 , SMA = 3 Next number = 5 , SMA = 3.8 Next number = 4 , SMA = 4.2 Next number = 3 , SMA = 4.2 Next number = 2 , SMA = 3.8 Next number = 1 , SMA = 3 ``` More imperatively: ```ocaml let sma_create period = let q = Queue.create () and sum = ref 0.0 in fun x -> sum := !sum +. x; Queue.push x q; if Queue.length q > period then sum := !sum -. Queue.pop q; !sum /. float (Queue.length q) let () = let periodLst = [ 3; 5 ] in let series = [ 1.; 2.; 3.; 4.; 5.; 5.; 4.; 3.; 2.; 1. ] in List.iter (fun d -> Printf.printf "SIMPLE MOVING AVERAGE: PERIOD = %d\n" d; let sma = sma_create d in List.iter (fun x -> Printf.printf "Next number = %-2g, SMA = %g\n" x (sma x); ) series; print_newline (); ) periodLst ``` ## Oforth createSMA returns a closure. The list of values is included into a channel so this code is thread-safe : multiple tasks running in parallel can call the closure returned. ```oforth import: parallel : createSMA(period) | ch | Channel new [ ] over send drop ->ch #[ ch receive + left(period) dup avg swap ch send drop ] ; ``` Usage: ```oforth : test | sma3 sma5 l | 3 createSMA -> sma3 5 createSMA -> sma5 [ 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 ] ->l "SMA3" .cr l apply( #[ sma3 perform . ] ) printcr "SMA5" .cr l apply( #[ sma5 perform . ] ) ; ``` {{out}} ```txt >test SMA3 1 1.5 2 3 4 4.66666666666667 4.66666666666667 4 3 2 SMA5 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3 ok ``` ## ooRexx ooRexx does not have stateful functions, but the same effect can be achieved by using object instances. ```ooRexx testdata = .array~of(1, 2, 3, 4, 5, 5, 4, 3, 2, 1) -- run with different period sizes loop period over .array~of(3, 5) say "Period size =" period say movingaverage = .movingaverage~new(period) loop number over testdata average = movingaverage~addnumber(number) say " Next number =" number", moving average =" average end say end ::class movingaverage ::method init expose period queue sum use strict arg period sum = 0 -- the circular queue makes this easy queue = .circularqueue~new(period) -- add a number to the average set ::method addNumber expose queue sum use strict arg number sum += number -- add this to the queue old = queue~queue(number) -- if we pushed an element off the end of the queue, -- subtract this from our sum if old \= .nil then sum -= old -- and return the current item return sum / queue~items -- extra method to retrieve current average ::method average expose queue sum -- undefined really, but just return 0 if queue~isempty then return 0 -- return current queue return sum / queue~items ``` {{out}} ```txt Period size = 3 Next number = 1, moving average = 1 Next number = 2, moving average = 1.5 Next number = 3, moving average = 2 Next number = 4, moving average = 3 Next number = 5, moving average = 4 Next number = 5, moving average = 4.66666667 Next number = 4, moving average = 4.66666667 Next number = 3, moving average = 4 Next number = 2, moving average = 3 Next number = 1, moving average = 2 Period size = 5 Next number = 1, moving average = 1 Next number = 2, moving average = 1.5 Next number = 3, moving average = 2 Next number = 4, moving average = 2.5 Next number = 5, moving average = 3 Next number = 5, moving average = 3.8 Next number = 4, moving average = 4.2 Next number = 3, moving average = 4.2 Next number = 2, moving average = 3.8 Next number = 1, moving average = 3 ``` ## OxygenBasic ```oxygenbasic def max 1000 Class MovingAverage ' ### ============ indexbase 1 double average,invperiod,mdata[max] sys index,period method Setup(double a,p) sys i Period=p invPeriod=1/p index=0 average=a for i=1 to period mdata[i]=a next end method method Data(double v) as double sys i index++ if index>period then index=1 'recycle i=index+1 'for oldest data if i>period then i=1 'recycle mdata[index]=v average=average+invperiod*(v-mdata[i]) end method end class 'TEST '==== MovingAverage A A.Setup 100,10 'initial value and period A.data 50 '... print A.average 'reult 95 ``` ## Oz ```oz declare fun {CreateSMA Period} Xs = {NewCell nil} in fun {\$ X} Xs := {List.take X|@Xs Period} {FoldL @Xs Number.'