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我正在尝试根据相应的年度价值将一些基本数学应用于每日股票价值。

代表

（每日价格）

library(tidyquant)

data(FANG)

# daily prices

FANG %>%
  select(c(date, symbol, adjusted)) %>%
  group_by(symbol)

# A tibble: 4,032 x 3
# Groups:   symbol [4]
   date       symbol adjusted
   <date>     <chr>     <dbl>
 1 2013-01-02 FB         28  
 2 2013-01-03 FB         27.8
 3 2013-01-04 FB         28.8
 4 2013-01-07 FB         29.4
 5 2013-01-08 FB         29.1
 6 2013-01-09 FB         30.6
 7 2013-01-10 FB         31.3
 8 2013-01-11 FB         31.7
 9 2013-01-14 FB         31.0
10 2013-01-15 FB         30.1
# ... with 4,022 more rows

（每年最高价格）

FANG_yearly_high <- 
  FANG %>%
  group_by(symbol) %>%
  summarise_by_time(
    .date_var = date, 
    .by = "year", 
    price = AVERAGE(adjusted))

# Groups:   symbol [4]
   symbol date       price
   <chr>  <date>     <dbl>
 1 AMZN   2013-01-01 404. 
 2 AMZN   2014-01-01 407. 
 3 AMZN   2015-01-01 694. 
 4 AMZN   2016-01-01 844. 
 5 FB     2013-01-01  58.0
 6 FB     2014-01-01  81.4
 7 FB     2015-01-01 109. 
 8 FB     2016-01-01 133. 
 9 GOOG   2013-01-01 560. 
10 GOOG   2014-01-01 609. 
11 GOOG   2015-01-01 777. 
12 GOOG   2016-01-01 813. 
13 NFLX   2013-01-01  54.4
14 NFLX   2014-01-01  69.2
15 NFLX   2015-01-01 131. 
16 NFLX   2016-01-01 128.

我想将每个每日价格除以该年度相应的最高价格。

我试过：

  FANG %>%
  group_by(symbol) %>%
  summarise_by_time(
    .date_var = date, 
    .by = "year", 
    price = AVERAGE(adjusted) / YEAR(date(MAX(adjusted)))
    )

并得到这个错误：

as.POSIXlt.numeric(x, tz = tz(x)) 中的错误：必须提供“原点”

有什么明智的方法可以做到这一点吗？谢谢

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1 回答 1

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summarise_by_time如果你只是想总结一下就好了。但是您想将每日价格除以一个期间的最大值。所以你需要使用mutate。下面是2个例子。第一个是每日价格，第二个是每周价格。您可以轻松地将每周版本调整为每月版本。

library(tidyquant)
library(dplyr)

data(FANG)

# daily prices

FANG %>%
  select(c(date, symbol, adjusted)) %>%
  group_by(symbol, year = year(date)) %>% 
  mutate(price_pct = adjusted / max(adjusted))

# A tibble: 4,032 x 5
# Groups:   symbol, year [16]
   date       symbol adjusted  year price_pct
   <date>     <chr>     <dbl> <dbl>     <dbl>
 1 2013-01-02 FB         28    2013     0.483
 2 2013-01-03 FB         27.8  2013     0.479
 3 2013-01-04 FB         28.8  2013     0.496
 4 2013-01-07 FB         29.4  2013     0.508
 5 2013-01-08 FB         29.1  2013     0.501
 6 2013-01-09 FB         30.6  2013     0.528
 7 2013-01-10 FB         31.3  2013     0.540
 8 2013-01-11 FB         31.7  2013     0.547
 9 2013-01-14 FB         31.0  2013     0.534
10 2013-01-15 FB         30.1  2013     0.519
# ... with 4,022 more rows

每周/每月：

# weekly
FANG %>%
  select(c(date, symbol, adjusted)) %>%
  group_by(symbol) %>% 
  tq_transmute(mutate_fun = to.period,
               period = "weeks"   # change weeks to months for monthly
               ) %>% 
  group_by(symbol, year = year(date)) %>% 
  mutate(price_pct = adjusted / max(adjusted))

# A tibble: 836 x 5
# Groups:   symbol, year [16]
   symbol date       adjusted  year price_pct
   <chr>  <date>        <dbl> <dbl>     <dbl>
 1 FB     2013-01-04     28.8  2013     0.519
 2 FB     2013-01-11     31.7  2013     0.572
 3 FB     2013-01-18     29.7  2013     0.535
 4 FB     2013-01-25     31.5  2013     0.569
 5 FB     2013-02-01     29.7  2013     0.536
 6 FB     2013-02-08     28.5  2013     0.515
 7 FB     2013-02-15     28.3  2013     0.511
 8 FB     2013-02-22     27.1  2013     0.489
 9 FB     2013-03-01     27.8  2013     0.501
10 FB     2013-03-08     28.0  2013     0.504
# ... with 826 more rows
于 2020-03-23T10:42:30.720 回答