使用 PostgreSql 以 5 分钟为间隔插入最后已知价格
Inserting last known price on a 5 minute interval using PostgreSql
我有这个 table 用于逐笔存储阿根廷债券数据：
bonos=# \d preciosrt
Table "public.preciosrt"
Column | Type | Collation | Nullable | Default
------------+------------------------+-----------+----------+-----------------------------------------------
idpreciort | integer | | not null | nextval('preciosrt_idpreciort_seq'::regclass)
fecha | date | | not null | now()::date
ticker | text | | not null |
bid | numeric | | |
ask | numeric | | |
hora | time without time zone | | not null |
bidqty | integer | | |
askqty | integer | | |
last | numeric | | |
volume | integer | | |
settlement | character varying(4) | | |
通过此查询，我可以获得 13:55 和 14:30 之间的“AL29”债券的所有最新价格：
select
row_number() over(), hora, last
from preciosrt
where
ticker='AL29' and
settlement='48hs' and
fecha=now()::date - interval '1 day' and
hora >= '13:55:00' and hora <= '14:30:00'
order by hora;
row_number | hora | last
------------+----------+--------
1 | 13:55:18 | 6260.0
2 | 13:56:00 | 6264.5
3 | 13:56:29 | 6264.5
4 | 13:57:08 | 6265.0
5 | 14:03:07 | 6266.0
6 | 14:05:16 | 6266.0
7 | 14:06:19 | 6266.0
8 | 14:06:33 | 6270.0
9 | 14:07:08 | 6276.5
10 | 14:07:24 | 6299.5
11 | 14:09:56 | 6318.5
12 | 14:11:24 | 6300.0
13 | 14:20:58 | 6315.0
14 | 14:24:58 | 6315.0
15 | 14:27:54 | 6315.0
16 | 14:28:06 | 6319.0
(16 rows)
让我们注意第 12 行和第 13 行，其中一个刻度位于 14:11:24，下一个刻度位于 14:20:58。
现在，通过这个查询，我可以以 5 分钟为间隔显示报价（从 14:00:00 到 15:00:00）：
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1 and x.intervalo >= '14:00:00' and x.intervalo <= '15:00:00';
ticker | last | intervalo
--------+--------+-----------
AL29 | 6266.0 | 14:00:00
AL29 | 6318.5 | 14:05:00
AL29 | 6300.0 | 14:10:00
AL29 | 6315.0 | 14:20:00
AL29 | 6319.0 | 14:25:00
AL29 | 6323.0 | 14:30:00
AL29 | 6320.0 | 14:35:00
AL29 | 6318.0 | 14:40:00
AL29 | 6329.5 | 14:50:00
AL29 | 6315.0 | 14:55:00
AL29 | 6316.0 | 15:00:00
(11 rows)
如您所见，在 14:10 的最后一个价格是 6300.0，下一个价格在 14:20 6315。
AL29 | 6300.0 | 14:10:00
AL29 | 6315.0 | 14:20:00
现在我想在 14:15:00 添加缺失的数据，其中包含最后一个已知报价的值，即 6300。我如何使用 PostgreSql 查询来执行此操作？
编辑
使用 generate_series 我可以创建整个区间，然后左连接到我的数据，但你可以猜到差距仍然存在：
with minutes as (
select generate_series(
timestamp '2000-01-01 11:00:00',
timestamp '2000-01-01 17:00:00',
'5 minute'::interval
) as tt
)
select
to_char(minutes.tt, 'HH24:MI:SS'),
x2.*
from minutes
left join
(
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1
) x2 on x2.intervalo = to_char(minutes.tt, 'HH24:MI:SS');
14:05:00 | AL29 | 6318.5 | 14:05:00
14:10:00 | AL29 | 6300.0 | 14:10:00
14:15:00 | | |