+' 0.0} / {Int.toFloat {Min Period {Length @Xs}}} end end in for Period in [3 5] do SMA = {CreateSMA Period} in {System.showInfo "\nSTART PERIOD "#Period} for I in 1..5 do {System.showInfo " Number = "#I#" , SMA = "#{SMA {Int.toFloat I}}} end for I in 5..1;~1 do {System.showInfo " Number = "#I#" , SMA = "#{SMA {Int.toFloat I}}} end end ``` ## PARI/GP Partial implementation: does not (yet?) create different stores on each invocation. ```parigp sma_per(n)={ sma_v=vector(n); sma_i = 0; n->if(sma_i++>#sma_v,sma_v[sma_i=1]=n;0,sma_v[sma_i]=n;0)+sum(i=1,#sma_v,sma_v[i])/#sma_v }; ``` ## Pascal {{works with|Free Pascal}} Like in other implementations the sum of the last p values is only updated by subtracting the oldest value and addindg the new. To minimize rounding errors after p values the sum is corrected to the real sum. ```Pascal program sma; type tsma = record smaValue : array of double; smaAverage, smaSumOld, smaSumNew, smaRezActLength : double; smaActLength, smaLength, smaPos :NativeInt; smaIsntFull: boolean; end; procedure smaInit(var sma:tsma;p: NativeUint); Begin with sma do Begin setlength(smaValue,0); setlength(smaValue,p); smaLength:= p; smaActLength := 0; smaAverage:= 0.0; smaSumOld := 0.0; smaSumNew := 0.0; smaPos := p-1; smaIsntFull := true end; end; function smaAddValue(var sma:tsma;v: double):double; Begin with sma do Begin IF smaIsntFull then Begin inc(smaActLength); smaRezActLength := 1/smaActLength; smaIsntFull := smaActLength < smaLength ; end; smaSumOld := smaSumOld+v-smaValue[smaPos]; smaValue[smaPos] := v; smaSumNew := smaSumNew+v; smaPos := smaPos-1; if smaPos < 0 then begin smaSumOld:= smaSumNew; smaSumNew:= 0.0; smaPos := smaLength-1; end; smaAverage := smaSumOld *smaRezActLength; smaAddValue:= smaAverage; end; end; var sma3,sma5:tsma; i : LongInt; begin smaInit(sma3,3); smaInit(sma5,5); For i := 1 to 5 do Begin write('Inserting ',i,' into sma3 ',smaAddValue(sma3,i):0:4); writeln(' Inserting ',i,' into sma5 ',smaAddValue(sma5,i):0:4); end; For i := 5 downto 1 do Begin write('Inserting ',i,' into sma3 ',smaAddValue(sma3,i):0:4); writeln(' Inserting ',i,' into sma5 ',smaAddValue(sma5,i):0:4); end; //speed test smaInit(sma3,3); For i := 1 to 100000000 do smaAddValue(sma3,i); writeln('100''000''000 insertions ',sma3.smaAverage:0:4); end. ``` ;output: ```txt time ./sma Inserting 1 into sma3 1.0000 Inserting 1 into sma5 1.0000 Inserting 2 into sma3 1.5000 Inserting 2 into sma5 1.5000 Inserting 3 into sma3 2.0000 Inserting 3 into sma5 2.0000 Inserting 4 into sma3 3.0000 Inserting 4 into sma5 2.5000 Inserting 5 into sma3 4.0000 Inserting 5 into sma5 3.0000 Inserting 5 into sma3 4.6667 Inserting 5 into sma5 3.8000 Inserting 4 into sma3 4.6667 Inserting 4 into sma5 4.2000 Inserting 3 into sma3 4.0000 Inserting 3 into sma5 4.2000 Inserting 2 into sma3 3.0000 Inserting 2 into sma5 3.8000 Inserting 1 into sma3 2.0000 Inserting 1 into sma5 3.0000 100'000'000 insertions 99999999.0000 real 0m0.780s { 64-Bit } ``` ## Perl Using an initializer function which returns an anonymous closure which closes over an instance ''(separate for each call to the initializer!)'' of the lexical variables \$period, @list, and \$sum: ```perl sub sma_generator { my \$period = shift; my (@list, \$sum); return sub { my \$number = shift; push @list, \$number; \$sum += \$number; \$sum -= shift @list if @list > \$period; return \$sum / @list; } } # Usage: my \$sma = sma_generator(3); for (1, 2, 3, 2, 7) { printf "append \$_ --> sma = %.2f (with period 3)\n", \$sma->(\$_); } ``` {{out}} ```txt append 1 --> sma = 1.00 (with period 3) append 2 --> sma = 1.50 (with period 3) append 3 --> sma = 2.00 (with period 3) append 2 --> sma = 2.33 (with period 3) append 7 --> sma = 4.00 (with period 3) ``` ## Perl 6 {{works with|Rakudo|2016.08}} ```perl6 sub sma-generator (Int \$P where * > 0) { sub (\$x) { state @a = 0 xx \$P; @a.push(\$x).shift; @a.sum / \$P; } } # Usage: my &sma = sma-generator 3; for 1, 2, 3, 2, 7 { printf "append \$_ --> sma = %.2f (with period 3)\n", sma \$_; } ``` {{out}} ```txt append 1 --> sma = 0.33 (with period 3) append 2 --> sma = 1.00 (with period 3) append 