library(tidyquant)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## ── Attaching core tidyquant packages ─────────────────────── tidyquant 1.0.11 ──
## ✔ PerformanceAnalytics 2.0.8 ✔ TTR 0.24.4
## ✔ quantmod 0.4.28 ✔ xts 0.14.1
## ── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date() masks base::as.Date()
## ✖ zoo::as.Date.numeric() masks base::as.Date.numeric()
## ✖ PerformanceAnalytics::legend() masks graphics::legend()
## ✖ quantmod::summary() masks base::summary()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks xts::first()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks xts::last()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(timetk)
##
## Attaching package: 'timetk'
## The following object is masked from 'package:tidyquant':
##
## FANG
read_delim("/Users/Frezza/Downloads/tej_day_price_2024_20250630.txt", delim = "\t")
## Rows: 337347 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): CO_ID, TSE Sector, English Short Name
## dbl (9): Date, TSE ID, Open(NTD), High(NTD), Low(NTD), Close(NTD), Volume(10...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 337,347 × 12
## CO_ID Date `TSE ID` `TSE Sector` `English Short Name` `Open(NTD)`
## <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 1101 TCC 2.02e7 1101 01 TCC 32.5
## 2 1102 ACC 2.02e7 1102 01 ACC 37.3
## 3 1103 CHC 2.02e7 1103 01 CHC 17.8
## 4 1104 UCC 2.02e7 1104 01 UCC 26.1
## 5 1108 Lucky Cem… 2.02e7 1108 01 Lucky Cement 14.2
## 6 1109 HSINGTA 2.02e7 1109 01 HSINGTA 16.2
## 7 1110 Tuna Ceme… 2.02e7 1110 01 Tuna Cement 18.3
## 8 1201 WEI CHUAN 2.02e7 1201 02 WEI CHUAN 18.2
## 9 1203 Ve Wong 2.02e7 1203 02 Ve Wong 55.8
## 10 1210 GREATWALL 2.02e7 1210 02 GREATWALL 53.8
## # ℹ 337,337 more rows
## # ℹ 6 more variables: `High(NTD)` <dbl>, `Low(NTD)` <dbl>, `Close(NTD)` <dbl>,
## # `Volume(1000S)` <dbl>, `Amount(NTD1000)` <dbl>, `Market Cap.(NTD MN)` <dbl>
data_tsv <- read_tsv("/Users/Frezza/Downloads/tej_day_price_2024_20250630.txt")
## Rows: 337347 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): CO_ID, TSE Sector, English Short Name
## dbl (9): Date, TSE ID, Open(NTD), High(NTD), Low(NTD), Close(NTD), Volume(10...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
str(data_tsv)
## spc_tbl_ [337,347 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ CO_ID : chr [1:337347] "1101 TCC" "1102 ACC" "1103 CHC" "1104 UCC" ...
## $ Date : num [1:337347] 20240102 20240102 20240102 20240102 20240102 ...
## $ TSE ID : num [1:337347] 1101 1102 1103 1104 1108 ...
## $ TSE Sector : chr [1:337347] "01" "01" "01" "01" ...
## $ English Short Name : chr [1:337347] "TCC" "ACC" "CHC" "UCC" ...
## $ Open(NTD) : num [1:337347] 32.5 37.3 17.8 26.1 14.2 ...
## $ High(NTD) : num [1:337347] 32.5 37.4 17.8 26.2 14.2 ...
## $ Low(NTD) : num [1:337347] 32.3 36.9 17.6 26 14 ...
## $ Close(NTD) : num [1:337347] 32.4 37 17.6 26.1 14.1 ...
## $ Volume(1000S) : num [1:337347] 14937 6223 171 260 442 ...
## $ Amount(NTD1000) : num [1:337347] 518751 256522 3240 7736 6992 ...
## $ Market Cap.(NTD MN): num [1:337347] 262026 145941 14896 19995 6395 ...
## - attr(*, "spec")=
## .. cols(
## .. CO_ID = col_character(),
## .. Date = col_double(),
## .. `TSE ID` = col_double(),
## .. `TSE Sector` = col_character(),
## .. `English Short Name` = col_character(),
## .. `Open(NTD)` = col_double(),
## .. `High(NTD)` = col_double(),
## .. `Low(NTD)` = col_double(),
## .. `Close(NTD)` = col_double(),
## .. `Volume(1000S)` = col_double(),
## .. `Amount(NTD1000)` = col_double(),
## .. `Market Cap.(NTD MN)` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
# fetch data by using read.delim
data_delim <- read.delim("/Users/Frezza/Downloads/tej_day_price_2024_20250630.txt", sep = "\t")
glimpse(data_delim)
## Rows: 337,347
## Columns: 12
## $ CO_ID <chr> "1101 TCC", "1102 ACC", "1103 CHC", "1104 UCC", "1…
## $ Date <int> 20240102, 20240102, 20240102, 20240102, 20240102, …
## $ TSE.ID <int> 1101, 1102, 1103, 1104, 1108, 1109, 1110, 1201, 12…
## $ TSE.Sector <int> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ English.Short.Name <chr> "TCC", "ACC", "CHC", "UCC", "Lucky Cement", "HSING…
## $ Open.NTD. <dbl> 32.5373, 37.2642, 17.7825, 26.0628, 14.1679, 16.18…
## $ High.NTD. <dbl> 32.5373, 37.4442, 17.7825, 26.1505, 14.1679, 16.26…
## $ Low.NTD. <dbl> 32.3038, 36.9492, 17.5953, 25.9750, 14.0343, 16.13…
## $ Close.NTD. <dbl> 32.3972, 37.0392, 17.6421, 26.0628, 14.0788, 16.18…
## $ Volume.1000S. <int> 14937, 6223, 171, 260, 442, 228, 57, 126, 48, 1849…
## $ Amount.NTD1000. <int> 518751, 256522, 3240, 7736, 6992, 4159, 1075, 2409…
## $ Market.Cap..NTD.MN. <int> 262026, 145941, 14896, 19995, 6395, 6209, 10754, 9…
data_renamed <- data_delim %>%
# Rename specific columns by their presumed old name (X#) or actual position
rename(
date = `Date`,
id = `TSE.ID`,
name = `English.Short.Name`,
price = `Close.NTD.`,
cap = `Market.Cap..NTD.MN.`
)
glimpse(data_renamed)
## Rows: 337,347
## Columns: 12
## $ CO_ID <chr> "1101 TCC", "1102 ACC", "1103 CHC", "1104 UCC", "1108 …
## $ date <int> 20240102, 20240102, 20240102, 20240102, 20240102, 2024…
## $ id <int> 1101, 1102, 1103, 1104, 1108, 1109, 1110, 1201, 1203, …
## $ TSE.Sector <int> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ name <chr> "TCC", "ACC", "CHC", "UCC", "Lucky Cement", "HSINGTA",…
## $ Open.NTD. <dbl> 32.5373, 37.2642, 17.7825, 26.0628, 14.1679, 16.1807, …
## $ High.NTD. <dbl> 32.5373, 37.4442, 17.7825, 26.1505, 14.1679, 16.2696, …
## $ Low.NTD. <dbl> 32.3038, 36.9492, 17.5953, 25.9750, 14.0343, 16.1362, …
## $ price <dbl> 32.3972, 37.0392, 17.6421, 26.0628, 14.0788, 16.1807, …
## $ Volume.1000S. <int> 14937, 6223, 171, 260, 442, 228, 57, 126, 48, 1849, 40…
## $ Amount.NTD1000. <int> 518751, 256522, 3240, 7736, 6992, 4159, 1075, 2409, 28…
## $ cap <int> 262026, 145941, 14896, 19995, 6395, 6209, 10754, 9640,…
# Set data_long
data_long <- data_renamed %>%
select(id, date, price) %>%
mutate(
# Change 'date' to Date format (assuming YYYY/MM/DD structure)
date = ymd(date),
# Change 'id' to text (character)
id = as.character(id)
)
# Print the data
glimpse(data_long)
## Rows: 337,347
## Columns: 3
## $ id <chr> "1101", "1102", "1103", "1104", "1108", "1109", "1110", "1201", …
## $ date <date> 2024-01-02, 2024-01-02, 2024-01-02, 2024-01-02, 2024-01-02, 202…
## $ price <dbl> 32.3972, 37.0392, 17.6421, 26.0628, 14.0788, 16.1807, 18.3336, 1…
# Convert the data from long to wide
data_spread <- data_long %>%
spread(
key = date,
value = price
)
# Print the data
head(data_spread)