14:20:00 | AL29 | 6315.0 | 14:20:00
14:25:00 | AL29 | 6319.0 | 14:25:00
14:30:00 | AL29 | 6323.0 | 14:30:00
14:35:00 | AL29 | 6320.0 | 14:35:00
14:40:00 | AL29 | 6318.0 | 14:40:00
14:45:00 | | |
14:50:00 | AL29 | 6329.5 | 14:50:00
14:55:00 | AL29 | 6315.0 | 14:55:00
15:00:00 | AL29 | 6316.0 | 15:00:00
15:05:00 | AL29 | 6320.5 | 15:05:00
15:10:00 | AL29 | 6374.5 | 15:10:00
15:15:00 | AL29 | 6344.0 | 15:15:00
15:20:00 | AL29 | 6395.0 | 15:20:00
15:25:00 | AL29 | 6400.0 | 15:25:00
15:30:00 | AL29 | 6355.0 | 15:30:00
15:35:00 | AL29 | 6400.0 | 15:35:00
15:40:00 | AL29 | 6395.0 | 15:40:00
编辑 2
我想我找到了：
with minutes as (
select generate_series(
timestamp '2000-01-01 11:00:00',
timestamp '2000-01-01 17:00:00',
'5 minute'::interval
) as tt
)
select
to_char(minutes.tt, 'HH24:MI:SS'),
coalesce(x2.last, lag(last) over()) as last
from minutes
left join
(
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1
) x2 on x2.intervalo = to_char(minutes.tt, 'HH24:MI:SS');
给出这个结果：
to_char | last
----------+--------
11:00:00 |
11:05:00 |
11:10:00 |
11:15:00 |
11:20:00 |
11:25:00 |
11:30:00 |
11:35:00 | 6225.0
11:40:00 | 6225.0
11:45:00 | 6205.0
11:50:00 | 6233.5
11:55:00 | 6233.0
12:00:00 | 6225.0
12:05:00 | 6222.0
12:10:00 | 6222.0
12:15:00 | 6228.5
12:20:00 | 6220.0
12:25:00 | 6249.0
12:30:00 | 6258.5
12:35:00 | 6257.5
12:40:00 | 6265.0
12:45:00 | 6280.0
12:50:00 | 6296.0
12:55:00 | 6277.0
13:00:00 | 6270.0
13:05:00 | 6270.0
13:10:00 | 6269.0
13:15:00 | 6250.0
13:20:00 | 6260.0
13:25:00 | 6250.0
13:30:00 | 6250.0
13:35:00 | 6250.0
13:40:00 | 6250.0
13:45:00 | 6250.0
13:50:00 | 6260.0
13:55:00 | 6265.0
14:00:00 | 6266.0
14:05:00 | 6318.5
14:10:00 | 6300.0
14:15:00 | 6300.0
14:20:00 | 6315.0
14:25:00 | 6319.0
14:30:00 | 6323.0
14:35:00 | 6320.0
14:40:00 | 6318.0
14:45:00 | 6318.0
14:50:00 | 6329.5
14:55:00 | 6315.0
15:00:00 | 6316.0
15:05:00 | 6320.5
15:10:00 | 6374.5
15:15:00 | 6344.0
15:20:00 | 6395.0
15:25:00 | 6400.0
15:30:00 | 6355.0
15:35:00 | 6400.0
15:40:00 | 6395.0
15:45:00 | 6372.0
15:50:00 | 6405.0
15:55:00 | 6420.0
16:00:00 | 6420.0
16:05:00 | 6410.5
16:10:00 | 6400.0
16:15:00 | 6417.0
16:20:00 | 6395.0
16:25:00 | 6390.0
16:30:00 | 6388.5
16:35:00 | 6390.0
16:40:00 | 6390.0
16:45:00 | 6381.0
16:50:00 | 6380.5
16:55:00 | 6380.5
17:00:00 | 6379.0
编辑 3
改编自 Tomáš Záluský 的回答：
with minutes as (
select generate_series(
timestamp '2000-01-01 11:00:00',
timestamp '2000-01-01 17:00:00',
'5 minute'::interval
) as tt
), t as (
select
to_char(minutes.tt, 'HH24:MI:SS'),
x2.*,
minutes.tt
from minutes
left join
(
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1