3 --> sma = 2.00 (with period 3) append 2 --> sma = 2.33 (with period 3) append 7 --> sma = 4.00 (with period 3) ``` ## Phix First create a separate file sma.e to encapsulate the private variables. Note in particular the complete lack of any special magic/syntax: it is just a table with some indexes. ```Phix sequence sma = {} -- {{period,history,circnxt}} (private to sma.e) integer sma_free = 0 global function new_sma(integer period) integer res if sma_free then res = sma_free sma_free = sma[sma_free] sma[res] = {period,{},0} else sma = append(sma,{period,{},0}) res = length(sma) end if return res end function global procedure add_sma(integer sidx, atom val) integer period, circnxt sequence history {period,history,circnxt} = sma[sidx] sma[sidx][2] = 0 -- (kill refcount) if length(history)period then circnxt = 1 end if sma[sidx][3] = circnxt history[circnxt] = val end if sma[sidx][2] = history end procedure global function get_sma_average(integer sidx) sequence history = sma[sidx][2] integer l = length(history) if l=0 then return 0 end if return sum(history)/l end function global function moving_average(integer sidx, atom val) add_sma(sidx,val) return get_sma_average(sidx) end function global procedure free_sma(integer sidx) sma[sidx] = sma_free sma_free = sidx end procedure ``` and the main file is: ```Phix include sma.e constant sma3 = new_sma(3) constant sma5 = new_sma(5) constant s = {1,2,3,4,5,5,4,3,2,1} integer si for i=1 to length(s) do si = s[i] printf(1,"%2g: sma3=%8g, sma5=%8g\n",{si,moving_average(sma3,si),moving_average(sma5,si)}) end for ``` {{out}} ```txt 1: sma3= 1, sma5= 1 2: sma3= 1.5, sma5= 1.5 3: sma3= 2, sma5= 2 4: sma3= 3, sma5= 2.5 5: sma3= 4, sma5= 3 5: sma3= 4.66667, sma5= 3.8 4: sma3= 4.66667, sma5= 4.2 3: sma3= 4, sma5= 4.2 2: sma3= 3, sma5= 3.8 1: sma3= 2, sma5= 3 ``` ## PicoLisp ```PicoLisp (de sma (@Len) (curry (@Len (Data)) (N) (push 'Data N) (and (nth Data @Len) (con @)) # Truncate (*/ (apply + Data) (length Data)) ) ) ``` ```PicoLisp (def 'sma3 (sma 3)) (def 'sma5 (sma 5)) (scl 2) (for N (1.0 2.0 3.0 4.0 5.0 5.0 4.0 3.0 2.0 1.0) (prinl (format N *Scl) " (sma3) " (format (sma3 N) *Scl) " (sma5) " (format (sma5 N) *Scl) ) ) ``` {{out}} ```txt 1.00 (sma3) 1.00 (sma5) 1.00 2.00 (sma3) 1.50 (sma5) 1.50 3.00 (sma3) 2.00 (sma5) 2.00 4.00 (sma3) 3.00 (sma5) 2.50 5.00 (sma3) 4.00 (sma5) 3.00 5.00 (sma3) 4.67 (sma5) 3.80 4.00 (sma3) 4.67 (sma5) 4.20 3.00 (sma3) 4.00 (sma5) 4.20 2.00 (sma3) 3.00 (sma5) 3.80 1.00 (sma3) 2.00 (sma5) 3.00 ``` ## PL/I ### version 1 ```pli SMA: procedure (N) returns (float byaddr); declare N fixed; declare A(*) fixed controlled, (p, q) fixed binary static initial (0); if allocation(A) = 0 then signal error; p = p + 1; if q < 20 then q = q + 1; if p > hbound(A, 1) then p = 1; A(p) = N; return (sum(float(A))/q); I: ENTRY (Period); declare Period fixed binary; if allocation(A) > 0 then FREE A; allocate A(Period); A = 0; p = 0; end SMA; ``` ### version 2 {{trans|REXX}} ```pli *process source attributes xref; mat: Proc Options(main); Dcl a(10) Dec Fixed(8,6); Dcl s Dec Fixed(10,8); Dcl n Bin Fixed(31) init(hbound(a)); /* number of items in the list. */ Dcl p Bin Fixed(31) init(3); /* the 1st period */ Dcl q Bin Fixed(31) init(5); /* the 2nd period */ Dcl m Bin Fixed(31); Call i(a); Put Edit(' SMA with SMA with', ' number period 3 period 5', ' -------- ---------- ----------') (Skip,a); Do m=1 To n; Put Edit(m,sma(p,m),sma(q,m))(Skip,f(5),2(f(13,6))); End; i: Proc(a); Dcl a(*) Dec Fixed(8,6); Dcl (j,m) Bin Fixed(31); Do j=1 To hbound(a)/2; a(j)=j; /* ··· increasing values. */ End; Do k=hbound(a)/2 To 1 By -1; a(j)=k; /* ··· decreasing values. */ j+=1; End; End; sma: Proc(p,j) Returns(Dec Fixed(8,6)); Dcl s Dec fixed(8,6) Init(0); Dcl i Bin Fixed(31) Init(0); Dcl j Bin Fixed(31) Init((hbound(a)+1)); Dcl (p,i,k,ka,kb) Bin Fixed(31); ka=max(1,j-p+1); kb=j+p; Do k=ka To kb While(k<=j); i+=1; s+=a(k) End; s=s/i+0.5e-6; Return(s); End; End; ``` {{out}} ```txt SMA with SMA with number period 3 period 5 -------- ---------- ---------- 1 1.000000 1.000000 2 1.500000 1.500000 3 2.000000 2.000000 4 