发表更新Notes7 分钟读完 (大约992个字)

A time series platform for the tidyverse

本文是学习 time_series_platform_for_tidyverse.pdf 的笔记。介绍了 tibbletime 包的使用示例。

首先我们需要为 tibble 数据框指定日期索引生成 time tibble 数据框：

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library(tibbletime)
data(FB)
FB$volume <- NULL
FB_time <- tbl_time(FB, index = date)
FB_time
#> # A time tibble: 1,008 x 7
#> # Index: date
#>    symbol date        open  high   low close adjusted
#>    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>
#>  1 FB     2013-01-02  27.4  28.2  27.4  28       28  
#>  2 FB     2013-01-03  27.9  28.5  27.6  27.8     27.8
#>  3 FB     2013-01-04  28.0  28.9  27.8  28.8     28.8
#>  4 FB     2013-01-07  28.7  29.8  28.6  29.4     29.4
#>  5 FB     2013-01-08  29.5  29.6  28.9  29.1     29.1
#>  6 FB     2013-01-09  29.7  30.6  29.5  30.6     30.6
#>  7 FB     2013-01-10  30.6  31.5  30.3  31.3     31.3
#>  8 FB     2013-01-11  31.3  32.0  31.1  31.7     31.7
#>  9 FB     2013-01-14  32.1  32.2  30.6  31.0     31.0
#> 10 FB     2013-01-15  30.6  31.7  29.9  30.1     30.1
#> # … with 998 more rows

计算收益率：

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library(dplyr)
FB_time %>%
  mutate(
    adjusted_return = adjusted / dplyr::lag(adjusted) - 1
  )
#> # A time tibble: 1,008 x 8
#> # Index: date
#>    symbol date        open  high   low close adjusted
#>    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>
#>  1 FB     2013-01-02  27.4  28.2  27.4  28       28  
#>  2 FB     2013-01-03  27.9  28.5  27.6  27.8     27.8
#>  3 FB     2013-01-04  28.0  28.9  27.8  28.8     28.8
#>  4 FB     2013-01-07  28.7  29.8  28.6  29.4     29.4
#>  5 FB     2013-01-08  29.5  29.6  28.9  29.1     29.1
#>  6 FB     2013-01-09  29.7  30.6  29.5  30.6     30.6
#>  7 FB     2013-01-10  30.6  31.5  30.3  31.3     31.3
#>  8 FB     2013-01-11  31.3  32.0  31.1  31.7     31.7
#>  9 FB     2013-01-14  32.1  32.2  30.6  31.0     31.0
#> 10 FB     2013-01-15  30.6  31.7  29.9  30.1     30.1
#> # … with 998 more rows, and 1 more variable: adjusted_return <dbl>

选择列：

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FB_time %>%
  select(adjusted)
#> # A tibble: 1,008 x 1
#>    adjusted
#>       <dbl>
#>  1     28  
#>  2     27.8
#>  3     28.8
#>  4     29.4
#>  5     29.1
#>  6     30.6
#>  7     31.3
#>  8     31.7
#>  9     31.0
#> 10     30.1
#> # … with 998 more rows

tibbletime 最好用的功能应该算是时间范围的筛选了：

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FB_time %>%
  filter_time("2013-01-01" ~ "2013-01-04")

#> # A time tibble: 3 x 7
#> # Index: date
#>   symbol date        open  high   low close adjusted
#>   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>
#> 1 FB     2013-01-02  27.4  28.2  27.4  28       28  
#> 2 FB     2013-01-03  27.9  28.5  27.6  27.8     27.8
#> 3 FB     2013-01-04  28.0  28.9  27.8  28.8     28.8

再例如，选择 2013 年 2 月的数据：

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FB_time %>%
  filter_time(~ "2013-02")