## id 2024-01-02 2024-01-03 2024-01-04 2024-01-05 2024-01-08 2024-01-09
## 1 1101 32.3972 31.9304 31.9304 32.0704 31.9771 31.7903
## 2 1102 37.0392 36.5892 37.0392 36.9942 37.1742 36.9042
## 3 1103 17.6421 17.5017 17.5017 17.5485 17.5953 17.5485
## 4 1104 26.0628 25.8873 25.5363 25.7995 25.7118 25.3607
## 5 1108 14.0788 14.0343 14.0788 14.0788 14.1679 13.8115
## 6 1109 16.1807 16.0918 16.1362 16.1362 16.0918 16.0473
## 2024-01-10 2024-01-11 2024-01-12 2024-01-15 2024-01-16 2024-01-17 2024-01-18
## 1 31.4636 31.4636 31.5103 31.4169 30.7167 30.0631 29.9231
## 2 36.6342 36.7242 36.5892 36.5892 35.9591 35.4190 35.3740
## 3 17.4081 17.5017 17.5017 17.4081 16.9870 16.8934 16.9402
## 4 25.3607 25.5801 25.6240 25.4485 25.0975 24.7903 24.8781
## 5 13.6778 13.7670 13.8115 13.7670 13.5442 13.4551 13.4551
## 6 16.0473 16.0918 16.0473 16.0029 15.8250 15.6472 15.6917
## 2024-01-19 2024-01-22 2024-01-23 2024-01-24 2024-01-25 2024-01-26 2024-01-29
## 1 30.2498 30.1565 30.5299 30.4366 30.5299 30.4366 30.2032
## 2 35.4640 35.5990 35.7791 35.9141 36.0041 36.0941 35.9591
## 3 16.8466 16.9402 16.8934 16.8934 16.8934 16.9870 16.9870
## 4 24.8342 25.0536 25.1852 25.4485 25.4046 25.3169 25.6240
## 5 13.4551 13.4996 13.5887 13.6333 13.5887 13.6333 13.7224
## 6 15.7806 15.9584 15.9139 15.9139 15.8695 15.8695 15.8695
## 2024-01-30 2024-01-31 2024-02-01 2024-02-02 2024-02-05 2024-02-15 2024-02-16
## 1 29.8764 29.7830 30.2965 30.1098 30.0164 29.8764 29.7830
## 2 35.8241 35.7791 36.0491 36.0491 35.5090 35.2840 35.6440
## 3 16.8934 16.7062 16.9402 16.9870 16.7998 16.7998 16.7062
## 4 25.6240 25.5801 25.7118 25.6679 25.4924 25.3607 25.4485
## 5 13.5887 13.5442 13.6778 13.5887 13.4551 13.6333 13.6333
## 6 15.8695 15.7361 15.7806 15.7806 15.7806 15.6472 15.6917
## 2024-02-19 2024-02-20 2024-02-21 2024-02-22 2024-02-23 2024-02-26 2024-02-27
## 1 30.6233 30.4833 30.3432 30.3899 30.2498 30.0164 29.7363
## 2 36.1391 36.1841 36.2741 36.3641 36.1841 36.2291 36.0941
## 3 16.7530 16.8934 16.8934 16.9402 16.8466 16.9402 16.7062
## 4 25.5801 25.3607 25.4046 25.5363 25.6240 25.7995 25.6679
## 5 14.0343 13.8561 13.8561 13.8561 13.8115 13.9006 13.8561
## 6 15.7806 15.7361 15.7361 15.7361 15.7361 15.7806 15.6917
## 2024-02-29 2024-03-01 2024-03-04 2024-03-05 2024-03-06 2024-03-07 2024-03-08
## 1 29.9698 29.9231 29.9231 29.8764 29.8764 29.8764 29.7363
## 2 36.4091 36.1391 36.0491 36.0491 36.0041 36.3191 36.3641
## 3 16.5658 16.6594 16.5658 16.4254 16.4722 16.3318 16.1914
## 4 26.0628 26.1066 26.8964 27.1158 27.3791 27.4230 27.2474
## 5 14.0343 14.0788 14.1234 14.3907 14.4798 14.3461 13.9452
## 6 15.8695 15.8250 15.7806 15.9139 15.9139 15.7361 15.6472
## 2024-03-11 2024-03-12 2024-03-13 2024-03-14 2024-03-15 2024-03-18 2024-03-19
## 1 29.6897 29.8764 29.5963 29.8764 29.8297 29.5963 29.3162
## 2 36.3641 36.5441 36.3191 37.0392 36.9942 36.9942 36.4991
## 3 16.2382 16.1914 16.1446 16.2382 16.0978 16.0510 16.0510
## 4 27.8178 27.5985 27.5546 27.9495 27.5985 28.5199 28.3882
## 5 14.0788 14.1234 13.9452 14.4353 14.5244 13.8561 13.9006
## 6 15.6028 15.6472 15.6917 16.6696 16.5807 16.9808 16.9364
## 2024-03-20 2024-03-21 2024-03-22 2024-03-25 2024-03-26 2024-03-27 2024-03-28
## 1 28.8494 29.4096 29.0828 29.0361 29.4563 29.5963 29.5963
## 2 36.6342 37.2192 37.1292 37.1742 37.4442 37.0842 36.8592
## 3 16.0042 16.0978 16.0042 16.0042 16.1914 16.1914 16.2850
## 4 28.0372 28.3005 27.9056 27.5546 27.2913 27.4668 27.6423
## 5 13.8561 14.1234 14.0343 14.0788 14.2125 14.2125 14.1234
## 6 16.8030 16.8474 16.8474 16.9364 17.1142 17.1586 17.0697
## 2024-03-29 2024-04-01 2024-04-02 2024-04-03 2024-04-08 2024-04-09 2024-04-10
## 1 30.0164 30.0164 29.8297 29.8764 30.1565 30.2965 30.5299
## 2 37.1742 37.2192 36.9942 37.0392 37.5793 37.8043 38.2543
## 3 16.2850 16.3786 16.2382 16.3318 16.4254 16.5190 16.7062
## 4 27.7301 28.0811 27.9056 28.1250 28.7393 29.3535 28.9148
## 5 14.0788 14.2125 14.0788 14.1679 14.8808 14.6580 14.7026
## 6 17.0697 17.1586 17.2475 17.2920 17.4698 17.5587 17.7809
## 2024-04-11 2024-04-12 2024-04-15 2024-04-16 2024-04-17 2024-04-18 2024-04-19
## 1 30.2498 30.0631 30.2032 30.2965 30.4833 30.5299 29.8764
## 2 37.8493 37.6243 37.8493 37.2642 37.3092 37.9393 37.8043
## 3 16.6126 16.4254 16.4722 16.1914 16.3318 16.6594 16.4254
## 4 28.6076 28.6515 28.7393 28.2566 29.0903 29.2219 29.0464
## 5 14.5244 14.5689 14.5244 14.3907 14.6580 15.1927 14.8808
## 6 17.2031 17.2031 17.2920 17.0697 17.1142 17.2475 17.0253
## 2024-04-22 2024-04-23 2024-04-24 2024-04-25 2024-04-26 2024-04-29 2024-04-30
## 1 30.4366 30.2032 30.0164 29.8764 29.6897 30.2032 29.9231
## 2 38.4344 38.6144 38.2093 38.2543 38.4794 39.2445 39.3345
## 3 16.6594 16.7530 16.6594 16.7062 16.7530 17.5485 17.2209
## 4 29.3096 29.8800 29.5729 29.7045 29.5290 30.8453 30.8453
## 5 15.1036 15.0590 15.0590 15.0590 14.9699 15.3263 15.1036
## 6 17.2475 17.3364 17.3364 17.2475 17.2031 17.6476 17.7809
## 2024-05-02 2024-05-03 2024-05-06 2024-05-07 2024-05-08 2024-05-09 2024-05-10
## 1 30.1565 30.2498 30.8100 30.9968 30.9034 30.4833 31.0901
## 2 39.4245 39.5595 40.0545 39.7845 39.5145 39.1094 41.4047
## 3 17.6421 17.3613 17.3145 17.2209 17.1741 17.0338 17.1273
## 4 31.0208 30.9331 31.0647 30.4943 30.1433 30.3188 30.6698
## 5 15.3709 15.2372 15.2818 15.2372 15.0590 15.0590 15.3263
## 6 18.6255 18.4033 18.3588 18.0032 17.8699 17.6476 18.0032
## 2024-05-13 2024-05-14 2024-05-15 2024-05-16 2024-05-17 2024-05-20 2024-05-21
## 1 31.4636 31.1368 31.0901 31.3702 31.6503 31.6036 31.1368
## 2 41.2247 41.0447 41.0447 41.1797 40.1446 40.9096 40.1446
## 3 17.1741 17.1273 17.0806 17.3145 17.2677 17.3613 17.1273
## 4 29.0464 28.9148 29.0903 29.0464 29.2219 29.8800 29.5729
## 5 15.2818 14.9253 15.1481 15.1927 15.1927 15.2372 15.0590
## 6 17.8699 17.7365 17.6920 17.7809 17.7809 17.7365 17.5587
## 2024-05-22 2024-05-23 2024-05-24 2024-05-27 2024-05-28 2024-05-29 2024-05-30
## 1 32.0704 31.7437 31.7903 31.5569 31.6970 30.9968 30.6700
## 2 39.5145 39.5595 39.6045 39.2445 39.3345 38.5244 37.8043
## 3 17.2209 16.9402 16.8934 16.7998 16.9870 16.9402 16.7530
## 4 29.5290 29.0464 29.1780 29.3096 29.3974 29.3974 29.2219
## 5 15.0590 14.7917 14.8362 14.9253 15.0590 15.0144 14.9699
## 6 17.6031 17.1586 17.2475 17.2920 17.4253 17.2475 17.2920
## 2024-05-31 2024-06-03 2024-06-04 2024-06-05 2024-06-06 2024-06-07 2024-06-11
## 1 30.8100 30.8100 30.5299 30.5299 30.5299 30.8567 30.6700
## 2 37.3992 37.4442 37.0842 37.0842 36.5441 37.2642 37.2642
## 3 17.7825 17.4549 17.1273 17.2677 17.0806 17.2209 16.9870
## 4 29.8362 29.7045 29.6168 29.7484 29.5290 29.7045 29.4413
## 5 15.1036 15.0590 15.0144 15.0144 15.0144 15.0590 15.0590
## 6 17.3364 17.3809 17.2475 17.3364 17.1142 17.2475 17.2031
## 2024-06-12 2024-06-13 2024-06-14 2024-06-17 2024-06-18 2024-06-19 2024-06-20
## 1 30.8100 31.2302 31.7437 31.7437 31.6503 31.7903 31.8837
## 2 37.3092 37.4893 37.6243 37.7593 37.9393 38.8394 39.8745
## 3 17.0338 17.0338 17.1273 17.1273 17.1273 16.9870 17.1741
## 4 29.5290 29.4852 29.5290 29.5729 29.6168 29.7923 30.0556
## 5 15.0144 14.9699 15.0590 14.9699 14.9253 14.9253 14.9699
## 6 17.1586 17.2031 17.3364 17.2920 17.2031 17.2475 17.2920
## 2024-06-21 2024-06-24 2024-06-25 2024-06-26 2024-06-27 2024-06-28 2024-07-01
## 1 32.3505 31.9771 32.2105 32.1171 31.9771 31.9304 32.2670
## 2 40.0995 40.3696 39.2445 39.1544 39.4695 39.4695 39.7845
## 3 17.2209 17.1741 17.2677 17.1741 17.1273 17.1273 17.2677
## 4 30.4943 30.5382 30.6698 30.8453 31.1086 31.2841 31.8106
## 5 15.0590 15.0590 15.0144 14.9699 14.8808 14.9253 14.9253
## 6 17.3809 17.3364 17.4698 17.3809 17.3809 17.5142 17.6031
## 2024-07-02 2024-07-03 2024-07-04 2024-07-05 2024-07-08 2024-07-09 2024-07-10
## 1 32.4113 32.6998 32.8441 33.1326 33.2769 32.6998 32.5555
## 2 39.6495 41.0897 41.3597 40.2796 40.2346 40.0995 39.9645
## 3 17.2209 17.3145 17.4549 17.5485 17.5017 17.3613 17.4549
## 4 31.7667 32.2494 32.3810 32.2494 32.0300 31.6790 31.4157
## 5 14.9699 15.1036 15.1481 15.1481 15.1481 14.7917 14.7917
## 6 17.5142 17.6476 17.6031 17.6920 17.7809 17.6031 17.6920
## 2024-07-11 2024-07-12 2024-07-15 2024-07-16 2024-07-17 2024-07-18 2024-07-19
## 1 32.7479 32.9402 32.7960 32.8441 33.1807 33.4692 33.2769
## 2 40.5496 41.1347 40.9096 40.3246 39.7845 39.4538 38.6978
## 3 17.5017 17.5017 17.5017 17.3613 17.4081 17.5043 17.2639
## 4 31.8106 31.3719 31.3719 30.9665 31.3436 31.3907 30.7780
## 5 14.7026 14.7917 14.7917 14.8362 14.8808 14.8362 14.6135
## 6 17.5587 17.5587 17.6476 17.6920 17.7365 17.5473 17.3108
## 2024-07-22 2024-07-23 2024-07-26 2024-07-29 