Compare Rows in oracle table and update matching ones

Question

I have a table such as the following:

**ID      tDate        Product    Price    Quantity    BuySell    Status**
  1     10-May-17       pppp       $12        20         Buy       Null
  2     12-May-17       tttt       $10        20         Sell      Null
  3     12-May-17       tttt       $10        20         Buy       Null
  4     18-May-17       pppp       $14        20         Sell      Null
  5     18-May-17       pppp       $14        20         Buy       Null
  6     18-May-17       pppp       $14        20         Sell      Null

I need to update the field named STATUS, and set it to 'Matched', wherever a pair is found with equal tDate, product, price, and quantity, and NOT equal BuySell.

Following is the desired result:

**ID      tDate        Product    Price    Quantity    BuySell    Status**
  1     10-May-17       pppp       $12        20         Buy       Null
  2     12-May-17       tttt       $10        20         Sell      Matched
  3     12-May-17       tttt       $10        20         Buy       Matched
  4     18-May-17       pppp       $14        20         Sell      Matched
  5     18-May-17       pppp       $14        20         Buy       Matched
  6     18-May-17       pppp       $14        20         Sell      Null

Notice How #6 did not match, because it can only match with another null.

I am hoping i can perform this with a single SQL statement.

What i am doing right now is probably the worst approach: I load into a pandas dataframe in python, and then i loop through each row comparing them.

s = "SELECT ID, Account, product, Price, tDate, BuySell, Qty" + \
    "FROM Table " + \
    "WHERE Status IS NULL " + \
    "ORDER BY Account, product, tDate, Price, Qty"

df = pd.read_sql(s, conn)

for i in range(len(df.index)-1):

    if df.iloc[i, 1] == df.iloc[i+1, 1]  \
        and df.iloc[i, 2] == df.iloc[i+1, 2] \
        and df.iloc[i, 3] == df.iloc[i+1, 3] \
        and df.iloc[i, 4] == df.iloc[i+1, 4] \
        and df.iloc[i, 5] != df.iloc[i+1, 5] \
        and df.iloc[i, 6] == df.iloc[i+1, 6]:

        s = "UPDATE Temp_Fees " + \
            "SET Strategy = 'UNALLOCATED \ CANCELLED' " + \
            "WHERE ID = " + str(df.iloc[i,0]) + \
            " OR ID = " + str(df.iloc[i + 1, 0])

        #custom function that will execute and commit statement
        bb.EXECUTE(s)

        #avoid reading a matched row 
        i = i + 1

Thank you

Answer 1

Untested but something like this using only SQL:

MERGE INTO your_table dst
USING (
  SELECT ROW_NUMBER() OVER (
             PARTITION BY tDate, Product, Price, Quantity, BuySell
             ORDER BY ID
           ) AS idx,
         COUNT( CASE BuySell WHEN 'Buy' THEN 1 END ) OVER (
             PARTITION BY tDate, Product, Price, Quantity
           ) AS num_buy,
         COUNT( CASE BuySell WHEN 'Sell' THEN 1 END ) OVER (
             PARTITION BY tDate, Product, Price, Quantity
           ) AS num_sell
  FROM   your_table
) src
ON ( src.ROWID = dst.ROWID AND src.idx <= LEAST( src.num_buy, src.num_sell ) )
WHEN MATCHED THEN
  UPDATE SET Status = 'Matched';

Answer 2

You can get the number of buy-sell pairs per tdate and update such rows.

MERGE INTO tablename dst
USING (select t.*,count(*) over(partition by tDate,Product,Price,Quantity,rn) as cnt 
       from (select t.*,row_number() over(partition by tDate,Product,Price,Quantity,buysell order by id) as rn
             from tablename t) t
       ) src
ON (src.id = dst.id AND src.cnt=2)
WHEN MATCHED THEN
UPDATE SET Status = 'Matched';

Run this query to see how row numbers are assigned to buy-sell.

select t.*,count(*) over(partition by tDate,Product,Price,Quantity,rn) as cnt 
from (select t.*,row_number() over(partition by tDate,Product,Price,Quantity,buysell order by id) as rn
      from tablename t) t

Answer 3

here's another perspective to add to the others. This addresses only the matching portion and not the update or merge part. I had a similar problem recently where I needed to find records that matched on transaction date and location, but came from two different sources. In this case the records have to already be sorted so that the like records will be together. The inner query compares the record to the one before and the one after and grabs it if they match. Then the outer query determines if they meet the 'difference' criteria. Hope this helps.

select sbs.trnsid, sbs.amount, sbs.transaction_date, sbs.posted_date, sbs.srcid, 
        sbs.credited_flag, sbs.accid, sbs.compid, sbs.badgeid, sbs.locid, sbs.date_credited, 
        sbs.searchable, sbs.priortime, sbs.nexttime, sbs.priorsource, sbs.nextsource 
    from 
    (select trnsid, amount, transaction_date, posted_date, srcid, credited_flag,
      accid, compid, badgeid, locid, date_credited, transaction_date||locid as searchable,
      lag(transaction_date||locid, 1) over (order by accid) as priortime,
      lead(transaction_date||locid, 1) over (order by accid) as nexttime, 
    lag(srcid, 1) over (order by accid) as priorsource, 
    lead(srcid, 1) over (order by accid) as nextsource
    from transactions_table
    where accid = v_acct
      and transaction_date >= to_date('10/01/2016 00:00:00', 'mm/dd/yyyy hh24:mi:ss') 
      and transaction_date <= to_date('04/23/2017 23:59:59', 'mm/dd/yyyy hh24:mi:ss')
      and srcid in ('B', 'S') order by accid, transaction_date, locid) sbs
    where (sbs.searchable = sbs.nexttime and sbs.srcid = 'S' and sbs.nextsource = 'B')
   or (sbs.searchable = sbs.priortime and sbs.srcid = 'B' and sbs.priorsource = 'S');

Answer 4

merge into mytable t3
using (select t1.*, count(*) over (partition by tdate,product,price,quantity,field) as field2 from
(
select mytable.*, row_number() over (partition by mytable.tdate,mytable.product,mytable.price,mytable.quantity,mytable.buysell 
order by id) field from 
mytable) t1)  t2
on (t2.id=t3.id and t2.field2='2')
when matched then 
update set status='Matched';