Averages/Simple moving average

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

Computing the simple moving average of a series of numbers.

Task

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
See also

11l[edit]

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

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

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

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

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

Ada[edit]

Works with: Ada 2005

moving.ads:

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

moving.adb:

with Ada.Containers.Vectors;

package body Moving is
   use Ada.Containers;

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

   Current_List : Number_Vectors.Vector := Number_Vectors.Empty_Vector;

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

   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
      Add_Number (N);
      return Get_Average;
   end Moving_Average;

end Moving;

main.adb:

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[edit]

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),
     SMAADD = STRUCT(REF SMAOBJ add obj, LONG REAL v),
     SMAMEAN = STRUCT(REF SMAOBJ mean obj, REF[]LONG REAL v);

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

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

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

[]LONG REAL v = ( 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 );
 
main: (
  INT i;
 
  REF SMAOBJ h3 := ( sma(SMANEW(3)) | (REF SMAOBJ obj):obj );
  REF SMAOBJ h5 := ( sma(SMANEW(5)) | (REF SMAOBJ obj):obj );
 
  FOR i FROM LWB v TO UPB v DO
    printf(($"next number "g(0,6)", SMA_3 = "g(0,6)", SMA_5 = "g(0,6)l$,
           v[i], (sma(SMAADD(h3, v[i]))|(LONG REAL r):r), ( sma(SMAADD(h5, v[i])) | (LONG REAL r):r )
    ))
  OD#;
 
  sma(SMAFREE(h3));
  sma(SMAFREE(h5))
#
)
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[edit]

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[edit]

#!/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[edit]

      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[edit]

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 ⟩

Try It!

Bracmat[edit]

( ( 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.)
          )
  )
& ( pad
  =   len w
    .   @(!arg:? [?len)
      & @("     ":? [!len ?w)
      & !w !arg
  )
& I$3:(=?sma3)
& I$5:(=?sma5)
& 1 2 3 4 5 5 4 3 2 1:?K
&   whl
  ' ( !K:%?k ?K
    &   out
      $ (str$(!k " - sma3:" pad$(sma3$!k) "  sma5:" pad$(sma5$!k)))
    )
);
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[edit]

Object version

SMA = object.new

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

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

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

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

Function version

sma = { period |
  list = []

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

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

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

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

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

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

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

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

int main()
{
  int i;

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

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

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

C#[edit]

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++[edit]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

	return 0;
}

Clojure[edit]

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[edit]

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
      cq.add(n)
      num_elems += 1
      sum / num_elems
    else
      old = cq.replace(n)
      sum -= old
      sum / P
 
circular_queue = (n) ->
  # queue that only ever stores up to n values;
  # Caller shouldn't call replace until n values
  # have been added.
  i = 0
  arr = []
  
  add: (elem) ->
    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[edit]

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[edit]

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[edit]

Using a Closure[edit]

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[edit]

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[edit]

Translation of: Pascal

Small variation of #Pascal.

program Simple_moving_average;

{$APPTYPE CONSOLE}

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

{ TMovingAverage }

function TMovingAverage.Add(Value: Double): Double;
begin
  head := (head + 1) mod Capacity;
  sum := sum + Value - buffer[head];
  buffer[head] := Value;

  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
  head := -1;
  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
    avg3.Add(i);
  writeln('100''000''000 insertions ', avg3.Value: 0: 4);

  Readln;
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[edit]

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[edit]

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[edit]

(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[edit]

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)
    };
    
    console.readChar()
}
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[edit]

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

  def read_numeric_input do
    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)
    numbers = read_numeric_input
    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[edit]

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) ->
    receive
        {_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},
    receive
        {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[edit]

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#[edit]

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[edit]

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
        100 random sma-add
    ] 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[edit]

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

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

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

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

    10.times |i|
    {
      echo ("After $i numbers list is ${av.stream} average is ${av.average}")
      av.addNumber (Int.random(0..100))
    }
  }
}
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[edit]

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

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

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

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

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

Fortran[edit]

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[edit]

' 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[edit]

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[edit]

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[edit]

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 

Haskell[edit]

Conform version to the requirement, function SMA called multiple times with just a number:

Works with: GHC version 6.10.4
{-# LANGUAGE BangPatterns #-}

import Control.Monad
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
import Control.Monad

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

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

Stateful function using the state monad to keep track of state

Works with: GHC version 7.8.3
import Control.Monad
import Control.Monad.State

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[edit]

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[edit]

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[edit]

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[edit]