) x2 on x2.intervalo = to_char(minutes.tt, 'HH24:MI:SS')
), x as (
select t.*, count(ticker) over(order by tt) as parttn from t
)
select
tt::time,
first_value(ticker) over(partition by parttn order by tt),
first_value(last) over(partition by parttn order by tt)
from x;
您正在解决填充前几行缺失值的问题。
左连接是好的开始。您需要将 ticker 和 last 列的任何 null 值替换为按 minutes.tt 排序的最后一个非空值。这通常可以使用 lag window 函数来实现。来自上一行的 lag 函数 returns 值。虽然它适用于您的具体情况，但我假设通常会有许多连续 5m 间隔的间隙。在这种情况下，lag 只能与 ignore nulls 子句一起使用：case when ticker is null then lag(ticker) ignore nulls over (order by tt) else ticker end。不幸的是，Postgres 目前不支持它。
解决方法：使用 count 分析函数获取按 tt 排序的仅非空值序列（即通过左连接连接的值）。函数 returns 数字随每个非空值增加而随每个 null 值保持不变。这个数字描述了由第一个非空值和可能的许多后续 null 值组成的块。因此，您可以使用此数字来定义分区。您想要的数据是该分区上的 first_value。
with minutes as (
...YOUR SERIES...
), t as (
select
to_char(minutes.tt, 'HH24:MI:SS') as tt,
...YOUR QUERY...
), x as (
select t.*, count(ticker) over (order by tt) as parttn from t
)
select tt
, first_value(ticker) over (partition by parttn order by tt)
, first_value(last) over (partition by parttn order by tt)
from x
Simplified fiddle here. 如果我在你的数据中忽略了一些重要的东西，那么请你自己滚动 fiddle。
我有这个 table 用于逐笔存储阿根廷债券数据：
bonos=# \d preciosrt
Table "public.preciosrt"
Column | Type | Collation | Nullable | Default
------------+------------------------+-----------+----------+-----------------------------------------------
idpreciort | integer | | not null | nextval('preciosrt_idpreciort_seq'::regclass)
fecha | date | | not null | now()::date
ticker | text | | not null |
bid | numeric | | |
ask | numeric | | |
hora | time without time zone | | not null |
bidqty | integer | | |
askqty | integer | | |
last | numeric | | |
volume | integer | | |
settlement | character varying(4) | | |
通过此查询，我可以获得 13:55 和 14:30 之间的“AL29”债券的所有最新价格：
select
row_number() over(), hora, last
from preciosrt
where
ticker='AL29' and
settlement='48hs' and
fecha=now()::date - interval '1 day' and
hora >= '13:55:00' and hora <= '14:30:00'
order by hora;
row_number | hora | last
------------+----------+--------
1 | 13:55:18 | 6260.0
2 | 13:56:00 | 6264.5
3 | 13:56:29 | 6264.5
4 | 13:57:08 | 6265.0
5 | 14:03:07 | 6266.0
6 | 14:05:16 | 6266.0
7 | 14:06:19 | 6266.0
8 | 14:06:33 | 6270.0
9 | 14:07:08 | 6276.5
10 | 14:07:24 | 6299.5
11 | 14:09:56 | 6318.5
12 | 14:11:24 | 6300.0
13 | 14:20:58 | 6315.0
14 | 14:24:58 | 6315.0
15 | 14:27:54 | 6315.0
16 | 14:28:06 | 6319.0
(16 rows)
让我们注意第 12 行和第 13 行，其中一个刻度位于 14:11:24，下一个刻度位于 14:20:58。
现在，通过这个查询，我可以以 5 分钟为间隔显示报价（从 14:00:00 到 15:00:00）：