2024-07-30 2024-07-31 2024-08-01
## 1 33.0364 33.6135 33.5654 33.5654 33.3249 33.3249 33.2769
## 2 38.6505 38.9340 38.7923 39.3120 39.0285 39.1703 38.9340
## 3 17.1677 17.3120 17.2639 17.4562 17.4081 17.3120 17.5524
## 4 29.8353 30.3066 30.5423 31.6735 31.4850 31.4378 31.7678
## 5 14.3907 14.5689 14.4798 14.4353 14.4798 14.5689 14.6135
## 6 17.0270 17.0743 17.0743 17.1216 17.0270 17.1216 17.3108
## 2024-08-02 2024-08-05 2024-08-06 2024-08-07 2024-08-08 2024-08-09 2024-08-12
## 1 32.9883 31.8342 32.5075 32.5075 32.6998 33.1807 33.1326
## 2 39.1703 38.1308 39.4065 39.3120 40.0208 40.9185 41.1075
## 3 17.2639 16.5425 16.7349 16.7349 16.5425 16.8311 17.0234
## 4 30.9194 28.3742 28.6570 29.3640 29.0341 28.8455 29.3640
## 5 14.4353 13.5442 13.6778 13.9006 13.8115 14.1679 14.1679
## 6 17.0270 16.1284 16.3176 16.5541 16.5068 16.5541 16.5068
## 2024-08-13 2024-08-14 2024-08-15 2024-08-16 2024-08-19 2024-08-20 2024-08-21
## 1 33.2769 32.3151 31.6419 31.5938 31.4495 31.5457 31.5457
## 2 41.2020 41.7690 41.2965 41.7218 41.9108 42.2415 41.8163
## 3 17.2639 17.1196 17.0715 17.0715 17.1196 17.2639 17.2639
## 4 28.8455 29.1283 28.7984 28.8927 28.8927 28.6570 28.9869
## 5 14.1679 14.2570 14.2570 14.3016 14.3461 14.3461 14.4798
## 6 16.5068 17.1689 17.0743 17.2162 17.2635 17.3581 17.3581
## 2024-08-22 2024-08-23 2024-08-26 2024-08-27 2024-08-28 2024-08-29 2024-08-30
## 1 31.7861 31.6900 31.8823 31.7861 31.6419 31.6900 31.6900
## 2 42.3360 42.4778 42.5250 42.4305 42.4305 42.4305 42.6195
## 3 17.2158 17.2158 17.3600 17.4562 17.4562 17.4562 17.6486
## 4 28.7041 28.7041 28.6570 28.6570 28.6570 28.7041 29.1283
## 5 14.4798 14.4798 14.5244 14.4798 14.4323 14.3848 14.4323
## 6 17.3581 17.4054 17.4527 17.4054 17.3581 17.3581 17.3581
## 2024-09-02 2024-09-03 2024-09-04 2024-09-05 2024-09-06 2024-09-09 2024-09-10
## 1 31.4976 30.9206 30.5358 30.3435 30.5358 30.3435 30.2473
## 2 42.6668 42.6195 41.8635 42.2415 42.4778 42.2888 42.3833
## 3 17.6005 17.6005 16.8311 17.0234 16.8311 16.5906 16.7349
## 4 28.7984 28.4685 27.6201 27.5258 27.5729 27.4315 27.4787
## 5 14.3374 14.3374 13.8152 13.8626 13.8152 13.7202 13.8626
## 6 17.4054 17.3581 17.0743 17.0743 17.2162 17.0743 17.0743
## 2024-09-11 2024-09-12 2024-09-13 2024-09-16 2024-09-18 2024-09-19 2024-09-20
## 1 30.1031 30.2954 30.5839 30.9686 30.8725 30.9686 31.0648
## 2 42.0525 42.2415 42.4305 42.5250 42.3360 42.4778 42.5250
## 3 16.5425 16.8311 17.0234 17.2639 17.3120 17.4081 16.7830
## 4 27.4787 27.9029 28.0914 28.4685 28.6099 28.7041 28.8927
## 5 13.7677 13.8626 13.8626 14.0051 14.0525 14.1950 14.1000
## 6 16.9797 17.1216 17.4527 17.5473 17.4527 17.5473 17.5946
## 2024-09-23 2024-09-24 2024-09-25 2024-09-26 2024-09-27 2024-09-30 2024-10-01
## 1 30.9686 31.0167 31.4976 31.3533 32.0266 32.5075 32.4113
## 2 42.6195 42.7140 43.2810 43.6590 44.0843 45.1238 44.8403
## 3 16.8792 17.0234 17.3600 17.3120 17.5043 17.5043 17.4562
## 4 28.6570 28.7041 28.9869 28.8927 28.9869 29.1283 28.8455
## 5 14.0525 14.1000 14.1475 14.1475 14.2424 14.3848 14.4323
## 6 17.5000 17.4527 17.4527 17.5473 17.7838 17.8311 17.7838
## 2024-10-04 2024-10-07 2024-10-08 2024-10-09 2024-10-11 2024-10-14 2024-10-15
## 1 32.5075 32.2189 31.7861 31.2091 31.2572 31.3053 31.2572
## 2 45.0293 44.7458 44.7458 44.0370 43.4700 43.4700 43.9898
## 3 17.2158 17.2639 17.1677 17.1196 17.0234 16.9753 16.8792
## 4 28.6099 29.2226 28.8455 28.5627 28.4685 28.3742 28.3271
## 5 14.1950 14.1475 13.9576 13.8152 13.7677 13.7677 13.7677
## 6 17.6419 17.7365 17.5473 17.5473 17.4527 17.4054 17.2162
## 2024-10-16 2024-10-17 2024-10-18 2024-10-21 2024-10-22 2024-10-23 2024-10-24
## 1 30.9206 31.2572 31.4976 31.0648 30.9206 30.9686 30.7763
## 2 43.1865 44.4150 44.9820 44.4150 44.3678 43.7535 44.2733
## 3 16.8311 16.9272 17.1196 17.1196 17.0234 16.8792 16.8792
## 4 29.7882 29.0812 28.8927 28.8455 28.9398 28.7984 28.6099
## 5 13.8152 13.6727 13.8152 13.8152 13.8152 13.8626 13.7677
## 6 17.4054 17.4054 17.4054 17.5473 17.5946 17.5000 17.5473
## 2024-10-25 2024-10-28 2024-10-29 2024-10-30 2024-11-01 2024-11-04 2024-11-05
## 1 30.8244 30.9206 30.8725 30.7282 30.8725 31.2091 31.3053
## 2 44.1788 44.1788 44.0370 44.0843 43.7535 44.5095 44.9820
## 3 16.9753 17.0234 16.8792 16.9272 17.0234 16.8311 16.9753
## 4 28.6099 28.8927 28.5156 28.5156 29.4111 28.8927 28.9398
## 5 13.8152 14.2899 14.0051 13.9101 14.0051 13.7202 13.7202
## 6 17.5000 17.5000 17.5000 17.4527 17.5946 17.5000 17.5473
## 2024-11-06 2024-11-07 2024-11-08 2024-11-11 2024-11-12 2024-11-13 2024-11-14
## 1 30.8244 30.7282 30.6801 31.0167 31.2091 31.2091 31.1129
## 2 44.4623 44.7930 44.9348 43.8953 43.5173 43.2338 43.0920
## 3 17.0715 17.1677 17.2639 17.3600 17.0715 17.1677 16.9753
## 4 28.8455 28.7984 28.2799 28.1857 27.8557 27.6201 27.3373
## 5 13.8152 13.8626 13.8152 13.7677 13.6727 13.7202 13.6727
## 6 17.5473 17.5473 17.6419 17.6419 17.4527 17.3581 17.2635
## 2024-11-15 2024-11-18 2024-11-19 2024-11-20 2024-11-21 2024-11-22 2024-11-25
## 1 31.8823 31.9785 32.2189 32.2189 31.9304 32.0747 32.6036
## 2 43.6590 42.7613 41.5800 41.3438 41.2965 41.4855 41.4855
## 3 17.2639 17.3120 17.4081 17.3120 17.3120 17.3120 17.5524
## 4 27.5258 27.6672 27.6201 27.4787 27.5729 27.7615 28.0914
## 5 13.7677 13.8152 13.7202 13.7202 13.7677 13.8152 13.8152
## 6 17.3108 17.2635 17.3108 17.3108 17.3108 17.4527 17.3581
## 2024-11-26 2024-11-27 2024-11-28 2024-11-29 2024-12-02 2024-12-03 2024-12-04
## 1 32.5075 32.1228 32.1228 32.2189 32.0266 32.5075 32.5075
## 2 40.9658 40.1625 39.9735 39.7373 40.5405 41.1075 40.0680
## 3 17.4562 17.3600 17.2639 17.2639 17.2158 17.4081 17.4081
## 4 27.9500 27.6201 27.6201 27.5729 27.5258 27.7143 27.9029
## 5 13.8152 13.7677 13.7202 13.7202 13.7677 13.7677 13.7677
## 6 17.3581 17.2162 17.3108 17.3581 17.4054 17.5473 17.4527
## 2024-12-05 2024-12-06 2024-12-09 2024-12-10 2024-12-11 2024-12-12 2024-12-13
## 1 32.3151 32.4113 32.2670 31.8823 32.0266 31.9304 31.5457
## 2 40.1153 40.3515 40.1625 39.9735 40.3515 40.4933 40.3988
## 3 17.4081 17.2158 17.2639 17.3120 17.3120 17.1196 17.0234
## 4 27.8557 27.6672 27.6201 27.5258 27.6201 27.2901 26.8188
## 5 13.7677 13.7677 13.8626 13.8626 13.8626 13.9576 13.8626
## 6 17.4054 17.5000 17.4527 17.5000 17.4527 17.4527 17.3581
## 2024-12-16 2024-12-17 2024-12-18 2024-12-19 2024-12-20 2024-12-23 2024-12-24
## 1 31.2091 31.1129 31.2572 30.9686 30.6320 31.0167 31.0167
## 2 39.3593 39.8790 39.9263 39.2648 39.1703 38.6978 38.4615
## 3 16.8792 16.7830 16.6868 16.3983 16.2059 16.4464 16.3983
## 4 26.2061 26.2061 26.2532 26.2061 26.1589 26.6303 26.3475
## 5 13.8152 13.7677 13.7677 13.6727 13.6253 13.7202 13.7677
## 6 17.1689 17.2162 17.1216 16.9797 16.9324 16.9797 16.9797
## 2024-12-25 2024-12-26 2024-12-27 2024-12-30 2024-12-31 2025-01-02 2025-01-03
## 1 31.0167 30.8725 30.6801 30.4878 30.4878 30.3916 30.2954
## 2 38.6505 38.7923 38.6033 38.3670 38.1780 38.0363 37.8945
## 3 16.4944 16.4944 16.3502 16.3983 16.3502 16.3502 16.1097
## 4 26.3475 27.4787 27.3373 27.2430 26.9602 26.6303 26.6303
## 5 13.7202 13.7202 13.7202 13.7202 13.7202 13.6727 13.5778
## 6 16.9797 16.9324 16.9324 16.8378 16.7432 16.6014 16.6014
## 2025-01-06 2025-01-07 2025-01-08 2025-01-09 2025-01-10 2025-01-13 2025-01-14
## 1 30.6320 30.2954 30.2473 29.6222 29.0932 29.3336 29.4298
## 2 38.1780 38.2725 37.6583 37.2803 37.0440 37.2803 37.6583
## 3 16.2540 16.0136 15.9655 15.8212 15.8212 15.4846 15.7731
## 4 26.7245 26.5831 26.7717 26.1118 25.9704 25.5462 25.9704
## 5 13.6727 13.6253 13.5303 13.4354 13.4354 13.3404 13.5303
## 6 16.7432 16.5068 16.4122 