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XLeratorDB/financial Documentation

SQL Server FIFO ending balances


FIFOend
Updated: 17 Apr 2017

Use the SQL Server table-valued function FIFOend to return the items which make up the ending inventory on a FIFO (First In, First Out) basis. FIFOend calculates the balances for each value from the first value to the last value in the ordered group or partition.

FIFOend supports both long and short inventory positions. In other words, if the quantity on hand falls below the zero, FIFOend calculates from the last sale or withdrawal transaction, rather than from the last purchases or additions to inventory.

FIFOend assumes that the quantity (i.e. the number of units) of the transaction and the monetary value of the transaction have the same sign. FIFOend adds NULL values to the inventory at the last price.

FIFOend supports the processing of multiple products in a single SQL statement. FIFOend resets all calculated values when the row number value is 1.

Syntax
SELECT * FROM [wct].[FIFOend] (
   <@DataQuery, nvarchar(max),>)
Arguments
Input NameDescription
@DataQueryAn SQL statement which returns a resultant table containing the unique transaction identifier, ROW_NUMBER(), quantity and amount.
Return Type
RETURNS  TABLE (
    [UID] [sql_variant] NULL,
    [RowNumber] [int] NULL,
    [qty] [float] NULL,
    [amt] [float] NULL
)
ColumnDescription
UIDunique transaction identifier
RowNumberthe row number passed into the function
qtyinventory number of items
amtinventory value
Remarks
  • If the 3rd column (quantity) of the resultant table from @DataQuery contains NULL an error will be generated.
  • For multi-product sets, this function relies on the SQL Server ROW_NUMBER() function and expects results to be returned in ascending ROW_NUMBER() order within a PARTITION.
  • When ROW_NUMBER()= 1 all inventory values are re-initialized.
  • Available in XLeratorDB / financial 2008 only
Examples
Example #1

In the following examples, we calculate FIFO inventory values for stock trades in the ABC, XYZ, and GHI companies. We will create a temporary table, #c and populate it with some data. We can be either short or long the shares at any point in time.

--Create the temporary table
CREATE TABLE #c   (
      trn               int,
      sym               char(3),
      tDate             date,
      qty               money,
      price_unit        money,
      price_extended    money,
      PRIMARY KEY (trn)
      )
 