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1 and x.intervalo >= '14:00:00' and x.intervalo <= '15:00:00';
ticker | last | intervalo
--------+--------+-----------
AL29 | 6266.0 | 14:00:00
AL29 | 6318.5 | 14:05:00
AL29 | 6300.0 | 14:10:00
AL29 | 6315.0 | 14:20:00
AL29 | 6319.0 | 14:25:00
AL29 | 6323.0 | 14:30:00
AL29 | 6320.0 | 14:35:00
AL29 | 6318.0 | 14:40:00
AL29 | 6329.5 | 14:50:00
AL29 | 6315.0 | 14:55:00
AL29 | 6316.0 | 15:00:00
(11 rows)
如您所见，在 14:10 的最后一个价格是 6300.0，下一个价格在 14:20 6315。
AL29 | 6300.0 | 14:10:00
AL29 | 6315.0 | 14:20:00
现在我想在 14:15:00 添加缺失的数据，其中包含最后一个已知报价的值，即 6300。我如何使用 PostgreSql 查询来执行此操作？
编辑
使用 generate_series 我可以创建整个区间，然后左连接到我的数据，但你可以猜到差距仍然存在：
with minutes as (
select generate_series(
timestamp '2000-01-01 11:00:00',
timestamp '2000-01-01 17:00:00',
'5 minute'::interval
) as tt
)
select
to_char(minutes.tt, 'HH24:MI:SS'),
x2.*
from minutes
left join
(
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1
) x2 on x2.intervalo = to_char(minutes.tt, 'HH24:MI:SS');
14:05:00 | AL29 | 6318.5 | 14:05:00
14:10:00 | AL29 | 6300.0 | 14:10:00
14:15:00 | | |
14:20:00 | AL29 | 6315.0 | 14:20:00
14:25:00 | AL29 | 6319.0 | 14:25:00
14:30:00 | AL29 | 6323.0 | 14:30:00
14:35:00 | AL29 | 6320.0 | 14:35:00
14:40:00 | AL29 | 6318.0 | 14:40:00
14:45:00 | | |
14:50:00 | AL29 | 6329.5 | 14:50:00
14:55:00 | AL29 | 6315.0 | 14:55:00
15:00:00 | AL29 | 6316.0 | 15:00:00
15:05:00 | AL29 | 6320.5 | 15:05:00
15:10:00 | AL29 | 6374.5 | 15:10:00
15:15:00 | AL29 | 6344.0 | 15:15:00
15:20:00 | AL29 | 6395.0 | 15:20:00
15:25:00 | AL29 | 6400.0 | 15:25:00
15:30:00 | AL29 | 6355.0 | 15:30:00
15:35:00 | AL29 | 6400.0 | 15:35:00
15:40:00 | AL29 | 6395.0 | 15:40:00
编辑 2
我想我找到了：
with minutes as (
select generate_series(
timestamp '2000-01-01 11:00:00',
timestamp '2000-01-01 17:00:00',
'5 minute'::interval
) as tt
)
select
to_char(minutes.tt, 'HH24:MI:SS'),
coalesce(x2.last, lag(last) over()) as last
from minutes
left join
(
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1
) x2 on x2.intervalo = to_char(minutes.tt, 'HH24:MI:SS');
给出这个结果：
to_char | last
----------+--------
11:00:00 |
11:05:00 |
11:10:00 |
11:15:00 |
11:20:00 |
11:25:00 |
11:30:00 |
11:35:00 | 6225.0
11:40:00 | 6225.0
11:45:00 | 6205.0
11:50:00 | 6233.5
11:55:00 | 6233.0
12:00:00 | 6225.0
12:05:00 | 6222.0
12:10:00 | 6222.0
12:15:00 | 6228.5
12:20:00 | 6220.0
12:25:00 | 6249.0
12:30:00 | 6258.5
12:35:00 | 6257.5
12:40:00 | 6265.0
12:45:00 | 6280.0
12:50:00 | 6296.0
12:55:00 | 6277.0
13:00:00 | 6270.0
13:05:00 | 6270.0
13:10:00 | 6269.0
13:15:00 | 6250.0
13:20:00 | 6260.0
13:25:00 | 6250.0
13:30:00 | 6250.0
