# Averages/Simple moving average

Computing the simple moving average of a series of numbers.

Averages/Simple moving average
You are encouraged to solve this task according to the task description, using any language you may know.

Create a 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.

Description

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

Translation of: 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]
Output:
```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

Translation of: 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
YREGS
END    AVGSMA```
Output:
``` 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
function Moving_Average (N : Number) return Number;
function Get_Average return Number;
end Moving;
```

```with Ada.Containers.Vectors;

package body Moving is

(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;
```
Output:
```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

Translation of: C
Works with: ALGOL 68 version Standard - no extensions to language used
Works with: ALGOL 68G version Any - tested with release 1.18.0-9h.tiny

Note: This following code is a direct translation of the 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),
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))
#
)```
Output:
```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: 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

```      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%)
```
Output:
```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
```

## BQN

`SMA` takes moving average of a list, given the whole array.

`SMA2` returns a stateful function which can be run on individual numbers of a stream.

```SMA ← {(+´÷≠)¨(1↓𝕨↑↑𝕩)∾<˘𝕨↕𝕩}

v ← (⊢∾⌽)1+↕5
•Show 5 SMA v

SMA2 ← {
𝕊 size:
nums ← ⟨⟩
sum ← 0
{
nums ∾↩ 𝕩
gb ← {(≠nums)≤size ? 0 ; a←⊑nums, nums↩1↓nums, a}
sum +↩ 𝕩 - gb
sum ÷ ≠nums
}
}

fun ← SMA2 5
Fun¨ v```
```⟨ 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3 ⟩
⟨ 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 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
)
);```
Output:
```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
}

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 |
}```

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)
}```
Output:
```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",
}

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

## C#

Works with: C# version 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();
};
}
}
}
```
Output:
```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
```

## C++

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

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

class SMA {
public:
SMA(unsigned int period) :
total(0) {
assert(period >= 1);
}
~SMA() {
delete[] window;
}

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

// 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 {
return 0;
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;
}
```

## 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
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)
```
Output:
```> 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))))
```

Use

```(mapcar '(simple-moving-average period) list-of-values)
```

## Crystal

```def sma(n) Proc(Float64, Float64)
a = Array(Float64).new
->(x : Float64) {
a.shift if a.size == n
a.push x
a.sum / a.size.to_f
}
end

sma3, sma5 = sma(3), sma(5)

# Copied from the Ruby solution.
(1.upto(5).to_a + 5.downto(1).to_a).each do |n|
printf "%d: sma3 = %.3f - sma5 = %.3f\n", n, sma3.call(n.to_f), sma5.call(n.to_f)
end
```
```1: sma3 = 1.000 - sma5 = 1.000
2: sma3 = 1.500 - sma5 = 1.500
3: sma3 = 2.000 - sma5 = 2.000
4: sma3 = 3.000 - sma5 = 2.500
5: sma3 = 4.000 - sma5 = 3.000
5: sma3 = 4.667 - sma5 = 3.800
4: sma3 = 4.667 - sma5 = 4.200
3: sma3 = 4.000 - sma5 = 4.200
2: sma3 = 3.000 - sma5 = 3.800
1: sma3 = 2.000 - sma5 = 3.000
```

## 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));
}
```
Output:
```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.

