感谢提问，这个目前没有特别高效的算法，但是可以用moving + 自定义函数来实现。未来DolphinDB会针对这种场景做一些优化。

1.30.15及以上版本可以用一行代码实现：

//t是模拟的四列数据
t = table(take(`IBM, 100) as code, 2020.01.01 + 1..100 as date, rand(100,100) + 20 as volume, rand(10,100) + 100.0 as close)
//moving支持用户使用自定义匿名聚合函数(https://www.dolphindb.cn/cn/help/130/Functionalprogramming/AnonymousFunction.html)
select code, date, moving(defg(vol, close){return close[isort(vol, false).subarray(0:min(5,close.size()))].avg()}, (volume, close), 20) from t context by code 

其他版本可以用自定义命名函数实现：

defg top_5_close(vol,close){
return close[isort(vol, false).subarray(0:min(5,close.size()))].avg()
}
select code, date, moving(top_5_close,(volume, close), 20) from t context by code 

t = table(take(`IBM, 10000) as code, 2020.01.01 + rand(100,10000) as date, rand(100,10000) + 20 as volume, rand(10,10000) + 100.0 as close)
t = select * from t order by date
select code, date, moving(defg(vol, close) {return sort(vol, false)[0:min(5,close.size())].avg()}, (volume, close), 20) from t context by code