16.1284 16.1284 16.1757 16.1284
## 2025-01-15 2025-01-16 2025-01-17 2025-01-20 2025-01-21 2025-01-22 2025-02-03
## 1 29.6222 29.7664 30.4878 30.6320 30.5839 30.6320 30.5358
## 2 38.1308 38.6505 39.1230 38.8868 38.8395 38.8395 38.4615
## 3 15.9655 16.1097 16.2540 16.3983 16.5425 16.5906 16.5425
## 4 26.0647 26.1118 26.5831 26.4889 26.8188 26.8659 26.9131
## 5 13.5303 13.6253 13.5778 13.6253 13.6727 13.7202 13.6253
## 6 16.1757 16.2703 16.3176 16.3649 16.5068 16.4595 16.3176
## 2025-02-04 2025-02-05 2025-02-06 2025-02-07 2025-02-10 2025-02-11 2025-02-12
## 1 30.0550 29.9107 30.2954 30.0069 29.9588 30.2954 30.6801
## 2 38.5088 38.7450 38.6505 38.6033 37.8000 37.8000 37.8945
## 3 16.2059 16.3021 16.5425 16.4944 16.4464 16.4464 16.2540
## 4 26.4417 26.5360 26.6774 26.8188 26.4889 26.6303 26.8659
## 5 13.6253 13.7677 13.8152 13.8152 13.9101 13.9101 13.9576
## 6 16.2703 16.3649 16.4595 16.3649 16.3649 16.4122 16.4122
## 2025-02-13 2025-02-14 2025-02-17 2025-02-18 2025-02-19 2025-02-20 2025-02-21
## 1 32.1708 31.7861 32.2189 32.1708 31.7861 32.6517 32.8922
## 2 39.0758 38.9340 39.2175 39.1230 38.8395 39.2648 39.6900
## 3 16.6387 16.7349 16.6387 16.3502 16.3502 16.4944 16.5425
## 4 27.4787 27.7143 27.7615 27.5729 27.6672 27.8557 27.9500
## 5 14.1950 14.1475 14.1475 14.0525 14.0525 14.1000 14.1475
## 6 16.6014 16.7905 16.8378 16.8851 16.8378 16.9324 16.8851
## 2025-02-24 2025-02-25 2025-02-26 2025-02-27 2025-03-03 2025-03-04 2025-03-05
## 1 32.6998 33.6135 33.9020 33.7577 33.6616 33.2769 33.8058
## 2 39.7845 39.5955 39.7845 39.5010 41.6745 41.2965 41.1548
## 3 16.4944 16.7349 16.5906 15.7250 15.7731 15.8212 15.9174
## 4 27.9500 27.9971 28.2799 28.5627 28.9869 28.6570 28.7513
## 5 14.1475 14.2899 14.3374 14.2899 14.1950 14.2424 14.2424
## 6 16.8851 16.9324 16.8851 16.9797 16.9797 17.0743 17.0743
## 2025-03-06 2025-03-07 2025-03-10 2025-03-11 2025-03-12 2025-03-13 2025-03-14
## 1 33.4692 33.1807 33.4211 33.4692 33.1807 32.1228 32.5555
## 2 41.3438 41.5800 41.6273 41.7218 41.6745 41.6745 42.1470
## 3 16.0616 15.9174 15.9174 15.7250 15.7731 15.5327 15.7250
## 4 28.5627 28.2799 28.4213 28.4685 28.2799 28.1857 28.5156
## 5 14.2424 14.2424 14.2899 14.3848 14.4798 14.4798 14.4798
## 6 17.0270 16.9324 16.9797 16.8851 16.8378 16.8378 16.8851
## 2025-03-17 2025-03-18 2025-03-19 2025-03-20 2025-03-21 2025-03-24 2025-03-25
## 1 32.4113 32.3151 32.3632 32.4113 32.2189 31.8823 31.5457
## 2 41.8635 42.5250 42.5723 42.9975 42.6195 43.2338 43.3283
## 3 15.7250 15.8212 15.7250 15.7250 16.2540 15.7731 15.6769
## 4 28.4685 28.5156 28.6099 28.6099 28.3742 28.4685 28.5156
## 5 14.5273 14.1000 14.1950 14.3848 14.2899 14.2899 14.2424
## 6 16.9324 16.9797 17.0743 17.0270 16.8851 16.8378 16.9324
## 2025-03-26 2025-03-27 2025-03-28 2025-03-31 2025-04-01 2025-04-02 2025-04-07
## 1 31.7861 31.9785 31.5938 30.8244 31.1610 31.1610 28.6604
## 2 44.0370 44.5568 44.4150 44.1315 44.3678 44.6513 40.2098
## 3 15.6769 15.6769 15.4846 15.1960 15.3403 15.3884 13.8496
## 4 28.3742 28.3271 28.0914 27.5258 27.9029 27.9500 25.1691
## 5 14.2899 14.2424 14.0525 13.9576 14.1000 14.0051 12.6283
## 6 16.9324 16.8378 16.6959 16.5541 16.6486 16.6014 14.9459
## 2025-04-08 2025-04-09 2025-04-10 2025-04-11 2025-04-14 2025-04-15 2025-04-16
## 1 28.5162 27.4101 29.5741 28.8528 28.7566 28.7566 28.3719
## 2 41.4383 40.9185 42.6668 42.7613 42.8085 42.3833 41.6273
## 3 13.7053 13.0321 14.3304 14.2343 14.1862 14.3785 14.0900
## 4 25.2634 24.4150 26.5831 26.0175 25.6876 26.0647 25.6876
## 5 12.3434 12.1061 13.1505 13.1505 12.9131 13.1505 13.0556
## 6 15.0878 14.6622 15.7973 15.5608 15.4662 15.9392 15.9392
## 2025-04-17 2025-04-18 2025-04-21 2025-04-22 2025-04-23 2025-04-24 2025-04-25
## 1 28.2276 27.8429 27.6025 27.5063 28.1315 28.1315 28.2276
## 2 42.0525 42.0525 42.3833 41.8163 42.0053 42.0053 41.5328
## 3 14.1381 14.2343 14.0419 13.9938 14.1381 14.2343 14.3785
## 4 25.5933 25.8761 25.4048 25.4519 25.7819 25.9233 26.0175
## 5 13.1030 13.0556 13.0081 12.8657 12.9606 12.9606 13.0556
## 6 15.8446 15.7500 15.6554 15.4662 15.4662 15.4189 15.4189
## 2025-04-28 2025-04-29 2025-04-30 2025-05-02 2025-05-05 2025-05-06 2025-05-07
## 1 28.2276 28.3719 28.5162 28.8047 29.4298 29.3817 29.6703
## 2 42.1943 42.4305 42.5250 42.3833 43.0448 43.1393 43.1865
## 3 14.3304 14.4266 14.2824 14.5709 15.0037 15.2441 15.3403
## 4 26.3475 26.6303 26.3475 26.6774 26.8188 27.0073 27.0545
## 5 13.1030 13.2455 13.1505 13.8152 14.2899 14.2424 14.1475
## 6 15.6081 15.8446 15.8919 16.0338 16.4595 16.5541 16.6486
## 2025-05-08 2025-05-09 2025-05-12 2025-05-13 2025-05-14 2025-05-15 2025-05-16
## 1 29.3817 29.5741 30.0069 29.9107 29.1894 28.9489 28.9489
## 2 42.5723 43.0448 42.6195 42.7613 42.4305 41.8163 41.9580
## 3 15.1480 15.1960 15.1480 15.1960 15.1480 15.0999 15.1960
## 4 27.4315 27.6201 27.9971 27.4787 27.5729 27.4787 27.7615
## 5 14.1950 14.1950 14.3374 14.3848 14.5273 14.5273 14.4798
## 6 16.2703 16.3176 16.3176 16.2230 16.4122 16.2703 16.3176
## 2025-05-19 2025-05-20 2025-05-21 2025-05-22 2025-05-23 2025-05-26 2025-05-27
## 1 28.6604 28.9970 29.1413 28.8528 28.6123 28.7085 28.2276
## 2 41.7690 42.6195 42.3360 40.9658 40.5878 40.3515 39.6900
## 3 15.2441 15.2922 15.5327 15.3884 15.2922 15.0518 14.7632
## 4 27.7143 27.8557 28.0914 27.9971 28.7513 28.9869 29.0812
## 5 14.4798 14.5273 14.4798 14.5273 14.4798 14.4798 14.4323
## 6 16.2230 16.4595 16.5068 16.4122 16.3176 16.5541 16.3649
## 2025-05-28 2025-05-29 2025-06-02 2025-06-03 2025-06-04 2025-06-05 2025-06-06
## 1 27.9872 28.2276 26.6407 25.8232 25.9675 26.4484 26.4965
## 2 40.0208 39.5483 39.4065 39.5010 39.1230 39.3593 39.7845
## 3 14.5709 14.5709 14.1381 14.0900 14.2343 14.3785 14.5228
## 4 28.8455 29.1283 28.3271 28.4685 29.3169 28.7513 28.5156
## 5 14.4323 14.4798 14.3374 14.2899 14.3374 14.3374 14.2899
## 6 16.3176 16.4122 16.0811 15.8919 15.9865 16.1757 16.1284
## 2025-06-09 2025-06-10 2025-06-11 2025-06-12 2025-06-13 2025-06-16 2025-06-17
## 1 26.1598 26.1118 26.2560 26.0156 25.8713 25.7751 25.7271
## 2 39.8790 40.4460 40.7768 40.4460 41.0130 40.5405 40.4460
## 3 14.4747 14.4266 14.1381 14.1862 13.9457 13.9938 14.0419
## 4 28.3742 28.4685 28.3742 28.2799 28.1857 28.4213 28.4213
## 5 14.2424 14.1950 14.1950 14.1950 14.1475 14.1475 14.1000
## 6 16.1284 16.1284 16.1284 16.0338 15.9392 16.0811 16.0811
## 2025-06-18 2025-06-19 2025-06-20 2025-06-23 2025-06-24 2025-06-25 2025-06-26
## 1 25.4866 25.0538 24.5249 24.1402 24.7653 24.7172 24.8134
## 2 40.1153 39.5483 39.5483 39.5955 39.9735 39.9263 40.3043
## 3 13.9938 13.7534 13.5129 13.5129 13.8496 13.8976 14.0900
## 4 28.7041 28.1857 28.5627 27.9971 27.9500 27.9000 28.1500
## 5 14.1475 14.0051 13.9576 13.7677 14.1475 14.1000 14.1475
## 6 15.9865 15.8446 15.7973 15.7027 15.8919 15.9865 15.9392
## 2025-06-27 2025-06-30
## 1 25.1981 24.5249
## 2 40.8240 40.3043
## 3 14.1381 14.0900
## 4 28.1000 27.8500
## 5 14.1000 14.0525
## 6 15.9865 15.9392
na_counts_by_id <- data_spread %>%
# Treat each row (stock ID) as a separate group
rowwise() %>%
# Calculate the number of NA values across all date columns.
# The -id part tells it to check every column EXCEPT the 'id' column.
mutate(
NA_count = sum(is.na(c_across(-id)))
) %>%
# Filter to keep only stock IDs that have at least one NA value
filter(NA_count > 0) %>%
# Select the stock ID and its total NA count
select(id, NA_count) %>%
# Ungroup for clean output (optional, but good practice)
ungroup() %>%
# Arrange by the number of NA counts (descending)
arrange(desc(NA_count))
# Show the results
print(head(na_counts_by_id))