## Delphi

Translation of: Pascal

Small variation of #Pascal.

```program Simple_moving_average;

{\$APPTYPE CONSOLE}

type
TMovingAverage = record
private
buffer: TArray<Double>;
Capacity: Integer;
Count: Integer;
sum, fValue: Double;
public
constructor Create(aCapacity: Integer);
procedure Reset;
end;

{ TMovingAverage }

begin
sum := sum + Value - buffer[head];

if count < capacity then
begin
inc(Count);
fValue := sum / count;
exit(fValue);
end;
fValue := sum / Capacity;
Result := fValue;
end;

constructor TMovingAverage.Create(aCapacity: Integer);
begin
Capacity := aCapacity;
SetLength(buffer, aCapacity);
Reset;
end;

procedure TMovingAverage.Reset;
var
i: integer;
begin
Count := 0;
sum := 0;
fValue := 0;
for i := 0 to High(buffer) do
buffer[i] := 0;
end;

var
avg3, avg5: TMovingAverage;
i: Integer;

begin
avg3 := TMovingAverage.Create(3);
avg5 := TMovingAverage.Create(5);

for i := 1 to 5 do
begin
write('Inserting ', i, ' into avg3 ', avg3.Add(i): 0: 4);
writeln(' Inserting ', i, ' into avg5 ', avg5.Add(i): 0: 4);
end;

for i := 5 downto 1 do
begin
write('Inserting ', i, ' into avg3 ', avg3.Add(i): 0: 4);
writeln(' Inserting ', i, ' into avg5 ', avg5.Add(i): 0: 4);
end;

avg3.Reset;
for i := 1 to 100000000 do
writeln('100''000''000 insertions ', avg3.Value: 0: 4);

end.
```
Output:
```Inserting 1 into avg3 1.0000 Inserting 1 into avg5 1.0000
Inserting 2 into avg3 1.5000 Inserting 2 into avg5 1.5000
Inserting 3 into avg3 2.0000 Inserting 3 into avg5 2.0000
Inserting 4 into avg3 3.0000 Inserting 4 into avg5 2.5000
Inserting 5 into avg3 4.0000 Inserting 5 into avg5 3.0000
Inserting 5 into avg3 4.6667 Inserting 5 into avg5 3.8000
Inserting 4 into avg3 4.6667 Inserting 4 into avg5 4.2000
Inserting 3 into avg3 4.0000 Inserting 3 into avg5 4.2000
Inserting 2 into avg3 3.0000 Inserting 2 into avg5 3.8000
Inserting 1 into avg3 2.0000 Inserting 1 into avg5 3.0000
100'000'000 insertions 99999999.0000
```

## Dyalect

Translation of: 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.Length() >= p {
s.RemoveAt(0)
}
s.Insert(s.Length(), x)
avg(s)
};
}

var nums = Iterator.Concat(1.0..5.0, 5.0^-1.0..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]
}```
```? 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```

## EchoLisp

```(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))))
```
Output:
```(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 5.0 :

```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(Real.new());

^ 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)
};

}```
Output:
```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.
```
Output:
```9> 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 ]```

## F#

```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 ""
```
Output:
```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
```
Output:
```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
{
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}")
}
}
}```
Output:
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 ;
dup sum float+ swap head @ floats + ;

dup ring f@ fnegate dup sum f+!
fdup dup ring f!         dup sum f+!

: 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 version 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
```
Output:
```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(period int) func(float64) float64 {
var i int
var sum float64
var storage = make([]float64, 0, period)

return func(input float64) (avrg float64) {
if len(storage) < period {
sum += input
storage = append(storage, input)
}

sum += input - storage[i]
storage[i], i = input, (i+1)%period
avrg = sum / float64(len(storage))

return
}
}

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))
}
}
```
Output:
```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

Translation of: 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)) }
```
Output:
`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 version 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]
```
Output:
```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 version 6.10.4
```import Data.List
import Control.Arrow

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 version 7.8.3
```import Control.Monad

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 []
```
Output:
```[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```

## Icon and Unicon

```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 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 `(+/%#)\`, 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 implement it. Thus the 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 version 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;
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();
}
}
}
```
Output:
```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));
}
```
Output:
```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

```// 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));
```
Output:
```[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

```using Statistics
```

The function wants specified the type of the data in the buffer and, if you want, the limit of the buffer.

```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])
```
Output:
```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 {
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)}")
}
```
Output:
```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

 This example is incorrect. Please fix the code and remove this message.Details: 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'
^}
```
Output:
```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
```

## Mathematica / 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]]
```

```MAData = {{}, 0};
MAS[x_, t_: Null] :=
With[{r = If[t === Null, MAData[[2]], t]},
If[Length[#] > (MAData[[2]] = r), #[[-r ;; -1]], #] &@
```

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
```

## MATLAB / 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, 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
```
Output:
```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

Translation of: 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)
period = period_
sum    = 0
return

-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
method newNum(num) public
sum = sum + 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
```
Output:
```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

```import deques

proc simplemovingaverage(period: int): auto =
assert period > 0

var
summ, n = 0.0
values: Deque[float]
for i in 1..period:

proc sma(x: float): float =
summ += x - values.popFirst()
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))
```
Output:
```1.0
1.5
2.0
3.0
4.0
4.666666666666667
4.666666666666667
4.0
3.0
2.0

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

## Objeck

Translation of: Java
```﻿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;
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();
};
}
}```
Output:
```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
```

