How to do an average on time series data?

Question

I need to generate average hold times for various stock of companies as follows:
The data looks like:
stock        timestamp (sec)            quantity
GOOG          12459.6                    -100        <-- SALE
GOOG          12634.0                    +100        <-- PURCHASE
GOOG          12636.2                    +200
GOOG          12464.8                    -100
GOOG          12568.3                    -300
GOOG          12678.0                    +200
The rules are
1. begin and end day with balance 0
2. can short sell, i.e. can sell shares even if balance is currently 0
3. hold time is defined as number of seconds stock was held before it was sold
4. first stock purchased are sold first
I need to generate the average hold times seconds per share.  I'd prefer to do this using SQL and NOT a procedure.
Any tips on how to go about calculating this?  I have looked at various analytic functions, but still not sure.
Thank you.

Answer 1

I'm afraid you might be after something like below:
this is a simplified scenario where the quantity balance always reaches 0 before changing sign (not very probable in real life)
Simple examples are reserved for the lecturer was a pretty common phrase in my university times
I dont know how to deal with a general case yet
select * from trade_0 order by position,time
TIME
POSITION
DIRECTION
QUANTITY
8
GOOG
S
100
13
GOOG
B
20
16
GOOG
B
30
17
GOOG
B
30
19
GOOG
B
20
22
GOOG
B
20
25
GOOG
B
30
26
GOOG
B
20
30
GOOG
B
30
34
GOOG
B
20
38
GOOG
B
30
41
GOOG
S
150
7
YHOO
S
10
12
YHOO
S
20
15
YHOO
S
30
16
YHOO
S
40
18
YHOO
S
60
21
YHOO
S
30
24
YHOO
S
10
25
YHOO
B
100
29
YHOO
B
300
33
YHOO
S
100
37
YHOO
S
80
40
YHOO
S
20
your condition 4. first stock purchased are sold first requires a procedural solution so model clause must be used if you want to do it in SQL.
Model Men, bear with me, please !
select m.*,
       avg(abs(x_time - decode(kind,'B',time_b,time_s))) over (partition by position
                                                                   order by rn rows between unbounded preceding
                                                                                        and unbounded following
          ) average
  from (select *
          from (select nvl(b.position,s.position) position,
                       nvl(b.rn,s.rn) rn,
                       nvl(b.cnt,0) cnt_b,
                       nvl(s.cnt,0) cnt_s,
                       b.time time_b,
                       s.time time_s,
                       b.quantity qty_b,
                       s.quantity qty_s
                  from (select time,position,quantity,
                               row_number() over (partition by position order by time) rn,
                               count(*) over (partition by position) cnt
                          from trade_0
                         where direction = 'B'
                       ) b
                       full outer join
                       (select time,position,quantity,
                               row_number() over (partition by position order by time) rn,
                               count(*) over (partition by position) cnt
                          from trade_0
                         where direction = 'S'
                       ) s
                    on b.position = s.position
                   and b.rn = s.rn
         model
         partition by (position)
         dimension by (rn)
         measures (0 loc,
                   case when cnt_b >= cnt_s then 'B' else 'S' end kind,
                   time_b,
                   time_s,
                   qty_b,
                   qty_s,
                   0 qty_left,
                   0 x_time
         rules iterate (1000000) until (loc[iteration_number] is null)
          loc[0] = nvl2(loc[0],loc[0],1),
          qty_left[loc[0]] = case when iteration_number > 0
                                  then qty_left[loc[0]] + case when kind[iteration_number] = 'B'
                                                               then qty_b[iteration_number]
                                                               else qty_s[iteration_number]
                                                          end
                                  else 0
                             end,
          x_time[iteration_number] = case when kind[iteration_number] = 'B'
                                          then time_s[loc[0]]
                                          else time_b[loc[0]]
                                     end,
          loc[0] = loc[0] + case when qty_left[loc[0]] = case when kind[iteration_number] = 'B'
                                                              then qty_s[loc[0]]
                                                              else qty_b[loc[0]]
                                                         end
                                 then 1
                                 else 0
                            end
       ) m
where kind is not null
order by position,rn
POSITION
RN
LOC
KIND
TIME_B
TIME_S
QTY_B
QTY_S
QTY_LEFT
X_TIME
AVERAGE
GOOG
1
0
B
13
8
20
100
100
8
10.4
GOOG
2
0
B
16
41
30
150
150
8
10.4
GOOG
3
0
B
17
30
8
10.4
GOOG
4
0
B
19
20
0
8
10.4
GOOG
5
0
B
22
20
0
41
10.4
GOOG
6
0
B
25
30
0
41
10.4
GOOG
7
0