## # A tibble: 6 × 2
## id NA_count
## <chr> <int>
## 1 7799 217
## 2 7788 198
## 3 7780 196
## 4 4585 177
## 5 3716 160
## 6 7765 151
data_filled <- data_spread %>%
# Use mutate(across(...)) to apply na.locf to specific columns.
# We select all columns EXCEPT the 'id' column.
mutate(
across(-id, ~na.locf(., na.rm = FALSE))
)
head(data_filled)
## id 2024-01-02 2024-01-03 2024-01-04 2024-01-05 2024-01-08 2024-01-09
## 1 1101 32.3972 31.9304 31.9304 32.0704 31.9771 31.7903
## 2 1102 37.0392 36.5892 37.0392 36.9942 37.1742 36.9042
## 3 1103 17.6421 17.5017 17.5017 17.5485 17.5953 17.5485
## 4 1104 26.0628 25.8873 25.5363 25.7995 25.7118 25.3607
## 5 1108 14.0788 14.0343 14.0788 14.0788 14.1679 13.8115
## 6 1109 16.1807 16.0918 16.1362 16.1362 16.0918 16.0473
## 2024-01-10 2024-01-11 2024-01-12 2024-01-15 2024-01-16 2024-01-17 2024-01-18
## 1 31.4636 31.4636 31.5103 31.4169 30.7167 30.0631 29.9231
## 2 36.6342 36.7242 36.5892 36.5892 35.9591 35.4190 35.3740
## 3 17.4081 17.5017 17.5017 17.4081 16.9870 16.8934 16.9402
## 4 25.3607 25.5801 25.6240 25.4485 25.0975 24.7903 24.8781
## 5 13.6778 13.7670 13.8115 13.7670 13.5442 13.4551 13.4551
## 6 16.0473 16.0918 16.0473 16.0029 15.8250 15.6472 15.6917
## 2024-01-19 2024-01-22 2024-01-23 2024-01-24 2024-01-25 2024-01-26 2024-01-29
## 1 30.2498 30.1565 30.5299 30.4366 30.5299 30.4366 30.2032
## 2 35.4640 35.5990 35.7791 35.9141 36.0041 36.0941 35.9591
## 3 16.8466 16.9402 16.8934 16.8934 16.8934 16.9870 16.9870
## 4 24.8342 25.0536 25.1852 25.4485 25.4046 25.3169 25.6240
## 5 13.4551 13.4996 13.5887 13.6333 13.5887 13.6333 13.7224
## 6 15.7806 15.9584 15.9139 15.9139 15.8695 15.8695 15.8695
## 2024-01-30 2024-01-31 2024-02-01 2024-02-02 2024-02-05 2024-02-15 2024-02-16
## 1 29.8764 29.7830 30.2965 30.1098 30.0164 29.8764 29.7830
## 2 35.8241 35.7791 36.0491 36.0491 35.5090 35.2840 35.6440
## 3 16.8934 16.7062 16.9402 16.9870 16.7998 16.7998 16.7062
## 4 25.6240 25.5801 25.7118 25.6679 25.4924 25.3607 25.4485
## 5 13.5887 13.5442 13.6778 13.5887 13.4551 13.6333 13.6333
## 6 15.8695 15.7361 15.7806 15.7806 15.7806 15.6472 15.6917
## 2024-02-19 2024-02-20 2024-02-21 2024-02-22 2024-02-23 2024-02-26 2024-02-27
## 1 30.6233 30.4833 30.3432 30.3899 30.2498 30.0164 29.7363
## 2 36.1391 36.1841 36.2741 36.3641 36.1841 36.2291 36.0941
## 3 16.7530 16.8934 16.8934 16.9402 16.8466 16.9402 16.7062
## 4 25.5801 25.3607 25.4046 25.5363 25.6240 25.7995 25.6679
## 5 14.0343 13.8561 13.8561 13.8561 13.8115 13.9006 13.8561
## 6 15.7806 15.7361 15.7361 15.7361 15.7361 15.7806 15.6917
## 2024-02-29 2024-03-01 2024-03-04 2024-03-05 2024-03-06 2024-03-07 2024-03-08
## 1 29.9698 29.9231 29.9231 29.8764 29.8764 29.8764 29.7363
## 2 36.4091 36.1391 36.0491 36.0491 36.0041 36.3191 36.3641
## 3 16.5658 16.6594 16.5658 16.4254 16.4722 16.3318 16.1914
## 4 26.0628 26.1066 26.8964 27.1158 27.3791 27.4230 27.2474
## 5 14.0343 14.0788 14.1234 14.3907 14.4798 14.3461 13.9452
## 6 15.8695 15.8250 15.7806 15.9139 15.9139 15.7361 15.6472
## 2024-03-11 2024-03-12 2024-03-13 2024-03-14 2024-03-15 2024-03-18 2024-03-19
## 1 29.6897 29.8764 29.5963 29.8764 29.8297 29.5963 29.3162
## 2 36.3641 36.5441 36.3191 37.0392 36.9942 36.9942 36.4991
## 3 16.2382 16.1914 16.1446 16.2382 16.0978 16.0510 16.0510
## 4 27.8178 27.5985 27.5546 27.9495 27.5985 28.5199 28.3882
## 5 14.0788 14.1234 13.9452 14.4353 14.5244 13.8561 13.9006
## 6 15.6028 15.6472 15.6917 16.6696 16.5807 16.9808 16.9364
## 2024-03-20 2024-03-21 2024-03-22 2024-03-25 2024-03-26 2024-03-27 2024-03-28
## 1 28.8494 29.4096 29.0828 29.0361 29.4563 29.5963 29.5963
## 2 36.6342 37.2192 37.1292 37.1742 37.4442 37.0842 36.8592
## 3 16.0042 16.0978 16.0042 16.0042 16.1914 16.1914 16.2850
## 4 28.0372 28.3005 27.9056 27.5546 27.2913 27.4668 27.6423
## 5 13.8561 14.1234 14.0343 14.0788 14.2125 14.2125 14.1234
## 6 16.8030 16.8474 16.8474 16.9364 17.1142 17.1586 17.0697
## 2024-03-29 2024-04-01 2024-04-02 2024-04-03 2024-04-08 2024-04-09 2024-04-10
## 1 30.0164 30.0164 29.8297 29.8764 30.1565 30.2965 30.5299
## 2 37.1742 37.2192 36.9942 37.0392 37.5793 37.8043 38.2543
## 3 16.2850 16.3786 16.2382 16.3318 16.4254 16.5190 16.7062
## 4 27.7301 28.0811 27.9056 28.1250 28.7393 29.3535 28.9148
## 5 14.0788 14.2125 14.0788 14.1679 14.8808 14.6580 14.7026
## 6 17.0697 17.1586 17.2475 17.2920 17.4698 17.5587 17.7809
## 2024-04-11 2024-04-12 2024-04-15 2024-04-16 2024-04-17 2024-04-18 2024-04-19
## 1 30.2498 30.0631 30.2032 30.2965 30.4833 30.5299 29.8764
## 2 37.8493 37.6243 37.8493 37.2642 37.3092 37.9393 37.8043
## 3 16.6126 16.4254 16.4722 16.1914 16.3318 16.6594 16.4254
## 4 28.6076 28.6515 28.7393 28.2566 29.0903 29.2219 29.0464
## 5 14.5244 14.5689 14.5244 14.3907 14.6580 15.1927 14.8808
## 6 17.2031 17.2031 17.2920 17.0697 17.1142 17.2475 17.0253
## 2024-04-22 2024-04-23 2024-04-24 2024-04-25 2024-04-26 2024-04-29 2024-04-30
## 1 30.4366 30.2032 30.0164 29.8764 29.6897 30.2032 29.9231
## 2 38.4344 38.6144 38.2093 38.2543 38.4794 39.2445 39.3345
## 3 16.6594 16.7530 16.6594 16.7062 16.7530 17.5485 17.2209
## 4 29.3096 29.8800 29.5729 29.7045 29.5290 30.8453 30.8453
## 5 15.1036 15.0590 15.0590 15.0590 14.9699 15.3263 15.1036
## 6 17.2475 17.3364 17.3364 17.2475 17.2031 17.6476 17.7809
## 2024-05-02 2024-05-03 2024-05-06 2024-05-07 2024-05-08 2024-05-09 2024-05-10
## 1 30.1565 30.2498 30.8100 30.9968 30.9034 30.4833 31.0901
## 2 39.4245 39.5595 40.0545 39.7845 39.5145 39.1094 41.4047
## 3 17.6421 17.3613 17.3145 17.2209 17.1741 17.0338 17.1273
## 4 31.0208 30.9331 31.0647 30.4943 30.1433 30.3188 30.6698
## 5 15.3709 15.2372 15.2818 15.2372 15.0590 15.0590 15.3263
## 6 18.6255 18.4033 18.3588 18.0032 17.8699 17.6476 18.0032
## 2024-05-13 2024-05-14 2024-05-15 2024-05-16 2024-05-17 2024-05-20 2024-05-21
## 1 31.4636 31.1368 31.0901 31.3702 31.6503 31.6036 31.1368
## 2 41.2247 41.0447 41.0447 41.1797 40.1446 40.9096 40.1446
## 3 17.1741 17.1273 17.0806 17.3145 17.2677 17.3613 17.1273
## 4 29.0464 28.9148 29.0903 29.0464 29.2219 29.8800 29.5729
## 5 15.2818 14.9253 15.1481 15.1927 15.1927 15.2372 15.0590
## 6 17.8699 17.7365 17.6920 17.7809 17.7809 17.7365 17.5587
## 2024-05-22 2024-05-23 2024-05-24 2024-05-27 2024-05-28 2024-05-29 2024-05-30
## 1 32.0704 31.7437 31.7903 31.5569 31.6970 30.9968 30.6700
## 2 39.5145 39.5595 39.6045 39.2445 39.3345 38.5244 37.8043
## 3 17.2209 16.9402 16.8934 16.7998 16.9870 16.9402 16.7530
## 4 29.5290 29.0464 29.1780 29.3096 29.3974 29.3974 29.2219
## 5 15.0590 14.7917 14.8362 14.9253 15.0590 15.0144 14.9699
## 6 17.6031 17.1586 17.2475 17.2920 17.4253 17.2475 17.2920
## 2024-05-31 2024-06-03 2024-06-04 2024-06-05 2024-06-06 2024-06-07 2024-06-11
## 1 30.8100 30.8100 30.5299 30.5299 30.5299 30.8567 30.6700
## 2 37.3992 37.4442 37.0842 37.0842 36.5441 37.2642 37.2642
## 3 17.7825 17.4549 17.1273 17.2677 17.0806 17.2209 16.9870
## 4 29.8362 29.7045 29.6168 29.7484 29.5290 29.7045 29.4413
## 5 15.1036 15.0590 15.0144 15.0144 15.0144 15.0590 15.0590
## 6 17.3364 17.3809 17.2475 17.3364 17.1142 17.2475 17.2031
## 2024-06-12 2024-06-13 2024-06-14 2024-06-17 2024-06-18 2024-06-19 2024-06-20
## 1 30.8100 31.2302 31.7437 31.7437 31.6503 31.7903 31.8837
## 2 37.3092 37.4893 37.6243 37.7593 37.9393 38.8394 39.8745
## 3 17.0338 17.0338 17.1273 17.1273 17.1273 16.9870 17.1741
## 4 29.5290 29.4852 29.5290 29.5729 29.6168 29.7923 30.0556
## 5 15.0144 14.9699 15.0590 14.9699 14.9253 14.9253 14.9699
## 6 17.1586 17.2031 17.3364 17.2920 17.2031 17.2475 17.2920
## 2024-06-21 2024-06-24 2024-06-25 2024-06-26 2024-06-27 2024-06-28 2024-07-01
## 1 32.3505 31.9771 32.2105 32.1171 31.9771 31.9304 32.2670
## 2 40.0995 40.3696 39.2445 39.1544 39.4695 39.4695 39.7845
## 3 17.2209 17.1741 17.2677 17.1741 17.1273 17.1273 17.2677
## 4 30.4943 30.5382 30.6698 30.8453 31.1086 31.2841 31.8106
## 5 15.0590 15.0590 15.0144 14.9699 14.8808 14.9253 14.9253
## 6 17.3809 17.3364 17.4698 17.3809 17.3809 17.5142 17.6031
## 2024-07-02 2024-07-03 2024-07-04 2024-07-05 2024-07-08 