#> # A time tibble: 19 x 7
#> # Index: date
#>    symbol date        open  high   low close adjusted
#>    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>
#>  1 FB     2013-02-01  31.0  31.0  29.6  29.7     29.7
#>  2 FB     2013-02-04  29.1  29.2  28.0  28.1     28.1
#>  3 FB     2013-02-05  28.3  29.0  28.0  28.6     28.6
#>  4 FB     2013-02-06  28.7  29.3  28.7  29.0     29.0
#>  5 FB     2013-02-07  29.1  29.2  28.3  28.6     28.6
#>  6 FB     2013-02-08  28.9  29.2  28.5  28.5     28.5
#>  7 FB     2013-02-11  28.6  28.7  28.0  28.3     28.3
#>  8 FB     2013-02-12  27.7  28.2  27.1  27.4     27.4
#>  9 FB     2013-02-13  27.4  28.3  27.3  27.9     27.9
#> 10 FB     2013-02-14  28.0  28.6  28.0  28.5     28.5
#> 11 FB     2013-02-15  28.5  28.8  28.1  28.3     28.3
#> 12 FB     2013-02-19  28.2  29.1  28.1  28.9     28.9
#> 13 FB     2013-02-20  28.9  29.0  28.3  28.5     28.5
#> 14 FB     2013-02-21  28.3  28.5  27.2  27.3     27.3
#> 15 FB     2013-02-22  27.6  27.6  26.8  27.1     27.1
#> 16 FB     2013-02-25  27.2  27.6  27.2  27.3     27.3
#> 17 FB     2013-02-26  27.4  27.5  26.7  27.4     27.4
#> 18 FB     2013-02-27  27.3  27.3  26.6  26.9     26.9
#> 19 FB     2013-02-28  26.8  27.3  26.3  27.2     27.2

collapse_by() 可以把日期索引变成每年的最后一个观测值对应的日期：

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(FB_yearly <- collapse_by(FB_time, period = "year"))

#> # A time tibble: 1,008 x 7
#> # Index: date
#>    symbol date        open  high   low close adjusted
#>    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>
#>  1 FB     2013-12-31  27.4  28.2  27.4  28       28  
#>  2 FB     2013-12-31  27.9  28.5  27.6  27.8     27.8
#>  3 FB     2013-12-31  28.0  28.9  27.8  28.8     28.8
#>  4 FB     2013-12-31  28.7  29.8  28.6  29.4     29.4
#>  5 FB     2013-12-31  29.5  29.6  28.9  29.1     29.1
#>  6 FB     2013-12-31  29.7  30.6  29.5  30.6     30.6
#>  7 FB     2013-12-31  30.6  31.5  30.3  31.3     31.3
#>  8 FB     2013-12-31  31.3  32.0  31.1  31.7     31.7
#>  9 FB     2013-12-31  32.1  32.2  30.6  31.0     31.0
#> 10 FB     2013-12-31  30.6  31.7  29.9  30.1     30.1
#> # … with 998 more rows

FB_yearly %>%
  select(date) %>%
  unique()

#> # A time tibble: 4 x 1
#> # Index: date
#>   date      
#>   <date>    
#> 1 2013-12-31
#> 2 2014-12-31
#> 3 2015-12-31
#> 4 2016-12-30

这样我们就可以通过下面的方式把高频数据汇总为低频数据：

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FB_time %>%
  collapse_by("year") %>%
  group_by(date) %>%
  summarise(adjusted_mean = mean(adjusted))

#> # A time tibble: 4 x 2
#> # Index: date
#>   date       adjusted_mean
#>   <date>             <dbl>
#> 1 2013-12-31          35.5
#> 2 2014-12-31          68.8
#> 3 2015-12-31          88.8
#> 4 2016-12-30         117.

再例如计算所有变量半年期的均值：

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FB_time %>%
  collapse_by("2 quarter") %>%
  group_by(date) %>%
  summarise_if(is.numeric, mean)

#> # A time tibble: 8 x 6
#> # Index: date
#>   date        open  high   low close adjusted
#>   <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>
#> 1 2013-06-28  27.0  27.4  26.6  27.0     27.0
#> 2 2013-12-31  43.6  44.4  43.0  43.7     43.7
#> 3 2014-06-30  62.5  63.4  61.4  62.4     62.4
#> 4 2014-12-31  74.8  75.7  74.0  74.9     74.9
#> 5 2015-06-30  80.0  80.8  79.3  80.0     80.0
#> 6 2015-12-31  97.2  98.3  96.0  97.2     97.2
#> 7 2016-06-30 111.  112.  109.  110.     110.
#> 8 2016-12-30 124.  124.  122.  123.     123.

