我正在尝试计算r中的fama法国因子。 经过几天的汗水和绝望，我设法计算了6种投资组合的回报...只是发现了一个我似乎无法解决的问题。
我的数据大致如下所示，这只是一个简化的数据集，用于说明我的问题：
> TestX = data.table(Group = c("SM", "SM", "SM", "SH", "SH", "SH", "SL", "SL", "SL"), Date= as.Date(c("1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-07-30", "1995-07-30")), Code= c("C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9"), SMRet = c(2,3,3, NA, NA, NA, NA, NA, NA), SHRet = c(NA, NA, NA, 5,5,5, NA, NA, NA), SLRet = c(NA, NA, NA, NA, NA, NA, 0,1,2) )
> TestX
Group Date Code SMRet SHRet SLRet
1: SM 1995-07-30 C1 2 NA NA
2: SM 1995-07-30 C2 3 NA NA
3: SM 1995-07-30 C3 3 NA NA
4: SH 1995-07-30 C4 NA 5 NA
5: SH 1995-07-30 C5 NA 5 NA
6: SH 1995-07-30 C6 NA 5 NA
7: SL 1995-07-30 C7 NA NA 0
8: SL 1995-07-30 C8 NA NA 1
9: SL 1995-07-30 C9 NA NA 2
Group给出了该组（SmallMedium，SmallHigh，SmallLow，我在真实的data.table中有其他组）。代码给出了各自的公司代码等。
我想要做的是使用相应的因素创建一个新列。
为此，我需要进行以下计算：
(Smret+SHret+SLret)/3，但是我该怎么做？
TestX[, Factor := (SMRet+SHRet+SLRet)/3, by = Date]
工作，我到处都只有NA。
Group Date Code SMRet SHRet SLRet Factor
1: SM 1995-07-30 C1 2 NA NA NA
2: SM 1995-07-30 C2 3 NA NA NA
3: SM 1995-07-30 C3 3 NA NA NA
4: SH 1995-07-30 C4 NA 5 NA NA
5: SH 1995-07-30 C5 NA 5 NA NA
6: SH 1995-07-30 C6 NA 5 NA NA
7: SL 1995-07-30 C7 NA NA 0 NA
8: SL 1995-07-30 C8 NA NA 1 NA
9: SL 1995-07-30 C9 NA NA 2 NA
我还需要按日期分组。实际数据表还有另外402个月。
谢谢。
编辑：这是一个更好的data.table来说明我的问题
TestX = data.table(Group = c("SM", "SM", "SH", "SH", "SL", "SL", "SM", "SM", "SH", "SH", "SL", "SL"), Date= as.Date(c("1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-07-30","1995-08-30","1995-08-30", "1995-08-30", "1995-08-30","1995-08-30","1995-08-30")), Code= c("C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9", "c10", "c11", "12"), SMRet = c(2,3, NA, NA, NA, NA, 4, 5, NA, NA, NA, NA), SHRet = c(NA, NA, 5, 5, NA, NA, NA, NA, 3, 4, NA, NA), SLRet = c(NA, NA, NA, NA, 0, 1, NA,NA,NA, NA, 2,3))
> TestX
Group Date Code SMRet SHRet SLRet
1: SM 1995-07-30 C1 2 NA NA
2: SM 1995-07-30 C2 3 NA NA
3: SH 1995-07-30 C3 NA 5 NA
4: SH 1995-07-30 C4 NA 5 NA
5: SL 1995-07-30 C5 NA NA 0
6: SL 1995-07-30 C6 NA NA 1
7: SM 1995-08-30 C7 4 NA NA
8: SM 1995-08-30 C8 5 NA NA
9: SH 1995-08-30 C9 NA 3 NA
10: SH 1995-08-30 c10 NA 4 NA
11: SL 1995-08-30 c11 NA NA 2
12: SL 1995-08-30 12 NA NA 3
这是期望的结果：
Group Date Code SMRet SHRet SLRet Factor
1: SM 1995-07-30 C1 2 NA NA 5.333333
2: SM 1995-07-30 C2 3 NA NA 5.333333
3: SH 1995-07-30 C3 NA 5 NA 5.333333
4: SH 1995-07-30 C4 NA 5 NA 5.333333
5: SL 1995-07-30 C5 NA NA 0 5.333333
6: SL 1995-07-30 C6 NA NA 1 5.333333
7: SM 1995-08-30 C7 4 NA NA 7.000000
8: SM 1995-08-30 C8 5 NA NA 7.000000
9: SH 1995-08-30 C9 NA 3 NA 7.000000
10: SH 1995-08-30 c10 NA 4 NA 7.000000
11: SL 1995-08-30 c11 NA NA 2 7.000000
12: SL 1995-08-30 12 NA NA 3 7.000000
so：每个月：（SMRet + ShRet + SLRet）/ 3
答案 0 :(得分：1)
您可以使用以下代码来计算R中的fama法式因子：
TestX[ , newvar := sum(SMRet, SHRet, SLRet, na.rm=TRUE)/3, by=Date]
答案 1 :(得分：1)
我认为add_action('pmxi_saved_post', 'post_saved', 10, 1);
function post_saved($id) {
$original_stock = get_post_meta($id, '_stock', true);
$new_stock = get_post_meta($id, '_custom_stock_placeholder', true);
$combined_stock= $original_stock + $new_stock;
update_post_meta($id, '_stock', $combined_stock);
}
是用于此任务的便捷程序包，但它可能不如import pandas as pd
import numpy as np
df2 = pd.DataFrame({
'date': [20130101,20130101, 20130105, 20130105, 20130107, 20130108],
'price': [25, 16.3, 23.5, 27, 40, 8],
})
快。您可以使用tidyverse轻松按组进行计算：
data.table