3.000000 2.500000 5 4.000000 3.000000 6 4.666667 3.800000 7 4.666667 4.200000 8 4.000000 4.200000 9 3.000000 3.800000 10 2.000000 3.000000 ``` ## PureBasic ```PureBasic Procedure.d SMA(Number, Period=0) Static P Static NewList L() Protected Sum=0 If Period<>0 P=Period EndIf LastElement(L()) AddElement(L()) L()=Number While ListSize(L())>P FirstElement(L()) DeleteElement(L(),1) Wend ForEach L() sum+L() Next ProcedureReturn sum/ListSize(L()) EndProcedure ``` ## Python {{Works with|Python|3.x}} Both implementations use the [http://www.doughellmann.com/PyMOTW/collections/index.html deque] datatype. ### Procedural ```python from collections import deque def simplemovingaverage(period): assert period == int(period) and period > 0, "Period must be an integer >0" summ = n = 0.0 values = deque([0.0] * period) # old value queue def sma(x): nonlocal summ, n values.append(x) summ += x - values.popleft() n = min(n+1, period) return summ / n return sma ``` ### Class based ```python from collections import deque class Simplemovingaverage(): def __init__(self, period): assert period == int(period) and period > 0, "Period must be an integer >0" self.period = period self.stream = deque() def __call__(self, n): stream = self.stream stream.append(n) # appends on the right streamlength = len(stream) if streamlength > self.period: stream.popleft() streamlength -= 1 if streamlength == 0: average = 0 else: average = sum( stream ) / streamlength return average ``` '''Tests''' ```python if __name__ == '__main__': for period in [3, 5]: print ("\nSIMPLE MOVING AVERAGE (procedural): PERIOD =", period) sma = simplemovingaverage(period) for i in range(1,6): print (" Next number = %-2g, SMA = %g " % (i, sma(i))) for i in range(5, 0, -1): print (" Next number = %-2g, SMA = %g " % (i, sma(i))) for period in [3, 5]: print ("\nSIMPLE MOVING AVERAGE (class based): PERIOD =", period) sma = Simplemovingaverage(period) for i in range(1,6): print (" Next number = %-2g, SMA = %g " % (i, sma(i))) for i in range(5, 0, -1): print (" Next number = %-2g, SMA = %g " % (i, sma(i))) ``` {{out}} ```txt SIMPLE MOVING AVERAGE (procedural): PERIOD = 3 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 3 Next number = 5 , SMA = 4 Next number = 5 , SMA = 4.66667 Next number = 4 , SMA = 4.66667 Next number = 3 , SMA = 4 Next number = 2 , SMA = 3 Next number = 1 , SMA = 2 SIMPLE MOVING AVERAGE (procedural): PERIOD = 5 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 2.5 Next number = 5 , SMA = 3 Next number = 5 , SMA = 3.8 Next number = 4 , SMA = 4.2 Next number = 3 , SMA = 4.2 Next number = 2 , SMA = 3.8 Next number = 1 , SMA = 3 SIMPLE MOVING AVERAGE (class based): PERIOD = 3 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 3 Next number = 5 , SMA = 4 Next number = 5 , SMA = 4.66667 Next number = 4 , SMA = 4.66667 Next number = 3 , SMA = 4 Next number = 2 , SMA = 3 Next number = 1 , SMA = 2 SIMPLE MOVING AVERAGE (class based): PERIOD = 5 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 2.5 Next number = 5 , SMA = 3 Next number = 5 , SMA = 3.8 Next number = 4 , SMA = 4.2 Next number = 3 , SMA = 4.2 Next number = 2 , SMA = 3.8 Next number = 1 , SMA = 3 ``` ## R This is easiest done with two functions: one to handle the state (i.e. the numbers already entered), and one to calculate the average. ```R #concat concatenates the new values to the existing vector of values, then discards any values that are too old. lastvalues <- local( { values <- c(); function(x, len) { values <<- c(values, x); lenv <- length(values); if(lenv > len) values <<- values[(len-lenv):-1] values } }) #moving.average accepts a numeric scalars input (and optionally a length, i.e. the number of values to retain) and calculates the stateful moving average. moving.average <- function(latestvalue, len=3) { #Check that all inputs are numeric scalars is.numeric.scalar <- function(x) is.numeric(x) && length(x)==1L if(!is.numeric.scalar(latestvalue) || !is.numeric.scalar(len)) { stop("all arguments must be numeric scalars") } #Calculate mean of variables so far mean(lastvalues(latestvalue, len)) } moving.average(5) # 