还可以和 group_by() 一起使用：

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data("FANG")
FANG_time <- FANG %>%
  group_by(symbol) %>%
  as_tbl_time(date)

FANG_time %>%
  collapse_by("year") %>%
  group_by(symbol, date) %>%
  summarise_all(median) %>%
  print(n = 12)

#> # A time tibble: 16 x 8
#> # Index:  date
#> # Groups: symbol [4]
#>    symbol date        open  high   low close   volume adjusted
#>    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
#>  1 AMZN   2013-12-31 282.  285.  280.  282.   2556250    282.
#>  2 AMZN   2014-12-31 328.  331.  323.  326.   3498200    326.
#>  3 AMZN   2015-12-31 440.  445.  436.  440.   3245950    440.
#>  4 AMZN   2016-12-30 727.  731.  721.  728.   3594800    728.
#>  5 FB     2013-12-31  29.7  30.4  29.4  29.8 49838850     29.8
#>  6 FB     2014-12-31  69.3  70.0  68.4  69.1 42264250     69.1
#>  7 FB     2015-12-31  86.8  87.8  85.6  86.8 23898750     86.8
#>  8 FB     2016-12-30 118.  119.  117.  118.  20993100    118.
#>  9 GOOG   2013-12-31 876.  880.  871.  876.   3838400    438.
#> 10 GOOG   2014-12-31 569.  574.  563.  568.   1877200    565.
#> 11 GOOG   2015-12-31 564.  571.  559.  563.   1817850    563.
#> 12 GOOG   2016-12-30 743.  747.  737.  743.   1587300    743.
#> # … with 4 more rows

和 tidyfinance 一起使用：

计算日度收益率：

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# devtools::install_github("DavisVaughan/tidyfinance")
library(tidyfinance)
FANG_time %>%
  calculate_return(adjusted, period = "daily") %>%
  select(symbol, date, adjusted, adjusted_return)

#> # A time tibble: 4,032 x 4
#> # Index:  date
#> # Groups: symbol [4]
#>    symbol date       adjusted adjusted_return
#>    <chr>  <date>        <dbl>           <dbl>
#>  1 FB     2013-01-02     28           0      
#>  2 FB     2013-01-03     27.8        -0.00821
#>  3 FB     2013-01-04     28.8         0.0356
#>  4 FB     2013-01-07     29.4         0.0229
#>  5 FB     2013-01-08     29.1        -0.0122
#>  6 FB     2013-01-09     30.6         0.0526
#>  7 FB     2013-01-10     31.3         0.0232
#>  8 FB     2013-01-11     31.7         0.0134
#>  9 FB     2013-01-14     31.0        -0.0243
#> 10 FB     2013-01-15     30.1        -0.0275
#> # … with 4,022 more rows

计算年度收益：

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FANG_time %>%
  calculate_return(open:low, adjusted, period = "year") %>%
  select(symbol, date, contains('return'))

#> # A time tibble: 20 x 6
#> # Index:  date
#> # Groups: symbol [4]
#>    symbol date       open_return high_return low_return
#>    <chr>  <date>           <dbl>       <dbl>      <dbl>
#>  1 FB     2013-01-02      0           0          0     
#>  2 FB     2013-12-31      0.972       0.947      0.966
#>  3 FB     2014-12-31      0.470       0.455      0.444
#>  4 FB     2015-12-31      0.333       0.330      0.344
#>  5 FB     2016-12-30      0.1000      0.100      0.0970
#>  6 AMZN   2013-01-02      0           0          0     
#>  7 AMZN   2013-12-31      0.541       0.545      0.555
#>  8 AMZN   2014-12-31     -0.210      -0.215     -0.213
#>  9 AMZN   2015-12-31      1.20        1.20       1.18  
#> 10 AMZN   2016-12-30      0.117       0.116      0.107
#> 11 NFLX   2013-01-02      0           0          0     
#> 12 NFLX   2013-12-31      2.84        2.85       3.01  
#> 13 NFLX   2014-12-31     -0.0609     -0.0634    -0.0608
#> 14 NFLX   2015-12-31     -0.661      -0.660     -0.665
#> 15 NFLX   2016-12-30      0.0863      0.0776     0.0816
#> 16 GOOG   2013-01-02      0           0          0     
#> 17 GOOG   2013-12-31      0.546       0.542      0.544
#> 18 GOOG   2014-12-31     -0.522      -0.525     -0.525
#> 19 GOOG   2015-12-31      0.448       0.445      0.442
#> 20 GOOG   2016-12-30      0.0172      0.0173     0.0159
#> # … with 1 more variable: adjusted_return <dbl>

