计算每月的权重差异答案

【问题标题】：Compute month on month difference in weights计算每月的权重差异
【发布时间】：2021-02-18 15:33:17
【问题描述】：

我正在处理一些投资组合数据，但我对这种数据操作感到困惑。我有这个样本数据

df <- tibble(
  date = as.Date(c("2020-01-31", "2020-01-31", "2020-01-31", 
                   "2020-02-29", "2020-02-29", "2020-02-29",
                   "2020-03-31", "2020-03-31", "2020-03-31") ),
  id = c("KO", "AAPL", "MSFT",
         "KO", "AAPL", "GOOG", 
         "KO", "AAPL", "MSFT"),
  weight = c(0.3, 0.4, 0.3,
             0.5, 0.3, 0.2,
             0.6, 0.2, 0.2),
  
  `weight_change (desired column)` = c(NA, NA, NA,
                                       0.2, -0.1, 0.2,
                                       0.1, -0.1, 0.2)
)

这些是示例投资组合中的职位。投资组合每个月都会获得新的权重。我要计算的是每个项目的重量变化在前几个月的重量方面。在这个例子中，我们看到在 2 月底，KO 的当前权重为 0.5，比上个月增加了 0.2。 AAPL 下降了 0.1，而 GOOG 取代了 MSFT，因此与上个月的变化是其当前的全部权重：0.2。如何设置变异，使其查找前一个日期的股票并计算权重之间的差异？

【问题讨论】：

标签： r dplyr

【解决方案1】：

如果每个 'id' 的数据是每月的，我们可以做一个complete 来考虑缺失的月份，然后通过diff 做一个分组

library(dplyr)
library(tidyr)
library(zoo)    
df %>%
     mutate(yearmonth = as.Date(as.yearmon(date))) %>%
     group_by(id) %>% 
     complete(yearmonth = seq(first(yearmonth), last(yearmonth), by = '1 month')) %>%
     mutate(weight_change = if(n() == 1) weight else c(NA, diff(replace_na(weight, 0)))) %>%
     ungroup %>%
     select(names(df), weight_change) %>%
     filter(!is.na(date))
# A tibble: 9 x 5
#  date       id    weight `weight_change (desired column)` weight_change
#  <date>     <chr>  <dbl>                            <dbl>         <dbl>
#1 2020-01-31 AAPL     0.4                             NA          NA    
#2 2020-02-29 AAPL     0.3                             -0.1        -0.1  
#3 2020-03-31 AAPL     0.2                             -0.1        -0.100
#4 2020-02-29 GOOG     0.2                              0.2         0.2  
#5 2020-01-31 KO       0.3                             NA          NA    
#6 2020-02-29 KO       0.5                              0.2         0.2  
#7 2020-03-31 KO       0.6                              0.1         0.100
#8 2020-01-31 MSFT     0.3                             NA          NA    
#9 2020-03-31 MSFT     0.2                              0.2         0.2

【讨论】：

【解决方案2】：

这是我不那么紧凑的解决方案。我只是使用了一些辅助列，我将其保留以便人们可以跟进。

library(tidyverse)
library(lubridate)

df <- tibble(
  date = c("2020-01-31", "2020-01-31", "2020-01-31", 
                   "2020-02-29", "2020-02-29", "2020-02-29",
                   "2020-03-31", "2020-03-31", "2020-03-31"),
  id = c("KO", "AAPL", "MSFT", "KO", "AAPL", "GOOG", "KO", "AAPL", "MSFT"),
  weight = c(0.3, 0.4, 0.3, 0.5, 0.3, 0.2, 0.6, 0.2, 0.2),
  `weight_change (desired_column)` = c(NA, NA, NA, 0.2, -0.1, 0.2, 0.1, -0.1, 0.2)
) %>% #new code starts here
  mutate(
    date = as_date(date),
    date_ym = floor_date(date,
                         unit = "month"))%>%
  group_by(id)%>%
  arrange(date)%>%
  mutate(id_n = row_number(),
         prev_exist = case_when(lag(date_ym) == date_ym - months(1) ~ "immediate month", #if there is an immediate month
                                id_n == 1 & date != min(df$date)~ "new month", #if this is a new month
                                TRUE ~ "no immediate month"),
         weight_change = case_when(prev_exist == "new month"~ weight,
                                   prev_exist == "no immediate month" & id_n > 1~ weight,
                                   TRUE ~ weight-lag(weight)),
         date_ym = NULL,
         id_n  = NULL,
         prev_exist = NULL)

【讨论】：

【解决方案3】：

timetk 方法：

library(timetk)
df %>% 
   mutate(Month = lubridate::floor_date(date, "month")) %>%
   group_by(id) %>% 
   timetk::pad_by_time(.date_var = Month, .by="month") %>% 
   select(-Month) %>% 
   mutate(WC = if(n() == 1) weight else c(NA, diff(weight)))

A tibble: 10 x 5
Groups:   id [4]
   id    date       weight weight_change     WC
   <chr> <date>      <dbl>         <dbl>  <dbl>
 1 KO    2020-01-31    0.3          NA   NA    
 2 KO    2020-02-29    0.5           0.2  0.2  
 3 KO    2020-03-31    0.6           0.1  0.100
 4 AAPL  2020-01-31    0.4          NA   NA    
 5 AAPL  2020-02-29    0.3          -0.1 -0.1  
 6 AAPL  2020-03-31    0.2          -0.1 -0.100
 7 MSFT  2020-01-31    0.3          NA   NA    
 8 MSFT  NA           NA            NA   NA    
 9 MSFT  2020-03-31    0.2           0.2 NA    
10 GOOG  2020-02-29    0.2           0.2  0.2

【讨论】：