--Populate the table with some data
INSERT INTO #c VALUES (01131019,'XYZ','2013-10-19',424,25.13,10655.12)
INSERT INTO #c VALUES (02130617,'ABC','2013-06-17',313,12.93,4047.09)
INSERT INTO #c VALUES (03130308,'ABC','2013-03-08',-157,13.17,-2067.69)
INSERT INTO #c VALUES (04130516,'GHI','2013-05-16',160,34.48,5516.8)
INSERT INTO #c VALUES (05130706,'XYZ','2013-07-06',-170,23.46,-3988.2)
INSERT INTO #c VALUES (06130924,'GHI','2013-09-24',328,34.95,11463.6)
INSERT INTO #c VALUES (07130722,'ABC','2013-07-22',599,13.65,8176.35)
INSERT INTO #c VALUES (08131231,'ABC','2013-12-31',-145,13.19,-1912.55)
INSERT INTO #c VALUES (09131025,'XYZ','2013-10-25',-153,24.31,-3719.43)
INSERT INTO #c VALUES (10130908,'ABC','2013-09-08',-386,13.65,-5268.9)
INSERT INTO #c VALUES (11130906,'XYZ','2013-09-06',-13,23.97,-311.61)
INSERT INTO #c VALUES (12130621,'ABC','2013-06-21',-326,12.73,-4149.98)
INSERT INTO #c VALUES (13131221,'GHI','2013-12-21',72,34.38,2475.36)
INSERT INTO #c VALUES (14130705,'XYZ','2013-07-05',-277,25.01,-6927.77)
INSERT INTO #c VALUES (15130307,'GHI','2013-03-07',559,35.21,19682.39)
INSERT INTO #c VALUES (16131107,'ABC','2013-11-07',27,12.68,342.36)
INSERT INTO #c VALUES (17130924,'GHI','2013-09-24',-291,35.69,-10385.79)
INSERT INTO #c VALUES (18140125,'GHI','2014-01-25',-78,35.46,-2765.88)
INSERT INTO #c VALUES (19130516,'XYZ','2013-05-16',315,23.57,7424.55)
INSERT INTO #c VALUES (20130518,'ABC','2013-05-18',298,13.23,3942.54)
INSERT INTO #c VALUES (21131103,'XYZ','2013-11-03',-326,23.24,-7576.24)
INSERT INTO #c VALUES (22131012,'XYZ','2013-10-12',596,23.16,13803.36)
INSERT INTO #c VALUES (23130619,'XYZ','2013-06-19',296,23.46,6944.16)
INSERT INTO #c VALUES (24130418,'XYZ','2013-04-18',275,24.83,6828.25)
INSERT INTO #c VALUES (25130408,'ABC','2013-04-08',298,12.98,3868.04)
INSERT INTO #c VALUES (26130320,'ABC','2013-03-20',-92,13.64,-1254.88)
INSERT INTO #c VALUES (27130906,'XYZ','2013-09-06',-147,23.81,-3500.07)
INSERT INTO #c VALUES (28131209,'XYZ','2013-12-09',315,24.46,7704.9)
INSERT INTO #c VALUES (29130602,'XYZ','2013-06-02',114,24.29,2769.06)
INSERT INTO #c VALUES (30130519,'XYZ','2013-05-19',467,23.15,10811.05)
INSERT INTO #c VALUES (31140205,'XYZ','2014-02-05',42,24.39,1024.38)
INSERT INTO #c VALUES (32130310,'ABC','2013-03-10',-63,12.61,-794.43)
INSERT INTO #c VALUES (33140102,'XYZ','2014-01-02',-196,22.98,-4504.08)
INSERT INTO #c VALUES (34130507,'XYZ','2013-05-07',55,23.43,1288.65)
INSERT INTO #c VALUES (35130321,'XYZ','2013-03-21',275,24.83,6828.25)
INSERT INTO #c VALUES (36130917,'XYZ','2013-09-17',92,24.6,2263.2)
INSERT INTO #c VALUES (37130220,'XYZ','2013-02-20',528,23.54,12429.12)
INSERT INTO #c VALUES (38130311,'XYZ','2013-03-11',-193,24.9,-4805.7)
INSERT INTO #c VALUES (39130908,'ABC','2013-09-08',490,12.69,6218.1)
INSERT INTO #c VALUES (40131013,'XYZ','2013-10-13',359,23.91,8583.69)
INSERT INTO #c VALUES (41130310,'ABC','2013-03-10',463,13.38,6194.94)
INSERT INTO #c VALUES (42131011,'XYZ','2013-10-11',-250,23.12,-5780)
INSERT INTO #c VALUES (43130521,'GHI','2013-05-21',-174,34.2,-5950.8)
INSERT INTO #c VALUES (44130227,'XYZ','2013-02-27',357,22.86,8161.02)
INSERT INTO #c VALUES (45131030,'XYZ','2013-10-30',-350,23.36,-8176)
INSERT INTO #c VALUES (46130301,'ABC','2013-03-01',157,13.01,2042.57)
INSERT INTO #c VALUES (47130619,'XYZ','2013-06-19',413,25.18,10399.34)
INSERT INTO #c VALUES (48130430,'ABC','2013-04-30',229,13.32,3050.28)
INSERT INTO #c VALUES (49130508,'ABC','2013-05-08',238,12.79,3044.02)