5 moving.average(1) # 3 moving.average(-3) # 1 moving.average(8) # 2 moving.average(7) # 4 ``` ## Racket ```Racket #lang racket (require data/queue) (define (simple-moving-average period) (define queue (make-queue)) (define sum 0.0) (lambda (x) (enqueue! queue x) (set! sum (+ sum x)) (when (> (queue-length queue) period) (set! sum (- sum (dequeue! queue)))) (/ sum (queue-length queue)))) ;; Tests (define sma3 (simple-moving-average 3)) (define sma5 (simple-moving-average 5)) (for/lists (lst1 lst2) ([i '(1 2 3 4 5 5 4 3 2 1)]) (values (sma3 i) (sma5 i))) ``` ## REXX The same list of numbers was used as in the '''ALGOL68''' example. The 1st and 2nd periods (number of values) were parametrized, as well as the total number of values. ```rexx /*REXX program illustrates and displays a simple moving average using a constructed list*/ parse arg p q n . /*obtain optional arguments from the CL*/ if p=='' | p=="," then p= 3 /*Not specified? Then use the default.*/ if q=='' | q=="," then q= 5 /* " " " " " " */ if n=='' | n=="," then n= 10 /* " " " " " " */ @.= 0 /*default value, only needed for odd N.*/ do j=1 for n%2; @.j= j /*build 1st half of list, increasing #s*/ end /*j*/ do k=n%2 by -1 to 1; @.j= k; j= j+1 /* " 2nd " " " decreasing " */ end /*k*/ say ' number ' " SMA with period" p' ' " SMA with period" q say ' ──────── ' "───────────────────" '───────────────────' pad=' ' do m=1 for n; say center(@.m, 10) pad left(SMA(p, m), 19) left(SMA(q, m), 19) end /*m*/ exit /*stick a fork in it, we're all done. */ /*──────────────────────────────────────────────────────────────────────────────────────*/ SMA: procedure expose @.; parse arg p,j; i= 0 ; \$= 0 do k=max(1, j-p+1) to j+p for p while k<=j; i= i + 1; \$= \$ + @.k end /*k*/ return \$/i /*SMA ≡ simple moving average. */ ``` {{out|output|text= when using the generated default input numbers:}} ```txt number SMA with period 3 SMA with period 5 ──────── ─────────────────── ─────────────────── 1 1 1 2 1.5 1.5 3 2 2 4 3 2.5 5 4 3 5 4.66666667 3.8 4 4.66666667 4.2 3 4 4.2 2 3 3.8 1 2 3 ``` ## Ring ### version 1 ```ring load "stdlib.ring" decimals(8) maxperiod = 20 nums = newlist(maxperiod,maxperiod) accum = list(maxperiod) index = list(maxperiod) window = list(maxperiod) for i = 1 to maxperiod index[i] = 1 accum[i] = 0 window[i] = 0 next for i = 1 to maxperiod for j = 1 to maxperiod nums[i][j] = 0 next next for n = 1 to 5 see "number = " + n + " sma3 = " + left((string(sma(n,3)) + " "),9) + " sma5 = " + sma(n,5) + nl next for n = 5 to 1 step -1 see "number = " + n + " sma3 = " + left((string(sma(n,3)) + " "),9) + " sma5 = " + sma(n,5) + nl next see nl func sma number, period accum[period] += number - nums[period][index[period]] nums[period][index[period]] = number index[period]= (index[period] + 1) % period + 1 if window[period] size ? nums.shift : 0 sum += hello - goodbye sum / nums.length end end ma3 = simple_moving_average(3) ma5 = simple_moving_average(5) (1.upto(5).to_a + 5.downto(1).to_a).each do |num| printf "Next number = %d, SMA_3 = %.3f, SMA_5 = %.1f\n", num, ma3.call(num), ma5.call(num) end ``` A class ```ruby class MovingAverager def initialize(size) @size = size @nums = [] @sum = 0.0 end def <<(hello) @nums << hello goodbye = @nums.length > @size ? @nums.shift : 0 @sum += hello - goodbye self end def average @sum / @nums.length end alias to_f average def to_s average.to_s end end ma3 = MovingAverager.new(3) ma5 = MovingAverager.new(5) (1.upto(5).to_a + 5.downto(1).to_a).each do |num| printf "Next number = %d, SMA_3 = %.3f, SMA_5 = %.1f\n", num, ma3 << num, ma5 < } impl SimpleMovingAverage { fn new(p: usize) -> SimpleMovingAverage { SimpleMovingAverage { period: p, numbers: Vec::new() } } fn add_number(&mut self, number: usize) -> f64 { self.numbers.push(number); if self.numbers.len() > self.period { self.numbers.remove(0); } if self.numbers.is_empty() { return 0f64; }else { let sum = self.numbers.iter().fold(0, |acc, x| acc+x); return sum as f64 / self.numbers.len() as f64; } } } fn main() { for period in [3, 5].iter() { println!