## Objective-C

```#import <Foundation/Foundation.h>

@interface MovingAverage : NSObject {
unsigned int period;
NSMutableArray *window;
double sum;
}
- (instancetype)initWithPeriod:(unsigned int)thePeriod;
@end

@implementation MovingAverage

// init with default period
- (instancetype)init {
self = [super init];
if(self) {
period = 10;
window = [[NSMutableArray alloc] init];
sum = 0.0;
}
return self;
}

// init with specified period
- (instancetype)initWithPeriod:(unsigned int)thePeriod {
self = [super init];
if(self) {
period = thePeriod;
window = [[NSMutableArray alloc] init];
sum = 0.0;
}
return self;
}

// add a new number to the window
sum += val;
if([window count] > 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) {
NSLog(@"Next number = %f, SMA = %f", testData[j], [ma avg]);
}
NSLog(@"\n");
}
}
return 0;
}
```
Output:
```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

```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
```
Output:
```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:

```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.

```import: parallel

: createSMA(period)
| ch |
Channel new [ ] over send drop ->ch
#[ ch receive + left(period) dup avg swap ch send drop ] ;```

Usage:

```: 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 . ] ) ;```
Output:
```>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.

```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
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
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
```
Output:
```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

```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

```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.

```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.

```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;

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;
end;
end;

var
sma3,sma5:tsma;
i : LongInt;
begin
smaInit(sma3,3);
smaInit(sma5,5);
For i := 1 to 5 do
Begin
writeln(' Inserting ',i,' into sma5 ',smaAddValue(sma5,i):0:4);
end;
For i := 5 downto 1 do
Begin
writeln(' Inserting ',i,' into sma5 ',smaAddValue(sma5,i):0:4);
end;
//speed test
smaInit(sma3,3);
For i := 1 to 100000000 do
writeln('100''000''000 insertions ',sma3.smaAverage:0:4);
end.
```
output
```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`:

```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->(\$_);
}
```
Output:
```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)
```

## 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.

```with javascript_semantics
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
history = append(history,val)
else
circnxt += 1
if circnxt>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)
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:

```with javascript_semantics
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
```
Output:
``` 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
```

## Picat

```main =>
L=[1, 2, 3, 4, 5, 5, 4, 3, 2, 1],
Map3 = new_map([p=3]),
Map5 = new_map([p=5]),
foreach(N in L)
printf("n: %-2d sma3: %-17w sma5: %-17w\n",N, sma(N,Map3), sma(N,Map5))
end.

sma(N,Map) = Average =>
Stream = Map.get(stream,[]) ++ [N],
if Stream.len > Map.get(p) then
Stream := Stream.tail
end,
Average = cond(Stream.len == 0,
0,
sum(Stream) / Stream.len),
Map.put(stream,Stream).```
Output:
```n: 1  sma3: 1.0               sma5: 1.0
n: 2  sma3: 1.5               sma5: 1.5
n: 3  sma3: 2.0               sma5: 2.0
n: 4  sma3: 3.0               sma5: 2.5
n: 5  sma3: 4.0               sma5: 3.0
n: 5  sma3: 4.666666666666667 sma5: 3.8
n: 4  sma3: 4.666666666666667 sma5: 4.2
n: 3  sma3: 4.0               sma5: 4.2
n: 2  sma3: 3.0               sma5: 3.8
n: 1  sma3: 2.0               sma5: 3.0```

## PicoLisp

```(de sma (@Len)
(curry (@Len (Data)) (N)
(push 'Data N)
(and (nth Data @Len) (con @))  # Truncate
(*/ (apply + Data) (length Data)) ) )```
```(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) ) )```
Output:
```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

```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

Translation of: REXX
```*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;```
Output:
```            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```

## Pony

```class MovingAverage
let period: USize
let _arr: Array[I32] // circular buffer
var _curr: USize  // index of pointer position
var _total: I32   // cache the total so far

new create(period': USize) =>
period = period'
_arr = Array[I32](period) // preallocate space
_curr = 0
_total = 0

fun ref apply(n: I32): F32 =>
_total = _total + n
if _arr.size() < period then
_arr.push(n)
else
try
let prev = _arr.update(_curr, n)?
_total = _total - prev
_curr = (_curr + 1) % period
end
end
_total.f32() / _arr.size().f32()

// ---- TESTING -----
actor Main
new create(env: Env) =>
let foo = MovingAverage(3)
let bar = MovingAverage(5)
let data: Array[I32] = [1; 2; 3; 4; 5; 5; 4; 3; 2; 1]
for v in data.values() do
env.out.print("Foo: " + foo(v).string())
end
for v in data.values() do
env.out.print("Bar: " + bar(v).string())
end
```