计算累计收益率和回撤指标：

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FANG_return <- FANG_time %>%
  select(symbol, date, adjusted) %>%
  calculate_return(adjusted, period = "daily") %>%
  mutate(
    drawdown = drawdown(adjusted_return),
    cum_ret = cumulative_return(adjusted_return)
  )
FANG_return

#> # A time tibble: 4,032 x 6
#> # Index:  date
#> # Groups: symbol [4]
#>    symbol date       adjusted adjusted_return drawdown  cum_ret
#>    <chr>  <date>        <dbl>           <dbl>    <dbl>    <dbl>
#>  1 FB     2013-01-02     28           0        0        0      
#>  2 FB     2013-01-03     27.8        -0.00821 -0.00821 -0.00821
#>  3 FB     2013-01-04     28.8         0.0356   0        0.0271
#>  4 FB     2013-01-07     29.4         0.0229   0        0.0507
#>  5 FB     2013-01-08     29.1        -0.0122  -0.0122   0.0379
#>  6 FB     2013-01-09     30.6         0.0526   0        0.0925
#>  7 FB     2013-01-10     31.3         0.0232   0        0.118  
#>  8 FB     2013-01-11     31.7         0.0134   0        0.133  
#>  9 FB     2013-01-14     31.0        -0.0243  -0.0243   0.105  
#> 10 FB     2013-01-15     30.1        -0.0275  -0.0511   0.0750
#> # … with 4,022 more rows

计算月度总收益：

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FANG_return_monthly <- FANG_return %>%
  collapse_by("month") %>%
  group_by(symbol, date) %>%
  summarise(monthly_return = total_return(adjusted_return))
FANG_return_monthly

#> # A time tibble: 192 x 3
#> # Index:  date
#> # Groups: symbol [4]
#>    symbol date       monthly_return
#>    <chr>  <date>              <dbl>
#>  1 AMZN   2013-01-31        0.0318
#>  2 AMZN   2013-02-28       -0.00463
#>  3 AMZN   2013-03-28        0.00840
#>  4 AMZN   2013-04-30       -0.0476
#>  5 AMZN   2013-05-31        0.0606
#>  6 AMZN   2013-06-28        0.0315
#>  7 AMZN   2013-07-31        0.0847
#>  8 AMZN   2013-08-30       -0.0672
#>  9 AMZN   2013-09-30        0.113  
#> 10 AMZN   2013-10-31        0.164  
#> # … with 182 more rows

绘图：

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# 累积收益率
plot_cum_ret <- FANG_return %>%
  ggplot(aes(x = date, y = cum_ret, color = symbol)) +
  geom_line() +
  theme_minimal(base_family = enfont) +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_blank()) +
  theme(axis.ticks.x = element_blank()) +
  labs(y = "Cumulative Return",
       title = "Performance summary: Facebook, Amazon, Netflix, Google") +
  scale_color_brewer(palette = "Dark2")

# 月度收益率
plot_month_ret <- FANG_return %>%
  calculate_return(adjusted, period = "monthly") %>%
  ggplot(aes(x = date, y = adjusted_return, fill = symbol)) +
  geom_col(width = 15, position = position_dodge()) +
  theme_minimal(base_family = enfont) +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  labs(y = "Monthly Return") +
  theme(legend.position = "none") +
  scale_fill_brewer(palette = "Dark2")

# 回撤指标
plot_drawdown <- FANG_return %>%
  ggplot(aes(x = date, y = drawdown, fill = symbol)) +
  geom_area(position = position_identity(), alpha = 0.5) +
  theme_minimal(base_family = enfont) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  labs(x = "", y = "Drawdown") +
  scale_fill_brewer(palette = "Dark2") +
  theme(axis.text.x = element_text(angle = 45, size = 5)) +
  theme(legend.position = "bottom", legend.margin = margin(t = -10))

library(patchwork)
plot_cum_ret +
  plot_month_ret +
  plot_drawdown +
  plot_layout(ncol = 1, heights = c(2, 1, 1))

A time series platform for the tidyverse

https://tidyfriday.cn/posts/31679/

作者

Painter

发布于

2019-04-01

更新于

2019-09-21

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