("Moving average with period {}", period); let mut sma = SimpleMovingAverage::new(*period); for i in [1, 2, 3, 4, 5, 5, 4, 3, 2, 1].iter() { println!("Number: {} | Average: {}", i, sma.add_number(*i)); } } } ``` ===Double-ended Queue Based=== ```rust use std::collections::VecDeque; struct SimpleMovingAverage { period: usize, numbers: VecDeque } impl SimpleMovingAverage { fn new(p: usize) -> SimpleMovingAverage { SimpleMovingAverage { period: p, numbers: VecDeque::new() } } fn add_number(&mut self, number: usize) -> f64 { self.numbers.push_back(number); if self.numbers.len() > self.period { self.numbers.pop_front(); } if self.numbers.is_empty() { return 0f64; }else { let sum = self.numbers.iter().fold(0, |acc, x| acc+x); return sum as f64 / self.numbers.len() as f64; } } } fn main() { for period in [3, 5].iter() { println!("Moving average with period {}", period); let mut sma = SimpleMovingAverage::new(*period); for i in [1, 2, 3, 4, 5, 5, 4, 3, 2, 1].iter() { println!("Number: {} | Average: {}", i, sma.add_number(*i)); } } } ``` ```txt Moving average with period 3 Number: 1 | Average: 1 Number: 2 | Average: 1.5 Number: 3 | Average: 2 Number: 4 | Average: 3 Number: 5 | Average: 4 Number: 5 | Average: 4.666666666666667 Number: 4 | Average: 4.666666666666667 Number: 3 | Average: 4 Number: 2 | Average: 3 Number: 1 | Average: 2 Moving average with period 5 Number: 1 | Average: 1 Number: 2 | Average: 1.5 Number: 3 | Average: 2 Number: 4 | Average: 2.5 Number: 5 | Average: 3 Number: 5 | Average: 3.8 Number: 4 | Average: 4.2 Number: 3 | Average: 4.2 Number: 2 | Average: 3.8 Number: 1 | Average: 3 ``` ## Run Basic ```runbasic data 1,2,3,4,5,5,4,3,2,1 dim sd(10) ' series data global sd ' make it global so we all see it for i = 1 to 10:read sd(i): next i x = sma(3) ' simple moving average for 3 periods x = sma(5) ' simple moving average for 5 periods function sma(p) ' the simple moving average function print "----- SMA:";p;" -----" for i = 1 to 10 sumSd = 0 for j = max((i - p) + 1,1) to i sumSd = sumSd + sd(j) ' sum series data for the period next j if p > i then p1 = i else p1 = p print sd(i);" sma:";p;" ";sumSd / p1 next i end function ``` ```txt ----- SMA:3 ----- 1 sma:3 1 2 sma:3 1.5 3 sma:3 2 4 sma:3 3 5 sma:3 4 5 sma:3 4.6666665 4 sma:3 4.6666665 3 sma:3 4 2 sma:3 3 1 sma:3 2 ----- SMA:5 ----- 1 sma:5 1 2 sma:5 1.5 3 sma:5 2 4 sma:5 2.5 5 sma:5 3 5 sma:5 3.79999995 4 sma:5 4.1999998 3 sma:5 4.1999998 2 sma:5 3.79999995 1 sma:5 3 ``` ## Scala ```scala class MovingAverage(period: Int) { private var queue = new scala.collection.mutable.Queue[Double]() def apply(n: Double) = { queue.enqueue(n) if (queue.size > period) queue.dequeue queue.sum / queue.size } override def toString = queue.mkString("(", ", ", ")")+", period "+period+", average "+(queue.sum / queue.size) def clear = queue.clear } ``` ```txt scala> List(3,5) foreach { period => | println("SIMPLE MOVING AVERAGE: PERIOD = "+period) | val sma = new MovingAverage(period) | 1.0 to 5.0 by 1.0 foreach {i => println(" Next number = %-2g, SMA = %g " format (i, sma(i)))} | 5.0 to 1.0 by -1.0 foreach {i => println(" Next number = %-2g, SMA = %g " format (i, sma(i)))} | println(sma+"\n") | } SIMPLE MOVING AVERAGE: PERIOD = 3 Next number = 1.00000, SMA = 1.00000 Next number = 2.00000, SMA = 1.50000 Next number = 3.00000, SMA = 2.00000 Next number = 4.00000, SMA = 3.00000 Next number = 5.00000, SMA = 4.00000 Next number = 5.00000, SMA = 4.66667 Next number = 4.00000, SMA = 4.66667 Next number = 3.00000, SMA = 4.00000 Next number = 2.00000, SMA = 3.00000 Next number = 1.00000, SMA = 2.00000 (3.0, 2.0, 1.0), period 3, average 2.0 SIMPLE MOVING AVERAGE: PERIOD = 5 Next number = 1.00000, SMA = 1.00000 Next number = 2.00000, SMA = 1.50000 Next number = 3.00000, SMA = 2.00000 Next number = 4.00000, SMA = 2.50000 Next number = 5.00000, SMA = 3.00000 Next number = 5.00000, SMA = 3.80000 Next number = 4.00000, SMA = 4.20000 Next number = 3.00000, SMA = 4.20000 Next number = 2.00000, SMA = 3.80000 Next number = 1.00000, SMA = 3.00000 (5.0, 4.0, 3.0, 2.0, 1.0), period 5, average 3.0 ``` ## Scheme ```scheme (define ((simple-moving-averager size . nums) num) (set! nums (cons num (if (= (length nums) size) (reverse (cdr (reverse nums))) nums))) (/ (apply + nums) (length nums))) (define av (simple-moving-averager 3)) (map av '(1 2 3 4 5 5 4 3 2 1)) ``` {{out}} ```txt (1 3/2 2 3 4 14/3 14/3 4 3 2) ``` ## Sidef Implemented with closures: ```ruby func simple_moving_average(period) { var list = [] var sum = 0 func (number) { list.append(number) sum += number if (list.len > period) { sum -= list.shift } (sum / list.length) } } var ma3 = simple_moving_average(3) var ma5 = simple_moving_average(5) for num (1..5, flip(1..5)) { printf("Next number = %d, SMA_3 = %.3f, SMA_5 = %.1f\n", num, ma3.call(num), ma5.call(num)) } ``` Implemented as a class: ```ruby class sma_generator(period, list=[], sum=0) { method SMA(number) { list.append(number) sum += number if (list.len > period) { sum -= list.shift } (sum / list.len) } } var ma3 = sma_generator(3) var ma5 = sma_generator(5) for num (1..5, flip(1..5)) { printf("Next number = %d, SMA_3 = %.3f, SMA_5 = %.1f\n", num, ma3.SMA(num), ma5.SMA(num)) } ``` {{out}} ```txt Next number = 1, SMA_3 = 1.000, SMA_5 = 1.0 Next number = 2, SMA_3 = 1.500, SMA_5 = 1.5 Next number = 3, SMA_3 = 2.000, SMA_5 = 2.0 Next number = 4, SMA_3 = 3.000, SMA_5 = 2.5 Next number = 5, SMA_3 = 4.000, SMA_5 = 3.0 Next number = 5, SMA_3 = 4.667, SMA_5 = 3.8 Next number = 4, SMA_3 = 4.667, SMA_5 = 4.2 Next number = 3, SMA_3 = 4.000, SMA_5 = 4.2 Next number = 2, SMA_3 = 3.000, SMA_5 = 3.8 Next number = 1, SMA_3 = 2.000, SMA_5 = 3.0 ``` ## Smalltalk {{works with|GNU Smalltalk}} ```smalltalk Object subclass: MovingAverage [ |valueCollection period collectedNumber sum| MovingAverage class >> newWithPeriod: thePeriod [ |r| r := super basicNew. ^ r initWithPeriod: thePeriod ] initWithPeriod: thePeriod [ valueCollection := OrderedCollection new: thePeriod. period := thePeriod. collectedNumber := 0. sum := 0 ] sma [ collectedNumber < period ifTrue: [ ^ sum / collectedNumber ] ifFalse: [ ^ sum / period ] ] add: value [ collectedNumber < period ifTrue: [ sum := sum + value. valueCollection add: value. collectedNumber := collectedNumber + 1. ] ifFalse: [ sum := sum - (valueCollection removeFirst). sum := sum + value. valueCollection add: value ]. ^ self sma ] ]. ``` ```smalltalk |sma3 sma5| sma3 := MovingAverage newWithPeriod: 3. sma5 := MovingAverage newWithPeriod: 5. #( 1 2 3 4 5 5 4 3 2 1 ) do: [ :v | ('Next number %1, SMA_3 = %2, SMA_5 = %3' % { v . (sma3 add: v) asFloat . (sma5 add: v) asFloat }) displayNl ] ``` ## Tcl {{works with|Tcl|8.6}} or {{libheader|TclOO}} ```tcl oo::class create SimpleMovingAverage { variable vals idx constructor {{period 3}} { set idx end-[expr {\$period-1}] set vals {} } method val x { set vals [lrange [list {*}\$vals \$x] \$idx end] expr {[tcl::mathop::+ {*}\$vals]/double([llength \$vals])} } } ``` Demonstration: ```tcl SimpleMovingAverage create averager3 SimpleMovingAverage create averager5 5 foreach n {1 2 3 4 5 5 4 3 2 1} { puts "Next number = \$n, SMA_3 = [averager3 val \$n], SMA_5 = [averager5 val \$n]" } ``` {{out}} ```txt Next number = 1, SMA_3 = 1.0, SMA_5 = 1.0 Next number = 2, SMA_3 = 1.5, SMA_5 = 1.5 Next number = 3, SMA_3 = 2.0, SMA_5 = 2.0 Next number = 4, SMA_3 = 3.0, SMA_5 = 2.5 Next number = 5, SMA_3 = 4.0, SMA_5 = 3.0 Next number = 5, SMA_3 = 4.666666666666667, SMA_5 = 3.8 Next number = 4, SMA_3 = 4.666666666666667, SMA_5 = 4.2 Next number = 3, SMA_3 = 4.0, SMA_5 = 4.2 Next number = 2, SMA_3 = 3.0, SMA_5 = 3.8 Next number = 1, SMA_3 = 2.0, SMA_5 = 3.0 ``` =={{header|TI-83 BASIC}}== Continuously prompts for an input I, which is added to the end of a list L1. L1 can be found by pressing "2ND"/"1", and mean can be found in "List"/"OPS" Press ON to terminate the program. ```ti83b :1->C :While 1 :Prompt I :C->dim(L1) :I->L1(C) :Disp mean(L1) :1+C->C :End ``` =={{header|TI-89 BASIC}}== Function that returns a list containing the averaged data of the supplied argument ```ti89b movinavg(list,p) Func Local r, i, z For i,1,dim(list) max(i-p,0)→z sum(mid(list,z+1,i-z))/(i-z)→r[i] EndFor r EndFunc ``` Program that returns a simple value at each invocation: ```ti89b