INSERT INTO #c VALUES (50131103,'GHI','2013-11-03',-246,35.61,-8760.06)
INSERT INTO #c VALUES (51131206,'GHI','2013-12-06',85,33.64,2859.4)
INSERT INTO #c VALUES (52131014,'GHI','2013-10-14',-91,33.12,-3013.92)
INSERT INTO #c VALUES (53140102,'GHI','2014-01-02',396,35.52,14065.92)
INSERT INTO #c VALUES (54130831,'XYZ','2013-08-31',-61,23.23,-1417.03)
INSERT INTO #c VALUES (55130630,'XYZ','2013-06-30',-272,23.45,-6378.4)
INSERT INTO #c VALUES (56130419,'GHI','2013-04-19',416,34.46,14335.36)
INSERT INTO #c VALUES (57130813,'XYZ','2013-08-13',-163,23.65,-3854.95)
INSERT INTO #c VALUES (58130722,'XYZ','2013-07-22',-88,24.64,-2168.32)
INSERT INTO #c VALUES (59130320,'XYZ','2013-03-20',-20,23.59,-471.8)
INSERT INTO #c VALUES (60130419,'XYZ','2013-04-19',277,22.83,6323.91)
INSERT INTO #c VALUES (61130916,'ABC','2013-09-16',-202,12.94,-2613.88)
INSERT INTO #c VALUES (62131027,'XYZ','2013-10-27',-248,24.71,-6128.08)
INSERT INTO #c VALUES (63130806,'GHI','2013-08-06',445,33.5,14907.5)
INSERT INTO #c VALUES (64140109,'XYZ','2014-01-09',-253,24.46,-6188.38)
INSERT INTO #c VALUES (65131227,'GHI','2013-12-27',376,33.49,12592.24)
INSERT INTO #c VALUES (66140203,'XYZ','2014-02-03',459,23.7,10878.3)
INSERT INTO #c VALUES (67130302,'XYZ','2013-03-02',9,24.04,216.36)
INSERT INTO #c VALUES (68130223,'ABC','2013-02-23',238,12.78,3041.64)
INSERT INTO #c VALUES (69130403,'GHI','2013-04-03',-151,33.16,-5007.16)
INSERT INTO #c VALUES (70130702,'GHI','2013-07-02',-162,35.48,-5747.76)
INSERT INTO #c VALUES (71130731,'ABC','2013-07-31',79,13.55,1070.45)
INSERT INTO #c VALUES (72140204,'XYZ','2014-02-04',-208,24.36,-5066.88)
INSERT INTO #c VALUES (73131028,'GHI','2013-10-28',-46,34.65,-1593.9)
INSERT INTO #c VALUES (74130619,'XYZ','2013-06-19',202,22.95,4635.9)
INSERT INTO #c VALUES (75131216,'GHI','2013-12-16',500,33.55,16775)
INSERT INTO #c VALUES (76131009,'ABC','2013-10-09',249,13.47,3354.03)
INSERT INTO #c VALUES (77130825,'GHI','2013-08-25',272,33.86,9209.92)
INSERT INTO #c VALUES (78140112,'XYZ','2014-01-12',332,24.28,8060.96)
INSERT INTO #c VALUES (79131223,'XYZ','2013-12-23',-376,25.12,-9445.12)
INSERT INTO #c VALUES (80140126,'XYZ','2014-01-26',404,24.23,9788.92)
INSERT INTO #c VALUES (81131123,'GHI','2013-11-23',187,33.86,6331.82)
INSERT INTO #c VALUES (82140131,'XYZ','2014-01-31',548,23.65,12960.2)
INSERT INTO #c VALUES (83130428,'XYZ','2013-04-28',142,23.13,3284.46)
INSERT INTO #c VALUES (84140104,'XYZ','2014-01-04',261,24.46,6384.06)
INSERT INTO #c VALUES (85131002,'GHI','2013-10-02',-295,32.51,-9590.45)
INSERT INTO #c VALUES (86131114,'ABC','2013-11-14',-386,12.4,-4786.4)
INSERT INTO #c VALUES (87131116,'GHI','2013-11-16',-320,34.16,-10931.2)
INSERT INTO #c VALUES (88131009,'ABC','2013-10-09',187,13.36,2498.32)
INSERT INTO #c VALUES (89130611,'ABC','2013-06-11',130,13.02,1692.6)
INSERT INTO #c VALUES (90130430,'ABC','2013-04-30',-191,13.49,-2576.59)
INSERT INTO #c VALUES (91130615,'XYZ','2013-06-15',545,23.88,13014.6)
INSERT INTO #c VALUES (92130924,'XYZ','2013-09-24',-248,24.58,-6095.84)
INSERT INTO #c VALUES (93130622,'ABC','2013-06-22',-227,13.47,-3057.69)
INSERT INTO #c VALUES (94131117,'GHI','2013-11-17',-92,32.65,-3003.8)
INSERT INTO #c VALUES (95130908,'GHI','2013-09-08',103,35.42,3648.26)
INSERT INTO #c VALUES (96130716,'GHI','2013-07-16',-347,35.29,-12245.63)
INSERT INTO #c VALUES (97131125,'XYZ','2013-11-25',-278,23.18,-6444.04)
INSERT INTO #c VALUES (98130413,'XYZ','2013-04-13',243,24.25,5892.75)
INSERT INTO #c VALUES (99130515,'XYZ','2013-05-15',97,25.13,2437.61)
 
