[英]Use T-SQL window functions to retrieve 5-minute averages from 1-minute data
I have a database table containing one-minute periods of Open, Close, High, Low, Volume values for a security.我有一个数据库表,其中包含一分钟的开盘价、收盘价、最高价、最低价、交易量值。 I'm using SQL Server 2017, but 2019 RC is an option.
我正在使用 SQL Server 2017,但可以选择 2019 RC。
I am trying to find an efficient SQL Server query that can aggregate these into 5-minute windows, where:我正在尝试找到一个高效的 SQL 服务器查询,可以将这些汇总到 5 分钟的 windows 中,其中:
Ideally this query would account for gaps in the data, ie be based on date calculations rather than counting preceding / following rows.理想情况下,此查询将考虑数据中的空白,即基于日期计算,而不是计算前面/后面的行。
For example say I have (here's 6 mins of data):例如说我有(这里是 6 分钟的数据):
| Time | Open | Close | High | Low | Volume | |------------------|------|-------|------|-----|--------| | 2019-10-30 09:30 | 5 | 10 | 15 | 1 | 125000 | | 2019-10-30 09:31 | 10 | 15 | 20 | 5 | 100000 | | 2019-10-30 09:32 | 15 | 20 | 25 | 10 | 120000 | | 2019-10-30 09:33 | 20 | 25 | 30 | 15 | 10000 | | 2019-10-30 09:34 | 20 | 22 | 40 | 2 | 13122 | | 2019-10-30 09:35 | 22 | 30 | 35 | 4 | 15000 | Not factored in, since this would be the first row of the next 5-minute window
I am trying to write a query that would give me (here's the first example of the 5-minute aggregate):我正在尝试编写一个可以给我的查询(这是 5 分钟聚合的第一个示例):
| Time | Open | Close | High | Low | Volume | |------------------|------|-------|------|-----|---------| | 2019-10-30 09:30 | 5 | 30 | 40 | 1 | 50224.4 |
Any tips?有小费吗? Am banging my head against the wall with the OVER clause and its PARTITION / RANGE options
我用 OVER 子句及其 PARTITION / RANGE 选项将我的头撞到墙上
The gist of the problem is rounding datetime values to 5 minute boundary which (assuming that the datatype is datetime
) could be done using DATEADD(MINUTE, DATEDIFF(MINUTE, 0, time) / 5 * 5, 0)
.问题的要点是将日期时间值四舍五入到 5 分钟边界(假设数据类型为
datetime
)可以使用DATEADD(MINUTE, DATEDIFF(MINUTE, 0, time) / 5 * 5, 0)
来完成。 Rest is basic grouping/window functions: Rest 是基本的分组/窗口功能:
WITH cte AS (
SELECT clamped_time
, [Open]
, [Close]
, [High]
, [Low]
, [Volume]
, rn1 = ROW_NUMBER() OVER (PARTITION BY clamped_time ORDER BY [Time])
, rn2 = ROW_NUMBER() OVER (PARTITION BY clamped_time ORDER BY [Time] DESC)
FROM t
CROSS APPLY (
SELECT DATEADD(MINUTE, DATEDIFF(MINUTE, 0, time) / 5 * 5, 0)
) AS x(clamped_time)
)
SELECT clamped_time
, MIN(CASE WHEN rn1 = 1 THEN [Open] END) AS [Open]
, MIN(CASE WHEN rn2 = 1 THEN [Close] END) AS [Close]
, MAX([High]) AS [High]
, MIN([Low]) AS [Low]
, AVG([Volume])
FROM cte
GROUP BY clamped_time
You want to analyze data by 5 minutes intervals.您希望以 5 分钟为间隔分析数据。 You could use window functions with the following partitioning clause:
您可以将 window 函数与以下分区子句一起使用:
partition by datepart(year, t.[time]),
datepart(month, t.[time]),
datepart(day, t.[time]),
datepart(hour, t.[time]),
(datepart(minute, t.[time]) / 5)
Query:询问:
select *
from (
select
t.time,
row_number() over(
partition by datepart(year, [time]),
datepart(month, [time]),
datepart(day, [time]),
datepart(hour, [time]),
(datepart(minute, [time]) / 5)
order by [time]
) [rn],
first_value([open]) over(
partition by datepart(year, [time]),
datepart(month, [time]),
datepart(day, [time]),
datepart(hour, [time]),
(datepart(minute, [time]) / 5)
order by [time]
) [open],
last_value([close]) over(
partition by datepart(year, [time]),
datepart(month, [time]),
datepart(day, [time]),
datepart(hour, [time]),
(datepart(minute, [time]) / 5)
order by [time]
) [close],
max([high]) over (
partition by datepart(year, [time]),
datepart(month, [time]),
datepart(day, [time]),
datepart(hour, [time]),
(datepart(minute, [time]) / 5)
) [high],
min([low]) over (
partition by datepart(year, [time]),
datepart(month, [time]),
datepart(day, [time]),
datepart(hour, [time]),
(datepart(minute, [time]) / 5)
) [low],
avg([volume]) over (
partition by datepart(year, [time]),
datepart(month, [time]),
datepart(day, [time]),
datepart(hour, [time]),
(datepart(minute, [time]) / 5)
) [volume]
from mytable t
) t
where rn = 1
you can try this.你可以试试这个。
SELECT
MIN([Time]) [Time],
Min([Open]) [Open],
LEAD(Min([Open])) OVER (ORDER BY MIN([Time])) AS [Close],
Max([High]) [High],
Min([Low]) [Low],
Avg(Volume) Volume
FROM SampleData
GROUP BY DATEADD(Minute, -1* DATEPART(Minute, Time) %5, Time)
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