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;
        window.add(num);
        if (window.size() > period) {
            sum -= window.remove();
        }
    }

    public double getAvg() {
        if (window.isEmpty()) return 0.0; // technically the average is undefined
        return sum / window.size();
    }

    public static void main(String[] args) {
        double[] testData = {1, 2, 3, 4, 5, 5, 4, 3, 2, 1};
        int[] windowSizes = {3, 5};
        for (int windSize : windowSizes) {
            MovingAverage ma = new MovingAverage(windSize);
            for (double x : testData) {
                ma.newNum(x);
                System.out.println("Next number = " + x + ", SMA = " + ma.getAvg());
            }
            System.out.println();
        }
    }
}
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[edit]

Using for loop[edit]

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[edit]

JS Fiddle

// single-sided
Array.prototype.simpleSMA=function(N) {
return this.map(
  function(el,index, _arr) { 
      return _arr.filter(
      function(x2,i2) { 
        return i2 <= index && i2 > index - N;
        })
      .reduce(
      function(current, last, index, arr){ 
        return (current + last); 
        })/index || 1;
      }); 
};

g=[0,1,2,3,4,5,6,7,8,9,10];
console.log(g.simpleSMA(3));
console.log(g.simpleSMA(5));
console.log(g.simpleSMA(g.length));
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[edit]

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[edit]

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[edit]

// version 1.0.6

fun initMovingAverage(p: Int): (Double) -> Double {
    if (p < 1) throw IllegalArgumentException("Period must be a positive integer")
    val list = mutableListOf<Double>()
    return { 
        list.add(it)
        if (list.size > p) list.removeAt(0)
        list.average()
    }
}

fun main(args: Array<String>) {
    val sma4 = initMovingAverage(4)
    val sma5 = initMovingAverage(5)
    val numbers = listOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0)
    println("num\tsma4\tsma5\n")
    for (number in numbers) println("${number}\t${sma4(number)}\t${sma5(number)}")   
}
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[edit]

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[edit]

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

[edit]

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[edit]

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[edit]

This version uses a list entry so it can use the built-in function.

MA[x_List, r_] := Join[Table[Mean[x[[1;;y]]],{y,r-1}], MovingAverage[x,r]]

This version is stateful instead.

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

Tests:

MA[{1, 2, 3, 4, 5, 5, 4, 3, 2, 1}, 5]
=> {1, 3/2, 2, 5/2, 3, 19/5, 21/5, 21/5, 19/5, 3}

MAS[1, 5]  => 1
MAS[2]     => 3/2
MAS[3]     => 2
MAS[4]     => 5/2
MAS[5]     => 3
MAS[5]     => 19/5
MAS[4]     => 21/5
MAS[3]     => 21/5
MAS[2]     => 19/5
MAS[1]     => 3

MATLAB / Octave[edit]

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[edit]

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[edit]

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[edit]

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)
    window = LinkedList()
    period = period_
    sum    = 0
    return

  -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  method newNum(num) public
    sum = sum + num
    window.add(num)
    if window.size() > period then do
      rmv = (Rexx window.remove())
      sum = sum - rmv
      end
    return

  -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  method getAvg() public returns Rexx
    if window.isEmpty() then do
      avg = 0
      end
    else do
      avg = sum / window.size()
      end
    return avg

  -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  method run_samples(args = String[]) public static
    testData = [Rexx 1, 2, 3, 4, 5, 5, 4, 3, 2, 1]
    windowSizes = [Rexx 3, 5]
    loop windSize over windowSizes
      ma = RAvgSimpleMoving(windSize)
      loop xVal over testData
        ma.newNum(xVal)
        say 'Next number =' xVal.right(5)', SMA =' ma.getAvg().format(10, 9)
        end xVal
      say
      end windSize

    return

  -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  method main(args = String[]) public static
    run_samples(args)
    return
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[edit]

import deques

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

  var
    summ, n = 0.0
    values: Deque[float]
  for i in 1..period:
    values.addLast(0)

  proc sma(x: float): float =
    values.addLast(x)
    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[edit]

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;
    @window->Add(num);
    if(@window->Size() > @period) {
      @sum -= @window->Remove();
    };
  }
  
  method : GetAvg() ~ Float {
    if(@window->IsEmpty()) {
      return 0; # technically the average is undefined
    };
  
    return @sum / @window->Size();
  }

  function : Main(args : String[]) ~ Nil {
    testData := [1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0];
    windowSizes := [3.0, 5.0];
  
    each(i : windowSizes) {
      windSize := windowSizes[i];
      ma := MovingAverage->New(windSize);
      each(j : testData) {
        x := testData[j];
        ma->NewNum(x);
        avg := ma->GetAvg();
        "Next number = {$x}, SMA = {$avg}"->PrintLine();
      };
      IO.Console->PrintLine();
    };
  }
}
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[edit]