2024-07-09 2024-07-10
## 1 32.4113 32.6998 32.8441 33.1326 33.2769 32.6998 32.5555
## 2 39.6495 41.0897 41.3597 40.2796 40.2346 40.0995 39.9645
## 3 17.2209 17.3145 17.4549 17.5485 17.5017 17.3613 17.4549
## 4 31.7667 32.2494 32.3810 32.2494 32.0300 31.6790 31.4157
## 5 14.9699 15.1036 15.1481 15.1481 15.1481 14.7917 14.7917
## 6 17.5142 17.6476 17.6031 17.6920 17.7809 17.6031 17.6920
## 2024-07-11 2024-07-12 2024-07-15 2024-07-16 2024-07-17 2024-07-18 2024-07-19
## 1 32.7479 32.9402 32.7960 32.8441 33.1807 33.4692 33.2769
## 2 40.5496 41.1347 40.9096 40.3246 39.7845 39.4538 38.6978
## 3 17.5017 17.5017 17.5017 17.3613 17.4081 17.5043 17.2639
## 4 31.8106 31.3719 31.3719 30.9665 31.3436 31.3907 30.7780
## 5 14.7026 14.7917 14.7917 14.8362 14.8808 14.8362 14.6135
## 6 17.5587 17.5587 17.6476 17.6920 17.7365 17.5473 17.3108
## 2024-07-22 2024-07-23 2024-07-26 2024-07-29 2024-07-30 2024-07-31 2024-08-01
## 1 33.0364 33.6135 33.5654 33.5654 33.3249 33.3249 33.2769
## 2 38.6505 38.9340 38.7923 39.3120 39.0285 39.1703 38.9340
## 3 17.1677 17.3120 17.2639 17.4562 17.4081 17.3120 17.5524
## 4 29.8353 30.3066 30.5423 31.6735 31.4850 31.4378 31.7678
## 5 14.3907 14.5689 14.4798 14.4353 14.4798 14.5689 14.6135
## 6 17.0270 17.0743 17.0743 17.1216 17.0270 17.1216 17.3108
## 2024-08-02 2024-08-05 2024-08-06 2024-08-07 2024-08-08 2024-08-09 2024-08-12
## 1 32.9883 31.8342 32.5075 32.5075 32.6998 33.1807 33.1326
## 2 39.1703 38.1308 39.4065 39.3120 40.0208 40.9185 41.1075
## 3 17.2639 16.5425 16.7349 16.7349 16.5425 16.8311 17.0234
## 4 30.9194 28.3742 28.6570 29.3640 29.0341 28.8455 29.3640
## 5 14.4353 13.5442 13.6778 13.9006 13.8115 14.1679 14.1679
## 6 17.0270 16.1284 16.3176 16.5541 16.5068 16.5541 16.5068
## 2024-08-13 2024-08-14 2024-08-15 2024-08-16 2024-08-19 2024-08-20 2024-08-21
## 1 33.2769 32.3151 31.6419 31.5938 31.4495 31.5457 31.5457
## 2 41.2020 41.7690 41.2965 41.7218 41.9108 42.2415 41.8163
## 3 17.2639 17.1196 17.0715 17.0715 17.1196 17.2639 17.2639
## 4 28.8455 29.1283 28.7984 28.8927 28.8927 28.6570 28.9869
## 5 14.1679 14.2570 14.2570 14.3016 14.3461 14.3461 14.4798
## 6 16.5068 17.1689 17.0743 17.2162 17.2635 17.3581 17.3581
## 2024-08-22 2024-08-23 2024-08-26 2024-08-27 2024-08-28 2024-08-29 2024-08-30
## 1 31.7861 31.6900 31.8823 31.7861 31.6419 31.6900 31.6900
## 2 42.3360 42.4778 42.5250 42.4305 42.4305 42.4305 42.6195
## 3 17.2158 17.2158 17.3600 17.4562 17.4562 17.4562 17.6486
## 4 28.7041 28.7041 28.6570 28.6570 28.6570 28.7041 29.1283
## 5 14.4798 14.4798 14.5244 14.4798 14.4323 14.3848 14.4323
## 6 17.3581 17.4054 17.4527 17.4054 17.3581 17.3581 17.3581
## 2024-09-02 2024-09-03 2024-09-04 2024-09-05 2024-09-06 2024-09-09 2024-09-10
## 1 31.4976 30.9206 30.5358 30.3435 30.5358 30.3435 30.2473
## 2 42.6668 42.6195 41.8635 42.2415 42.4778 42.2888 42.3833
## 3 17.6005 17.6005 16.8311 17.0234 16.8311 16.5906 16.7349
## 4 28.7984 28.4685 27.6201 27.5258 27.5729 27.4315 27.4787
## 5 14.3374 14.3374 13.8152 13.8626 13.8152 13.7202 13.8626
## 6 17.4054 17.3581 17.0743 17.0743 17.2162 17.0743 17.0743
## 2024-09-11 2024-09-12 2024-09-13 2024-09-16 2024-09-18 2024-09-19 2024-09-20
## 1 30.1031 30.2954 30.5839 30.9686 30.8725 30.9686 31.0648
## 2 42.0525 42.2415 42.4305 42.5250 42.3360 42.4778 42.5250
## 3 16.5425 16.8311 17.0234 17.2639 17.3120 17.4081 16.7830
## 4 27.4787 27.9029 28.0914 28.4685 28.6099 28.7041 28.8927
## 5 13.7677 13.8626 13.8626 14.0051 14.0525 14.1950 14.1000
## 6 16.9797 17.1216 17.4527 17.5473 17.4527 17.5473 17.5946
## 2024-09-23 2024-09-24 2024-09-25 2024-09-26 2024-09-27 2024-09-30 2024-10-01
## 1 30.9686 31.0167 31.4976 31.3533 32.0266 32.5075 32.4113
## 2 42.6195 42.7140 43.2810 43.6590 44.0843 45.1238 44.8403
## 3 16.8792 17.0234 17.3600 17.3120 17.5043 17.5043 17.4562
## 4 28.6570 28.7041 28.9869 28.8927 28.9869 29.1283 28.8455
## 5 14.0525 14.1000 14.1475 14.1475 14.2424 14.3848 14.4323
## 6 17.5000 17.4527 17.4527 17.5473 17.7838 17.8311 17.7838
## 2024-10-04 2024-10-07 2024-10-08 2024-10-09 2024-10-11 2024-10-14 2024-10-15
## 1 32.5075 32.2189 31.7861 31.2091 31.2572 31.3053 31.2572
## 2 45.0293 44.7458 44.7458 44.0370 43.4700 43.4700 43.9898
## 3 17.2158 17.2639 17.1677 17.1196 17.0234 16.9753 16.8792
## 4 28.6099 29.2226 28.8455 28.5627 28.4685 28.3742 28.3271
## 5 14.1950 14.1475 13.9576 13.8152 13.7677 13.7677 13.7677
## 6 17.6419 17.7365 17.5473 17.5473 17.4527 17.4054 17.2162
## 2024-10-16 2024-10-17 2024-10-18 2024-10-21 2024-10-22 2024-10-23 2024-10-24
## 1 30.9206 31.2572 31.4976 31.0648 30.9206 30.9686 30.7763
## 2 43.1865 44.4150 44.9820 44.4150 44.3678 43.7535 44.2733
## 3 16.8311 16.9272 17.1196 17.1196 17.0234 16.8792 16.8792
## 4 29.7882 29.0812 28.8927 28.8455 28.9398 28.7984 28.6099
## 5 13.8152 13.6727 13.8152 13.8152 13.8152 13.8626 13.7677
## 6 17.4054 17.4054 17.4054 17.5473 17.5946 17.5000 17.5473
## 2024-10-25 2024-10-28 2024-10-29 2024-10-30 2024-11-01 2024-11-04 2024-11-05
## 1 30.8244 30.9206 30.8725 30.7282 30.8725 31.2091 31.3053
## 2 44.1788 44.1788 44.0370 44.0843 43.7535 44.5095 44.9820
## 3 16.9753 17.0234 16.8792 16.9272 17.0234 16.8311 16.9753
## 4 28.6099 28.8927 28.5156 28.5156 29.4111 28.8927 28.9398
## 5 13.8152 14.2899 14.0051 13.9101 14.0051 13.7202 13.7202
## 6 17.5000 17.5000 17.5000 17.4527 17.5946 17.5000 17.5473
## 2024-11-06 2024-11-07 2024-11-08 2024-11-11 2024-11-12 2024-11-13 2024-11-14
## 1 30.8244 30.7282 30.6801 31.0167 31.2091 31.2091 31.1129
## 2 44.4623 44.7930 44.9348 43.8953 43.5173 43.2338 43.0920
## 3 17.0715 17.1677 17.2639 17.3600 17.0715 17.1677 16.9753
## 4 28.8455 28.7984 28.2799 28.1857 27.8557 27.6201 27.3373
## 5 13.8152 13.8626 13.8152 13.7677 13.6727 13.7202 13.6727
## 6 17.5473 17.5473 17.6419 17.6419 17.4527 17.3581 17.2635
## 2024-11-15 2024-11-18 2024-11-19 2024-11-20 2024-11-21 2024-11-22 2024-11-25
## 1 31.8823 31.9785 32.2189 32.2189 31.9304 32.0747 32.6036
## 2 43.6590 42.7613 41.5800 41.3438 41.2965 41.4855 41.4855
## 3 17.2639 17.3120 17.4081 17.3120 17.3120 17.3120 17.5524
## 4 27.5258 27.6672 27.6201 27.4787 27.5729 27.7615 28.0914
## 5 13.7677 13.8152 13.7202 13.7202 13.7677 13.8152 13.8152
## 6 17.3108 17.2635 17.3108 17.3108 17.3108 17.4527 17.3581
## 2024-11-26 2024-11-27 2024-11-28 2024-11-29 2024-12-02 2024-12-03 2024-12-04
## 1 32.5075 32.1228 32.1228 32.2189 32.0266 32.5075 32.5075
## 2 40.9658 40.1625 39.9735 39.7373 40.5405 41.1075 40.0680
## 3 17.4562 17.3600 17.2639 17.2639 17.2158 17.4081 17.4081
## 4 27.9500 27.6201 27.6201 27.5729 27.5258 27.7143 27.9029
## 5 13.8152 13.7677 13.7202 13.7202 13.7677 13.7677 13.7677
## 6 17.3581 17.2162 17.3108 17.3581 17.4054 17.5473 17.4527
## 2024-12-05 2024-12-06 2024-12-09 2024-12-10 2024-12-11 2024-12-12 2024-12-13
## 1 32.3151 32.4113 32.2670 31.8823 32.0266 31.9304 31.5457
## 2 40.1153 40.3515 40.1625 39.9735 40.3515 40.4933 40.3988
## 3 17.4081 17.2158 17.2639 17.3120 17.3120 17.1196 17.0234
## 4 27.8557 27.6672 27.6201 27.5258 27.6201 27.2901 26.8188
## 5 13.7677 13.7677 13.8626 13.8626 13.8626 13.9576 13.8626
## 6 17.4054 17.5000 17.4527 17.5000 17.4527 17.4527 17.3581
## 2024-12-16 2024-12-17 2024-12-18 2024-12-19 2024-12-20 2024-12-23 2024-12-24
## 1 31.2091 31.1129 31.2572 30.9686 30.6320 31.0167 31.0167
## 2 39.3593 39.8790 39.9263 39.2648 39.1703 38.6978 38.4615
## 3 16.8792 16.7830 16.6868 16.3983 16.2059 16.4464 16.3983
## 4 26.2061 26.2061 26.2532 26.2061 26.1589 26.6303 26.3475
## 5 13.8152 13.7677 13.7677 13.6727 13.6253 13.7202 13.7677
## 6 17.1689 17.2162 17.1216 16.9797 16.9324 16.9797 16.9797
## 2024-12-25 2024-12-26 2024-12-27 2024-12-30 2024-12-31 2025-01-02 2025-01-03
## 1 31.0167 30.8725 30.6801 30.4878 30.4878 30.3916 30.2954
## 2 38.6505 38.7923 38.6033 38.3670 38.1780 38.0363 37.8945
## 3 16.4944 16.4944 16.3502 16.3983 16.3502 16.3502 16.1097
## 4 26.3475 27.4787 27.3373 27.2430 26.9602 26.6303 26.6303
## 5 13.7202 13.7202 13.7202 13.7202 13.7202 13.6727 13.5778
## 6 16.9797 16.9324 16.9324 16.8378 16.7432 16.6014 16.6014