--Calculate the FIFO Values and store in temp table
SELECT
     cast(UID as int) as ID
    ,RowNumber
    ,qty
    ,amt
INTO
    #fifo
FROM
    wct.FIFOend('SELECT trn, ROW_NUMBER() OVER (PARTITION by sym ORDER BY sym, tDate, trn),qty,price_extended FROM #c ORDER BY sym, tDate, trn')
 
--JOIN to the source data to produce the inventory output
SELECT
     c.trn
    ,c.sym
    ,c.tDate
    ,c.qty as [Invoice Quantity]
    ,c.price_unit
    ,c.price_extended as [Invoice Amount]
    ,f.[qty] as [Inventory Quantity]
    ,f.[amt] as [Inventory Cost]
FROM
    #c
INNER JOIN
    #FIFO f
ON
    c.trn = f.[ID]
ORDER BY
    c.sym, c.tDate, c.trn

This produces the following result.

Example #2

Using the same data as Example #1, we generate the ending inventory values without using the ROW_NUMBER() feature.

--Calculate the FIFO Values and store in temp table
SELECT
     n.sym
    ,cast(k.UID as int) as ID
    ,k.RowNumber
    ,k.qty
    ,k.amt
INTO
    #fifo
FROM
    (SELECT DISTINCT sym FROM #c)n
CROSS APPLY
    wct.FIFOend('SELECT trn,NULL,qty,price_extended FROM #c where sym = ''' + n.sym + ''' ORDER BY tDate, trn')k
 
--JOIN to the source data to produce the inventory output
SELECT
     c.trn
    ,c.sym
    ,c.tDate
    ,c.qty as [Invoice Quantity]
    ,c.price_unit
    ,c.price_extended as [Invoice Amount]
    ,f.[qty] as [Inventory Quantity]
    ,f.[amt] as [Inventory Cost]
FROM
    #c
INNER JOIN
    #FIFO f
ON
    c.trn = f.[ID]
    AND c.sym = f.sym
ORDER BY
    c.sym, c.tDate, c.trn

This produces the following result.

Example #3

In this example, we will look at a simple FX blotter. In FX trading, it is not at all unusual to switch from a long currency position to a short currency position during the course of a day. Additionally, if you are trading something like the US dollars against the Euro, if you are long US dollars, you are short the Euro. Thus, it’s critical to pay attention to the sign. We will create the #fx table and populate it with some data.