## PowerShell

```#This version allows a user to enter numbers one at a time to figure this into the SMA calculations

\$inputs = @() #Create an array to hold all inputs as they are entered.
\$period1 = 3 #Define the periods you want to utilize
\$period2 = 5

Write-host "Enter numbers to observe their moving averages." -ForegroundColor Green

function getSMA (\$inputs, [int]\$period) #Function takes a array of entered values and a period (3 and 5 in this case)
{
if(\$inputs.Count -lt \$period){\$period = \$inputs.Count} #Makes sure that if there's less numbers than the designated period (3 in this case), the number of availble values is used as the period instead.

for(\$count = 0; \$count -lt \$period; \$count++) #Loop sums the latest available values
{
\$result += \$inputs[(\$inputs.Count) - \$count - 1]
}

return (\$result | ForEach-Object -begin {\$sum=0 }-process {\$sum+=\$_} -end {\$sum/\$period}) #Gets the average for a given period
}

while(\$true) #Infinite loop so the user can keep entering numbers
{
try{\$inputs += [decimal] (Read-Host)}catch{Write-Host "Enter only numbers" -ForegroundColor Red} #Enter the numbers. Error checking to help mitigate bad inputs (non-number values)

"Added " + \$inputs[((\$inputs.Count) - 1)] + ", sma(\$period1) = " + (getSMA \$inputs \$Period1) + ", sma(\$period2) = " + (getSMA \$inputs \$period2)
}
```

## PureBasic

```Procedure.d SMA(Number, Period=0)
Static P
Static NewList L()
Protected Sum=0
If Period<>0
P=Period
EndIf
LastElement(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 version 3.x

Both implementations use the deque datatype.

### Procedural

```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

```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

```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)))
```
Output:
```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 ```

## Quackery

```  [ \$ "bigrat.qky" loadfile ] now!

[ over size -
space swap of
join ]                 is pad      ( \$ n --> \$ )

[ ' [ stack [ ] ]
copy nested
' [ tuck take swap join
dup size ] join
swap join
' [ > if
[ 1 split nip ]
tuck swap put
0 over witheach +
swap size
dip n->v n->v v/ ]
join copy ]            is make-sma (   n --> [ )
( behaviour of [ is: n --> n/d )

[ stack ]                is sma-3    (     --> s )
3 make-sma sma-3 put

[ stack ]                is sma-5    (     --> s )
5 make-sma sma-5 put

say "n sma-3      sma-5" cr cr
' [ 1 2 3 4 5 5 4 3 2 1 ]
witheach
[ dup echo sp
dup sma-3 share do
7 point\$ 10 pad echo\$ sp
sma-5 share do
7 point\$ 10 pad echo\$ cr ]```
Output:
```n sma-3      sma-5

1 1          1
2 1.5        1.5
3 2          2
4 3          2.5
5 4          3
5 4.6666667  3.8
4 4.6666667  4.2
3 4          4.2
2 3          3.8
1 2          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.

```#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

```#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)))
```

## Raku

(formerly Perl 6)

Works with: Rakudo version 2016.08
```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 \$_;
}
```
Output:
```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)
```

## 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 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 ' ──────── '     "───────────────────"     '───────────────────'
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.     */
```
output   when using the generated default input numbers:
```  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

```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]<period window[period] += 1 ok
return (accum[period] / window[period])```

Output:

```number = 1  sma3 = 1          sma5 = 1
number = 2  sma3 = 1.5000000  sma5 = 1.50000000
number = 3  sma3 = 2          sma5 = 2
number = 4  sma3 = 3          sma5 = 2.50000000
number = 5  sma3 = 4          sma5 = 3
number = 5  sma3 = 4.6666666  sma5 = 3.80000000
number = 4  sma3 = 4.6666666  sma5 = 4.20000000
number = 3  sma3 = 4          sma5 = 4.20000000
number = 2  sma3 = 3          sma5 = 3.80000000
number = 1  sma3 = 2          sma5 = 3
```

### version 2

```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]<period window[period] += 1 ok
return (accum[period] / window[period])```

Output:

```number = 1  sma3 = 1          sma5 = 1
number = 2  sma3 = 1.5000000  sma5 = 1.50000000
number = 3  sma3 = 2          sma5 = 2
number = 4  sma3 = 3          sma5 = 2.50000000
number = 5  sma3 = 4          sma5 = 3
number = 5  sma3 = 4.6666666  sma5 = 3.80000000
number = 4  sma3 = 4.6666666  sma5 = 4.20000000
number = 3  sma3 = 4          sma5 = 4.20000000
number = 2  sma3 = 3          sma5 = 3.80000000
number = 1  sma3 = 2          sma5 = 3
```

### version 3

```### RING: Function Moving Average.   Bert Mariani 2016-06-22

###------------------------------
### Data array of Google prices

aGOOGPrices = ["658","675","670","664","664","663","663","662","675","693","689","675",
"636","633","632","607","607","617","617","581","593","570","574","571","575","596",
"596","601","583","635","587","574","552","531","536","502","488","482","490","503",
"507","521","534","525","534","559","552","554","555","555","552","579","580","577",
"575","562","560","559","558","569","573","577","574","559","552","553","560","569",
"582","579","593","598","593","598","593","586","602","591","594","595","603","614",
"620","625","635","627","632","631","620","626","616","606","602","659","683","671",
"670","659","673","679"]

###-------------------------------------------------------------
### CALL the Function:  MovingAverage  arrayOfPrices timePeriod

aGOOGMvgAvg = MovingAverage( aGOOGPrices, 10 )

aGOOGMvgAvg = MovingAverage( aGOOGPrices, 30 )

###-------------------------------------------------------------
### FUNCTION: MovingAverage

Func MovingAverage arrayPrices, timePeriod

arrayMvgAvg  = []             ### Output Results to this array
z = len(arrayPrices)          ### array data length
sumPrices  = 0

###--------------------------------
### First MAvg Sum 1 to timePeriod
###--------------------------------

for i = 1 to  timePeriod
sumPrices = sumPrices + arrayPrices[i]
mvgAvg    = sumPrices / i
next

###-----------------------------------------------
### Second MAvg Sum  timePeriod +1 to End of Data
###-----------------------------------------------

for i = timePeriod + 1 to z
sumPrices = sumPrices - arrayPrices[i-timePeriod] + arrayPrices[i]
mvgAvg    = sumPrices / timePeriod
next

return arrayMvgAvg

###-------------------------------------------------------------
OUTPUT Google Prices moving average using timePeriod = 10

Index 88 CurPrice 631 Sum 17735 MvgAvg 591.17
Index 89 CurPrice 620 Sum 17797 MvgAvg 593.23
Index 90 CurPrice 626 Sum 17854 MvgAvg 595.13
Index 91 CurPrice 616 Sum 17897 MvgAvg 596.57
Index 92 CurPrice 606 Sum 17926 MvgAvg 597.53
Index 93 CurPrice 602 Sum 17954 MvgAvg 598.47
Index 94 CurPrice 659 Sum 18054 MvgAvg 601.80
Index 95 CurPrice 683 Sum 18185 MvgAvg 606.17
Index 96 CurPrice 671 Sum 18303 MvgAvg 610.10
Index 97 CurPrice 670 Sum 18413 MvgAvg 613.77
Index 98 CurPrice 659 Sum 18503 MvgAvg 616.77
Index 99 CurPrice 673 Sum 18594 MvgAvg 619.80
Index 100 CurPrice 679 Sum 18694 MvgAvg 623.13
###-------------------------------------------------------------```

## Ruby

A closure:

```def simple_moving_average(size)
nums = []
sum = 0.0
lambda do |hello|
nums << hello
goodbye = nums.length > 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

```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 <<num
end
end
```

## Run Basic

```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```
```----- 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```

## Rust

### Vector Based

```struct SimpleMovingAverage {
period: usize,
numbers: Vec<usize>
}

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

```use std::collections::VecDeque;

struct SimpleMovingAverage {
period: usize,
numbers: VecDeque<usize>
}

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));
}
}
}
```
```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
```

## 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
}
```
```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

```(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))
```
Output:
```(1 3/2 2 3 4 14/3 14/3 4 3 2)
```

## Sidef

Implemented with closures:

```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:

```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))
}
```
Output:
```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
```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 ] ]
collectedNumber < period
ifTrue: [
sum := sum + value.
collectedNumber := collectedNumber + 1.
]
ifFalse: [
sum := sum - (valueCollection removeFirst).
sum := sum + value.
].
^ self sma
]
].
```
```|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' % {
}) displayNl
]
```

## Swift

Translation of: Rust
```struct SimpleMovingAverage {
var period: Int
var numbers = [Double]()

mutating func addNumber(_ n: Double) -> Double {
numbers.append(n)

if numbers.count > period {
numbers.removeFirst()
}

guard !numbers.isEmpty else {
return 0
}

return numbers.reduce(0, +) / Double(numbers.count)
}
}

for period in [3, 5] {
print("Moving average with period \(period)")

var averager = SimpleMovingAverage(period: period)

for n in [1.0, 2, 3, 4, 5, 5, 4, 3, 2, 1] {
}
}
```
Output:
```Moving average with period 3
n: 1.0; average 1.0
n: 2.0; average 1.5
n: 3.0; average 2.0
n: 4.0; average 3.0
n: 5.0; average 4.0
n: 5.0; average 4.666666666666667
n: 4.0; average 4.666666666666667
n: 3.0; average 4.0
n: 2.0; average 3.0
n: 1.0; average 2.0
Moving average with period 5
n: 1.0; average 1.0
n: 2.0; average 1.5
n: 3.0; average 2.0
n: 4.0; average 2.5
n: 5.0; average 3.0
n: 5.0; average 3.8
n: 4.0; average 4.2
n: 3.0; average 4.2
n: 2.0; average 3.8
n: 1.0; average 3.0```

## Tcl

Works with: Tcl version 8.6
or
Library: TclOO
```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:

```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]"
}
```
Output:
```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```

## 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.

```:1->C
:While 1
:Prompt I
:C->dim(L1)
:I->L1(C)
:Disp mean(L1)
:1+C->C
:End```

## TI-89 BASIC

Function that returns a list containing the averaged data of the supplied argument

```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:

```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.

```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
```
Output:
``` 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

```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
```
Output:
```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
```

## V (Vlang)

Translation of: Go
```fn sma(period int) fn(f64) f64 {
mut i := int(0)
mut sum := f64(0)
mut storage := []f64{len: 0, cap:period}

return fn[mut storage, mut sum, mut i, period](input f64) f64 {
if storage.len < period {
sum += input
storage << input
}

sum += input - storage[i]
storage[i], i = input, (i+1)%period
return sum / f64(storage.len)
}
}

fn main() {
sma3 := sma(3)
sma5 := sma(5)
println("x       sma3   sma5")
for x in [f64(1), 2, 3, 4, 5, 5, 4, 3, 2, 1] {
println("\${x:5.3f}  \${sma3(x):5.3f}  \${sma5(x):5.3f}")
}
}```
Output:
```  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
```

## Wren

Translation of: Go
Library: Wren-fmt
```import "/fmt" for Fmt

var sma = Fn.new { |period|
var i = 0
var sum = 0
var storage = []
return Fn.new { |input|
if (storage.count < period) {
sum = sum + input
}
sum = sum + input - storage[i]
storage[i] = input
i = (i+1) % period
return sum/storage.count
}
}

var sma3 = sma.call(3)
var sma5 = sma.call(5)
System.print("  x     sma3   sma5")
for (x in [1, 2, 3, 4, 5, 5, 4, 3, 2, 1]) {
Fmt.precision = 3
System.print("%(Fmt.f(5, x))  %(Fmt.f(5, sma3.call(x)))  %(Fmt.f(5, sma5.call(x)))")
}
```
Output:
```  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
```

## zkl

```fcn SMA(P){
fcn(n,ns,P){
sz:=ns.append(n.toFloat()).len();
if(P>sz) return(0.0);
if(P<sz) ns.del(0);
ns.sum(0.0)/P;
}.fp1(List.createLong(P+1),P)  // pre-allocate a list of length P+1
}```

fp1 creates a partial application fixing the (in this case) the second and third parameters

```T(1,2,3,4,5,5,4,3,2,1).apply(SMA(3)).println();
T(1,2,3,4,5,5,4,3,2,1).apply(SMA(5)).println();```
Output:
```L(0,0,2,3,4,4.66667,4.66667,4,3,2)
L(0,0,0,0,3,3.8,4.2,4.2,3.8,3)
```