## 2025-01-06 2025-01-07 2025-01-08 2025-01-09 2025-01-10 2025-01-13 2025-01-14
## 1 30.6320 30.2954 30.2473 29.6222 29.0932 29.3336 29.4298
## 2 38.1780 38.2725 37.6583 37.2803 37.0440 37.2803 37.6583
## 3 16.2540 16.0136 15.9655 15.8212 15.8212 15.4846 15.7731
## 4 26.7245 26.5831 26.7717 26.1118 25.9704 25.5462 25.9704
## 5 13.6727 13.6253 13.5303 13.4354 13.4354 13.3404 13.5303
## 6 16.7432 16.5068 16.4122 16.1284 16.1284 16.1757 16.1284
## 2025-01-15 2025-01-16 2025-01-17 2025-01-20 2025-01-21 2025-01-22 2025-02-03
## 1 29.6222 29.7664 30.4878 30.6320 30.5839 30.6320 30.5358
## 2 38.1308 38.6505 39.1230 38.8868 38.8395 38.8395 38.4615
## 3 15.9655 16.1097 16.2540 16.3983 16.5425 16.5906 16.5425
## 4 26.0647 26.1118 26.5831 26.4889 26.8188 26.8659 26.9131
## 5 13.5303 13.6253 13.5778 13.6253 13.6727 13.7202 13.6253
## 6 16.1757 16.2703 16.3176 16.3649 16.5068 16.4595 16.3176
## 2025-02-04 2025-02-05 2025-02-06 2025-02-07 2025-02-10 2025-02-11 2025-02-12
## 1 30.0550 29.9107 30.2954 30.0069 29.9588 30.2954 30.6801
## 2 38.5088 38.7450 38.6505 38.6033 37.8000 37.8000 37.8945
## 3 16.2059 16.3021 16.5425 16.4944 16.4464 16.4464 16.2540
## 4 26.4417 26.5360 26.6774 26.8188 26.4889 26.6303 26.8659
## 5 13.6253 13.7677 13.8152 13.8152 13.9101 13.9101 13.9576
## 6 16.2703 16.3649 16.4595 16.3649 16.3649 16.4122 16.4122
## 2025-02-13 2025-02-14 2025-02-17 2025-02-18 2025-02-19 2025-02-20 2025-02-21
## 1 32.1708 31.7861 32.2189 32.1708 31.7861 32.6517 32.8922
## 2 39.0758 38.9340 39.2175 39.1230 38.8395 39.2648 39.6900
## 3 16.6387 16.7349 16.6387 16.3502 16.3502 16.4944 16.5425
## 4 27.4787 27.7143 27.7615 27.5729 27.6672 27.8557 27.9500
## 5 14.1950 14.1475 14.1475 14.0525 14.0525 14.1000 14.1475
## 6 16.6014 16.7905 16.8378 16.8851 16.8378 16.9324 16.8851
## 2025-02-24 2025-02-25 2025-02-26 2025-02-27 2025-03-03 2025-03-04 2025-03-05
## 1 32.6998 33.6135 33.9020 33.7577 33.6616 33.2769 33.8058
## 2 39.7845 39.5955 39.7845 39.5010 41.6745 41.2965 41.1548
## 3 16.4944 16.7349 16.5906 15.7250 15.7731 15.8212 15.9174
## 4 27.9500 27.9971 28.2799 28.5627 28.9869 28.6570 28.7513
## 5 14.1475 14.2899 14.3374 14.2899 14.1950 14.2424 14.2424
## 6 16.8851 16.9324 16.8851 16.9797 16.9797 17.0743 17.0743
## 2025-03-06 2025-03-07 2025-03-10 2025-03-11 2025-03-12 2025-03-13 2025-03-14
## 1 33.4692 33.1807 33.4211 33.4692 33.1807 32.1228 32.5555
## 2 41.3438 41.5800 41.6273 41.7218 41.6745 41.6745 42.1470
## 3 16.0616 15.9174 15.9174 15.7250 15.7731 15.5327 15.7250
## 4 28.5627 28.2799 28.4213 28.4685 28.2799 28.1857 28.5156
## 5 14.2424 14.2424 14.2899 14.3848 14.4798 14.4798 14.4798
## 6 17.0270 16.9324 16.9797 16.8851 16.8378 16.8378 16.8851
## 2025-03-17 2025-03-18 2025-03-19 2025-03-20 2025-03-21 2025-03-24 2025-03-25
## 1 32.4113 32.3151 32.3632 32.4113 32.2189 31.8823 31.5457
## 2 41.8635 42.5250 42.5723 42.9975 42.6195 43.2338 43.3283
## 3 15.7250 15.8212 15.7250 15.7250 16.2540 15.7731 15.6769
## 4 28.4685 28.5156 28.6099 28.6099 28.3742 28.4685 28.5156
## 5 14.5273 14.1000 14.1950 14.3848 14.2899 14.2899 14.2424
## 6 16.9324 16.9797 17.0743 17.0270 16.8851 16.8378 16.9324
## 2025-03-26 2025-03-27 2025-03-28 2025-03-31 2025-04-01 2025-04-02 2025-04-07
## 1 31.7861 31.9785 31.5938 30.8244 31.1610 31.1610 28.6604
## 2 44.0370 44.5568 44.4150 44.1315 44.3678 44.6513 40.2098
## 3 15.6769 15.6769 15.4846 15.1960 15.3403 15.3884 13.8496
## 4 28.3742 28.3271 28.0914 27.5258 27.9029 27.9500 25.1691
## 5 14.2899 14.2424 14.0525 13.9576 14.1000 14.0051 12.6283
## 6 16.9324 16.8378 16.6959 16.5541 16.6486 16.6014 14.9459
## 2025-04-08 2025-04-09 2025-04-10 2025-04-11 2025-04-14 2025-04-15 2025-04-16
## 1 28.5162 27.4101 29.5741 28.8528 28.7566 28.7566 28.3719
## 2 41.4383 40.9185 42.6668 42.7613 42.8085 42.3833 41.6273
## 3 13.7053 13.0321 14.3304 14.2343 14.1862 14.3785 14.0900
## 4 25.2634 24.4150 26.5831 26.0175 25.6876 26.0647 25.6876
## 5 12.3434 12.1061 13.1505 13.1505 12.9131 13.1505 13.0556
## 6 15.0878 14.6622 15.7973 15.5608 15.4662 15.9392 15.9392
## 2025-04-17 2025-04-18 2025-04-21 2025-04-22 2025-04-23 2025-04-24 2025-04-25
## 1 28.2276 27.8429 27.6025 27.5063 28.1315 28.1315 28.2276
## 2 42.0525 42.0525 42.3833 41.8163 42.0053 42.0053 41.5328
## 3 14.1381 14.2343 14.0419 13.9938 14.1381 14.2343 14.3785
## 4 25.5933 25.8761 25.4048 25.4519 25.7819 25.9233 26.0175
## 5 13.1030 13.0556 13.0081 12.8657 12.9606 12.9606 13.0556
## 6 15.8446 15.7500 15.6554 15.4662 15.4662 15.4189 15.4189
## 2025-04-28 2025-04-29 2025-04-30 2025-05-02 2025-05-05 2025-05-06 2025-05-07
## 1 28.2276 28.3719 28.5162 28.8047 29.4298 29.3817 29.6703
## 2 42.1943 42.4305 42.5250 42.3833 43.0448 43.1393 43.1865
## 3 14.3304 14.4266 14.2824 14.5709 15.0037 15.2441 15.3403
## 4 26.3475 26.6303 26.3475 26.6774 26.8188 27.0073 27.0545
## 5 13.1030 13.2455 13.1505 13.8152 14.2899 14.2424 14.1475
## 6 15.6081 15.8446 15.8919 16.0338 16.4595 16.5541 16.6486
## 2025-05-08 2025-05-09 2025-05-12 2025-05-13 2025-05-14 2025-05-15 2025-05-16
## 1 29.3817 29.5741 30.0069 29.9107 29.1894 28.9489 28.9489
## 2 42.5723 43.0448 42.6195 42.7613 42.4305 41.8163 41.9580
## 3 15.1480 15.1960 15.1480 15.1960 15.1480 15.0999 15.1960
## 4 27.4315 27.6201 27.9971 27.4787 27.5729 27.4787 27.7615
## 5 14.1950 14.1950 14.3374 14.3848 14.5273 14.5273 14.4798
## 6 16.2703 16.3176 16.3176 16.2230 16.4122 16.2703 16.3176
## 2025-05-19 2025-05-20 2025-05-21 2025-05-22 2025-05-23 2025-05-26 2025-05-27
## 1 28.6604 28.9970 29.1413 28.8528 28.6123 28.7085 28.2276
## 2 41.7690 42.6195 42.3360 40.9658 40.5878 40.3515 39.6900
## 3 15.2441 15.2922 15.5327 15.3884 15.2922 15.0518 14.7632
## 4 27.7143 27.8557 28.0914 27.9971 28.7513 28.9869 29.0812
## 5 14.4798 14.5273 14.4798 14.5273 14.4798 14.4798 14.4323
## 6 16.2230 16.4595 16.5068 16.4122 16.3176 16.5541 16.3649
## 2025-05-28 2025-05-29 2025-06-02 2025-06-03 2025-06-04 2025-06-05 2025-06-06
## 1 27.9872 28.2276 26.6407 25.8232 25.9675 26.4484 26.4965
## 2 40.0208 39.5483 39.4065 39.5010 39.1230 39.3593 39.7845
## 3 14.5709 14.5709 14.1381 14.0900 14.2343 14.3785 14.5228
## 4 28.8455 29.1283 28.3271 28.4685 29.3169 28.7513 28.5156
## 5 14.4323 14.4798 14.3374 14.2899 14.3374 14.3374 14.2899
## 6 16.3176 16.4122 16.0811 15.8919 15.9865 16.1757 16.1284
## 2025-06-09 2025-06-10 2025-06-11 2025-06-12 2025-06-13 2025-06-16 2025-06-17
## 1 26.1598 26.1118 26.2560 26.0156 25.8713 25.7751 25.7271
## 2 39.8790 40.4460 40.7768 40.4460 41.0130 40.5405 40.4460
## 3 14.4747 14.4266 14.1381 14.1862 13.9457 13.9938 14.0419
## 4 28.3742 28.4685 28.3742 28.2799 28.1857 28.4213 28.4213
## 5 14.2424 14.1950 14.1950 14.1950 14.1475 14.1475 14.1000
## 6 16.1284 16.1284 16.1284 16.0338 15.9392 16.0811 16.0811
## 2025-06-18 2025-06-19 2025-06-20 2025-06-23 2025-06-24 2025-06-25 2025-06-26
## 1 25.4866 25.0538 24.5249 24.1402 24.7653 24.7172 24.8134
## 2 40.1153 39.5483 39.5483 39.5955 39.9735 39.9263 40.3043
## 3 13.9938 13.7534 13.5129 13.5129 13.8496 13.8976 14.0900
## 4 28.7041 28.1857 28.5627 27.9971 27.9500 27.9000 28.1500
## 5 14.1475 14.0051 13.9576 13.7677 14.1475 14.1000 14.1475
## 6 15.9865 15.8446 15.7973 15.7027 15.8919 15.9865 15.9392
## 2025-06-27 2025-06-30
## 1 25.1981 24.5249
## 2 40.8240 40.3043
## 3 14.1381 14.0900
## 4 28.1000 27.8500
## 5 14.1000 14.0525
## 6 15.9865 15.9392
data_filtered <- data_spread %>%
tidyr::drop_na()
filtered_rows <- nrow(data_filtered)
filtered_cols <- ncol(data_filtered)
cat(filtered_rows, filtered_cols)
## 936 359
library(lubridate)
library(PerformanceAnalytics)
library(xts) # Add xts library explicitly if not already loaded
# Note: Ensure your 'data_filtered' tibble is available from Question 6
# -----------------------------------------------
# STEP 2: FIX - Convert to Time Series (xts)
# -----------------------------------------------
# 1. Prepare Data Matrix: Set 'id' as row names and transpose to get [Date x Stock] matrix
data_matrix <- data_filtered %>%
column_to_rownames(var = "id") %>%
t() %>%
as.matrix()