13:35:00 | 6250.0
13:40:00 | 6250.0
13:45:00 | 6250.0
13:50:00 | 6260.0
13:55:00 | 6265.0
14:00:00 | 6266.0
14:05:00 | 6318.5
14:10:00 | 6300.0
14:15:00 | 6300.0
14:20:00 | 6315.0
14:25:00 | 6319.0
14:30:00 | 6323.0
14:35:00 | 6320.0
14:40:00 | 6318.0
14:45:00 | 6318.0
14:50:00 | 6329.5
14:55:00 | 6315.0
15:00:00 | 6316.0
15:05:00 | 6320.5
15:10:00 | 6374.5
15:15:00 | 6344.0
15:20:00 | 6395.0
15:25:00 | 6400.0
15:30:00 | 6355.0
15:35:00 | 6400.0
15:40:00 | 6395.0
15:45:00 | 6372.0
15:50:00 | 6405.0
15:55:00 | 6420.0
16:00:00 | 6420.0
16:05:00 | 6410.5
16:10:00 | 6400.0
16:15:00 | 6417.0
16:20:00 | 6395.0
16:25:00 | 6390.0
16:30:00 | 6388.5
16:35:00 | 6390.0
16:40:00 | 6390.0
16:45:00 | 6381.0
16:50:00 | 6380.5
16:55:00 | 6380.5
17:00:00 | 6379.0
编辑 3
改编自 Tomáš Záluský 的回答：
with minutes as (
select generate_series(
timestamp '2000-01-01 11:00:00',
timestamp '2000-01-01 17:00:00',
'5 minute'::interval
) as tt
), t as (
select
to_char(minutes.tt, 'HH24:MI:SS'),
x2.*,
minutes.tt
from minutes
left join
(
select
ticker,
last,
intervalo
from(
select
ticker, last,
row_number() over (
partition by to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC'
order by hora desc
) as rownum,
to_char(to_timestamp(floor((extract('epoch' from hora) / 300 )) * 300) at time zone 'UTC', 'HH24:MI:SS') as intervalo
from preciosrt p1
where
p1.ticker='AL29' and p1.settlement='48hs' and
p1.fecha=now()::date - interval '1 day'
) x
where x.rownum=1
) x2 on x2.intervalo = to_char(minutes.tt, 'HH24:MI:SS')
), x as (
select t.*, count(ticker) over(order by tt) as parttn from t
)
select
tt::time,
first_value(ticker) over(partition by parttn order by tt),
first_value(last) over(partition by parttn order by tt)
from x;
您正在解决填充前几行缺失值的问题。
左连接是好的开始。您需要将 ticker 和 last 列的任何 null 值替换为按 minutes.tt 排序的最后一个非空值。这通常可以使用 lag window 函数来实现。来自上一行的 lag 函数 returns 值。虽然它适用于您的具体情况，但我假设通常会有许多连续 5m 间隔的间隙。在这种情况下，lag 只能与 ignore nulls 子句一起使用：case when ticker is null then lag(ticker) ignore nulls over (order by tt) else ticker end。不幸的是，Postgres 目前不支持它。
解决方法：使用 count 分析函数获取按 tt 排序的仅非空值序列（即通过左连接连接的值）。函数 returns 数字随每个非空值增加而随每个 null 值保持不变。这个数字描述了由第一个非空值和可能的许多后续 null 值组成的块。因此，您可以使用此数字来定义分区。您想要的数据是该分区上的 first_value。
with minutes as (
...YOUR SERIES...
), t as (
select
to_char(minutes.tt, 'HH24:MI:SS') as tt,
...YOUR QUERY...
), x as (
select t.*, count(ticker) over (order by tt) as parttn from t
)
select tt
, first_value(ticker) over (partition by parttn order by tt)
, first_value(last) over (partition by parttn order by tt)
from x
Simplified fiddle here. 如果我在你的数据中忽略了一些重要的东西，那么请你自己滚动 fiddle。