movinav2(x_,v_) Prgm If getType(x_)="STR" Then {}→list v_→p Return EndIf right(augment(list,{x_}),p)→list sum(list)/dim(list)→#v_ EndPrgm ``` Example1: Using the function movinavg({1,2,3,4,5,6,7,8,9,10},5) list is the list being averaged: {1,2,3,4,5,6,7,8,9,10} p is the period: 5 returns the averaged list: {1, 3/2, 2, 5/2, 3, 4, 5, 6, 7, 8} Example 2: Using the program movinav2("i",5) - Initializing moving average calculation, and define period of 5 movinav2(3, "x"):x - new data in the list (value 3), and result will be stored on variable x, and displayed movinav2(4, "x"):x - new data (value 4), and the new result will be stored on variable x, and displayed (4+3)/2 ... Description of the function movinavg: variable r - is the result (the averaged list) that will be returned variable i - is the index variable, and it points to the end of the sub-list the list being averaged. variable z - an helper variable The function uses variable i to determine which values of the list will be considered in the next average calculation. At every iteration, variable i points to the last value in the list that will be used in the average calculation. So we only need to figure out which will be the first value in the list. Usually we'll have to consider p elements, so the first element will be the one indexed by (i-p+1). However on the first iterations that calculation will usually be negative, so the following equation will avoid negative indexes: max(i-p+1,1) or, arranging the equation, max(i-p,0)+1. But the number of elements on the first iterations will also be smaller, the correct value will be (end index - begin index + 1) or, arranging the equation, (i - (max(i-p,0)+1) +1) ,and then, (i-max(i-p,0)). Variable z holds the common value (max(i-p),0) so the begin_index will be (z+1) and the number_of_elements will be (i-z) mid(list,z+1, i-z) will return the list of value that will be averaged sum(...) will sum them sum(...)/(i-z) → r[i] will average them and store the result in the appropriate place in the result list ## VBA This is a "simple" moving average. ```vb Class sma 'to be stored in a class module with name "sma" Private n As Integer 'period Private arr() As Double 'circular list Private index As Integer 'pointer into arr Private oldsma As Double Public Sub init(size As Integer) n = size ReDim arr(n - 1) index = 0 End Sub Public Function sma(number As Double) As Double sma = oldsma + (-arr(index) + number) / n oldsma = sma arr(index) = number index = (index + 1) Mod n End Function Normal module Public Sub main() s = [{1,2,3,4,5,5,4,3,2,1}] Dim sma3 As New sma Dim sma5 As New sma sma3.init 3 sma5.init 5 For i = 1 To UBound(s) Debug.Print i, Format(sma3.sma(CDbl(s(i))), "0.00000"), Debug.Print Format(sma5.sma(CDbl(s(i))), "0.00000") Next i End Sub ``` {{out}} ```txt 1 0,33333 0,20000 2 1,00000 0,60000 3 2,00000 1,20000 4 3,00000 2,00000 5 4,00000 3,00000 6 4,66667 3,80000 7 4,66667 4,20000 8 4,00000 4,20000 9 3,00000 3,80000 10 2,00000 3,00000 ``` ## VBScript ```vb data = "1,2,3,4,5,5,4,3,2,1" token = Split(data,",") stream = "" WScript.StdOut.WriteLine "Number" & vbTab & "SMA3" & vbTab & "SMA5" For j = LBound(token) To UBound(token) If Len(stream) = 0 Then stream = token(j) Else stream = stream & "," & token(j) End If WScript.StdOut.WriteLine token(j) & vbTab & Round(SMA(stream,3),2) & vbTab & Round(SMA(stream,5),2) Next Function SMA(s,p) If Len(s) = 0 Then SMA = 0 Exit Function End If d = Split(s,",") sum = 0 If UBound(d) + 1 >= p Then c = 0 For i = UBound(d) To LBound(d) Step -1 sum = sum + Int(d(i)) c = c + 1 If c = p Then Exit For End If Next SMA = sum / p Else For i = UBound(d) To LBound(d) Step -1 sum = sum + Int(d(i)) Next SMA = sum / (UBound(d) + 1) End If End Function ``` {{Out}} ```txt Number SMA3 SMA5 1 1 1 2 1.5 1.5 3 2 2 4 3 2.5 5 4 3 5 4.67 3.8 4 4.67 4.2 3 4 4.2 2 3 3.8 1 2 3 ``` ## zkl ```zkl fcn SMA(P){ fcn(n,ns,P){ sz:=ns.append(n.toFloat()).len(); if(P>sz) return(0.0); if(P ```
``` Created by Adrian Sieber and contributors with Zola Licensed under the GFDL-1.3-or-later ```
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