# 💡 CRITICAL FIX: Ensure the matrix is numeric before creating xts
# This solves issues where the transpose function coerces prices to character.
data_matrix_numeric <- apply(data_matrix, 2, as.numeric)
rownames(data_matrix_numeric) <- rownames(data_matrix) # Preserve the date rownames
# 2. Extract and Convert Index (Dates)
date_index <- rownames(data_matrix_numeric) %>% # Use the corrected matrix's rownames
ymd() %>%
as.Date()
# 3. Create the xts Object using the base xts() function
stock_xts <- xts(
x = data_matrix_numeric, # Use the confirmed numeric matrix
order.by = date_index
)
# -----------------------------------------------
# STEP 3 & 4 (Return Calculation and Display)
# -----------------------------------------------
# This explicit call should now work with the correct numeric input data.
returns_xts <- (stock_xts / stats::lag(stock_xts, k = 1)) - 1
# -----------------------------------------------
# STEP 4: Delete the first row and show results
# -----------------------------------------------
# The first row is always NA.
# We delete the first row using standard xts indexing [row_start:row_end, col_start:col_end].
returns_xts_clean <- returns_xts[2:nrow(returns_xts), 1:5] # Selects row 2 to end, and col 1 to 5
cat("## Filtered Daily Discrete Returns (xts) \n")
## ## Filtered Daily Discrete Returns (xts)
# Show the first 5 rows and first 5 columns (stocks)
print(returns_xts_clean[1:5, 1:5])
## 1101 1102 1103 1104 1108
## 2024-01-03 -0.014408653 -0.012149290 -0.007958236 -0.006733735 -0.003160781
## 2024-01-04 0.000000000 0.012298711 0.000000000 -0.013558772 0.003170803
## 2024-01-05 0.004384536 -0.001214929 0.002674026 0.010306896 0.000000000
## 2024-01-08 -0.002909225 0.004865628 0.002666895 -0.003399291 0.006328664
## 2024-01-09 -0.005841680 -0.007263102 -0.002659801 -0.013655209 -0.025155457
data_matrix <- data_filtered %>%
column_to_rownames(var = "id") %>%
t() %>%
as.matrix()
# 2. Extract and Convert Index (Dates)
date_index <- rownames(data_matrix) %>%
ymd() %>%
as.Date()
# 3. Create the daily xts Object
stock_xts_daily <- xts(
x = data_matrix,
order.by = date_index
)
# --- 3. Compute Monthly Returns ---
# A. Convert Daily Prices to Monthly Prices (uses the last day's closing price)
stock_xts_monthly_prices <- to.monthly(stock_xts_daily, indexAt = "lastof")[, -1] # Remove Open, High, Low columns left by to.monthly
# B. Calculate Discrete Monthly Returns
# FIX: Use stats::lag() to calculate returns correctly.
returns_xts_monthly <- (stock_xts_monthly_prices / stats::lag(stock_xts_monthly_prices, k = 1)) - 1
# C. Delete the first row (NA)
returns_xts_clean_monthly <- returns_xts_monthly[2:nrow(returns_xts_monthly), ]
# --- 4. Show Results ---
cat("## Filtered Monthly Discrete Returns (xts) \n")
## ## Filtered Monthly Discrete Returns (xts)
# Show the first 5 rows (months) and first 5 columns (stocks)
print(returns_xts_clean_monthly)
## stock_xts_daily.High stock_xts_daily.Low stock_xts_daily.Close
## 2024-02-29 -0.020581479 -0.008404066 0.018870137
## 2024-03-31 0.028429706 -0.033901170 0.063972405
## 2024-04-30 0.050483119 0.011696930 0.112340020
## 2024-05-31 0.052630642 0.034685080 -0.032714871
## 2024-06-30 -0.024999577 0.013967648 0.048528298
## 2024-07-31 0.024525881 0.010637546 0.004913039
## 2024-08-31 0.030459602 -0.036417225 -0.073462520
## 2024-09-30 0.058759488 0.000000000 0.000000000
## 2024-10-31 -0.002094239 0.017445972 -0.021034527
## 2024-11-30 -0.001050427 0.000000000 -0.033059097
## 2024-12-31 -0.086134454 -0.037145522 -0.022221094
## 2025-01-31 -0.048275862 -0.044508481 -0.003497749
## 2025-02-28 0.016908213 0.015525102 0.063158130
## 2025-03-31 0.119953751 -0.033640700 -0.036302590
## 2025-04-30 0.002120888 -0.142399316 -0.042807112
## 2025-05-31 -0.032805316 0.118077670 0.105543220
## 2025-06-30 -0.050328228 -0.072610477 -0.043885156
raw_data <- read_delim(
file = "/Users/Frezza/Downloads/tej_day_price_2024_20250630.txt",
delim = "\t",
) %>%
# Convert Date to Date object
mutate(Date = ymd(Date)) %>%
# Rename the Market Cap column for easier use
rename(Market_Cap = `Market Cap.(NTD MN)`)
## Rows: 337347 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): CO_ID, TSE Sector, English Short Name
## dbl (9): Date, TSE ID, Open(NTD), High(NTD), Low(NTD), Close(NTD), Volume(10...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# --- 2. Define Year-End Dates ---
# Last available date in 2024 (Find Max Date where year is 2024)
end_2024_date <- raw_data %>%
filter(year(Date) == 2024) %>%
pull(Date) %>%
max()
# Last available date in 2025 (Last date in file, which is 2025-06-30)
end_2025_date <- raw_data %>%
filter(year(Date) == 2025) %>%
pull(Date) %>%
max()
# --- 3. Filter and Find Top 20 for Each Period ---
# A. Top 20 at 2024 Year-End
top_20_2024 <- raw_data %>%
filter(Date == end_2024_date) %>%
arrange(desc(Market_Cap)) %>%
slice(1:20) %>%
select(Date, `TSE ID`, `English Short Name`, Market_Cap) %>%
mutate(Rank = row_number()) %>%
rename(`2024 Rank` = Rank, `2024 Market Cap (NTD MN)` = Market_Cap)
# B. Top 20 at 2025 Year-End (June 30)
top_20_2025 <- raw_data %>%
filter(Date == end_2025_date) %>%
arrange(desc(Market_Cap)) %>%
slice(1:20) %>%
select(Date, `TSE ID`, `English Short Name`, Market_Cap) %>%
mutate(Rank = row_number()) %>%
rename(`2025 Rank` = Rank, `2025 Market Cap (NTD MN)` = Market_Cap)
# --- 4. Merge Results for Combined View ---
# Combine the two lists to show ranking changes
top_20_combined <- full_join(
top_20_2024 %>% select(`TSE ID`, `English Short Name`, `2024 Rank`, `2024 Market Cap (NTD MN)`),
top_20_2025 %>% select(`TSE ID`, `English Short Name`, `2025 Rank`, `2025 Market Cap (NTD MN)`),
by = c("TSE ID", "English Short Name")
) %>%
# Arrange by the most recent rank (2025)
arrange(`2025 Rank`) %>%
# Fill NA ranks with the maximum rank to keep unranked firms at the bottom
mutate(`2024 Rank` = replace_na(`2024 Rank`, max(`2024 Rank`, na.rm = TRUE) + 1))
print(top_20_combined)
## # A tibble: 20 × 6
## `TSE ID` `English Short Name` `2024 Rank` 2024 Market Cap (NTD …¹ `2025 Rank`
## <dbl> <chr> <int> <dbl> <int>
## 1 2330 TSMC 1 27877688 1
## 2 2317 Hon Hai 2 2556073 2
## 3 2454 MediaTek 3 2266389 3
## 4 2881 Fubon Financial 4 1234015 4
## 5 2308 DELTA 5 1118242 5
## 6 2382 QCI 6 1108574 6
## 7 2412 CHT 8 958045 7
## 8 2882 CATHAY FHC 7 1001907 8
## 9 2891 CTBC Holding 9 767154 9
## 10 3711 ASEH 10 715219 10
## 11 2886 Mega FHC 11 574052 11
## 12 2303 UMC 12 540739 12
## 13 2884 E.S.F.H 19 431087 13
## 14 2357 Asustek 16 457540 14
## 15 6669 Wiwynn 14 486903 15
## 16 1216 Uni-President 15 459675 16
## 17 2885 Yuanta Group 17 440057 17
## 18 2603 EMC 13 487135 18
## 19 3045 TWM 20 422590 19
## 20 2345 Accton 18 433744 20
## # ℹ abbreviated name: ¹`2024 Market Cap (NTD MN)`
## # ℹ 1 more variable: `2025 Market Cap (NTD MN)` <dbl>