#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
- (void)add:(double)val {
	sum += val;
	[window addObject:@(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) {
				[ma add:testData[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[edit]

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[edit]

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[edit]

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
        average = movingaverage~addnumber(number)
        say "   Next number =" number", moving average =" average
    end
    say
end

::class movingaverage
::method init
  expose period queue sum
  use strict arg period
  sum = 0
  -- the circular queue makes this easy
  queue = .circularqueue~new(period)

-- add a number to the average set
::method addNumber
  expose queue sum
  use strict arg number
  sum += number
  -- add this to the queue
  old = queue~queue(number)
  -- if we pushed an element off the end of the queue,
  -- subtract this from our sum
  if old \= .nil then sum -= old
  -- and return the current item
  return sum / queue~items

-- extra method to retrieve current average
::method average
  expose queue sum
  -- undefined really, but just return 0
  if queue~isempty then return 0
  -- return current queue
  return sum / queue~items
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[edit]

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[edit]

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[edit]

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[edit]

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;

function smaAddValue(var sma:tsma;v: double):double;
Begin
  with sma do
  Begin
    IF smaIsntFull then
    Begin
      inc(smaActLength);
      smaRezActLength := 1/smaActLength;
      smaIsntFull :=  smaActLength < smaLength ;
    end;
    smaSumOld := smaSumOld+v-smaValue[smaPos];
    smaValue[smaPos] := v;
    smaSumNew := smaSumNew+v;

    smaPos := smaPos-1;
    if smaPos < 0 then
    begin
      smaSumOld:= smaSumNew;
      smaSumNew:= 0.0;
      smaPos := smaLength-1;
    end;
    smaAverage := smaSumOld *smaRezActLength;
    smaAddValue:= smaAverage;
  end;
end;

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

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[edit]

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)
    add_sma(sidx,val)
    return get_sma_average(sidx)
end function
 
global procedure free_sma(integer sidx)
    sma[sidx] = sma_free
    sma_free = sidx
end procedure

and the main file is:

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[edit]

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[edit]

(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[edit]

version 1[edit]

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[edit]

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[edit]

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[edit]

#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[edit]

Procedure.d SMA(Number, Period=0)
  Static P
  Static NewList L()
  Protected Sum=0
  If Period<>0
    P=Period
  EndIf
  LastElement(L())
  AddElement(L())
  L()=Number
  While ListSize(L())>P
    FirstElement(L())
    DeleteElement(L(),1)
  Wend
  ForEach L()
    sum+L()
  Next 
  ProcedureReturn sum/ListSize(L())
EndProcedure

Python[edit]

Works with: Python version 3.x

Both implementations use the deque datatype.

Procedural[edit]

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[edit]

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[edit]

  [ $ "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[edit]

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[edit]

#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[edit]

(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[edit]

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 ' ──────── '     "───────────────────"     '───────────────────'
                                           pad='     '
      do m=1  for n;  say center(@.m, 10)  pad left(SMA(p, m), 19)     left(SMA(q, m), 19)
      end   /*m*/
exit                                             /*stick a fork in it,  we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
SMA: procedure expose @.;  parse arg p,j;                          i= 0    ;    $= 0
                 do k=max(1, j-p+1)  to j+p  for p  while k<=j;    i= i + 1;    $= $ + @.k
                 end   /*k*/
     return $/i                                  /*SMA   ≡   simple moving average.     */
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[edit]

version 1[edit]

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[edit]

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[edit]

### 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
        Add( arrayMvgAvg, mvgAvg)   
    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                                  
        Add (arrayMvgAvg, mvgAvg
    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[edit]

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[edit]

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[edit]

Vector Based[edit]

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[edit]

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[edit]

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[edit]

(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[edit]

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[edit]

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 ] ]
    add: value [
        collectedNumber < period
   	ifTrue: [
	   sum := sum + value.
	   valueCollection add: value.
	   collectedNumber := collectedNumber + 1.
	]
	ifFalse: [
	   sum := sum - (valueCollection removeFirst).
	   sum := sum + value.
	   valueCollection add: value
	].
	^ self sma
    ]
].
|sma3 sma5|

sma3 := MovingAverage newWithPeriod: 3.
sma5 := MovingAverage newWithPeriod: 5.

#( 1 2 3 4 5 5 4 3 2 1 ) do: [ :v |
  ('Next number %1, SMA_3 = %2, SMA_5 = %3' % {
         v . (sma3 add: v) asFloat . (sma5 add: v) asFloat
    }) displayNl
]

Swift[edit]

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] {
    print("n: \(n); average \(averager.addNumber(n))")
  }
}
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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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)[edit]

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[edit]

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
            storage.add(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[edit]

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)