--Create the temporary table
CREATE TABLE #fx (
      trn        int,
      ccy        char(3),
      amt_ccy    money,
      rate        float,
      ctr        char(3),
      amt_ctr    money,
      PRIMARY KEY (trn)
      )
 
--Populate the table with some data
INSERT INTO #fx VALUES (101,'GBP',8000000,1.619,'USD',-12952000)
INSERT INTO #fx VALUES (102,'GBP',-10000000,1.62,'USD',16200000)
INSERT INTO #fx VALUES (103,'GBP',-4000000,1.613,'USD',6452000)
INSERT INTO #fx VALUES (104,'GBP',7000000,1.618,'USD',-11326000)
INSERT INTO #fx VALUES (105,'GBP',6000000,1.623,'USD',-9738000)
INSERT INTO #fx VALUES (106,'GBP',-5000000,1.618,'USD',8090000)
INSERT INTO #fx VALUES (107,'GBP',-10000000,1.602,'USD',16020000)
INSERT INTO #fx VALUES (108,'GBP',2000000,1.608,'USD',-3216000)
INSERT INTO #fx VALUES (109,'GBP',-2000000,1.602,'USD',3204000)
INSERT INTO #fx VALUES (110,'GBP',10000000,1.626,'USD',-16260000)
 
--Calculate the FIFO Values
SELECT
     cast(k.UID as int) as trn
    ,fx.amt_ccy as [GBP trade amount]
    ,fx.amt_ctr as [USD trade amount]
    ,k.qty as [GBP Position]
    ,-k.amt as [USD Position]
FROM
    wct.FIFOend('SELECT trn,NULL,amt_ccy,-amt_ctr FROM #fx ORDER BY trn')k
INNER JOIN
    #fx
ON
    cast(k.UID as int) = fx.trn

This produces the following result.

Example #4

In this example we require multiple columns to uniquely identify the inventory items and there are no unique transaction identifiers.

--Put some data into #p
SELECT
    *
INTO
    #p
FROM (VALUES
     ('ZYX','Coffee Mug','Ceramic','8 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','8 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Plastic','8 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Ceramic','8 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','8 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-16',-5,2.95,-14.75)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-23',-2,2.95,-5.9)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-24',-1,2.95,-2.95)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-01',-3,2.95,-8.85)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-08',-7,2.95,-20.65)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-15',-6,2.95,-17.7)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-22',-2,2.95,-5.9)
    )n([Manufacturer],[Description],[Material],[Size],[Color],[Date],[qty],[unit price],[extended price])
 
--Copy the #p data into #p2 and put it in order for FIFO processing
SELECT
     IDENTITY(int,1,1) as ID
    ,ROW_NUMBER() OVER (PARTITION BY [Manufacturer],[Description],[Material],[Size],[Color] ORDER BY [Manufacturer],[Description],[Material],[Size],[Color],[Date],qty DESC) as rn
    ,#p.*
INTO
    #p2
FROM
    #p
ORDER BY
     [Manufacturer]
    ,[Description]
    ,[Material]
    ,[Size]
    ,[Color]
    ,[Date]
    ,qty DESC
 
--Put the tvf results into the #fifo table
SELECT
     CAST([UID] as Int) as ID
    ,RowNumber
    ,qty
    ,amt
INTO
    #fifo
FROM
    wct.FIFOend('SELECT ID, rn,qty,[extended price] FROM #p2 ORDER BY ID')
 
--JOIN to the source data to produce the inventory output
SELECT
     #p2.[Manufacturer]
    ,#p2.[Description]
    ,#p2.[Material]
    ,#p2.[Size]
    ,#p2.[Color]
    ,#p2.[Date]
    ,#p2.[qty] as [Invoice Quantity]
    ,#p2.[unit price]
    ,#p2.[extended price] as [Invoice Amount]
    ,f.[qty] as [Inventory-On-Hand]
    ,f.[amt] as [Inventory Cost]
FROM
    #p2
INNER JOIN
    #FIFO f
ON
    #p2.ID = f.[ID]
ORDER BY
    #p2.id

This produces the following result.

 

See Also

 



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