tqk

Lifecycle: experimental R-CMD-check runiverse-name runiverse-package Codecov test coverage

Installation

# CRAN version NOT YET!!!
install.packages("tqk")

# Dev version
install.packages('tqk', repos = c(
                     'https://mrchypark.r-universe.dev',
                     'https://cloud.r-project.org'
                 ))

How to use

Stock code

code_get() function provide stock code.

code_get()
## # A tibble: 2,641 × 6
##    market name           code   name_full              name_eng        code_full
##    <chr>  <chr>          <chr>  <chr>                  <chr>           <chr>    
##  1 KOSDAQ 마이크로컨텍솔 098120 (주)마이크로컨텍솔루션 Micro Contact … KR709812…
##  2 KOSDAQ 스카이이앤엠   131100 (주)스카이이앤엠       SKY E&M Co., L… KR713110…
##  3 KOSDAQ 포스코엠텍     009520 (주)포스코엠텍         POSCO M-TECH C… KR700952…
##  4 KOSPI  AJ네트웍스     095570 AJ네트웍스보통주       AJ Networks Co… KR709557…
##  5 KOSPI  AK홀딩스       006840 AK홀딩스보통주         AK Holdings, I… KR700684…
##  6 KOSPI  BGF리테일      282330 BGF리테일보통주        BGF Retail      KR728233…
##  7 KOSPI  BGF            027410 BGF보통주              BGF             KR702741…
##  8 KOSPI  BNK금융지주    138930 BNK금융지주보통주      BNK Financial … KR713893…
##  9 KOSPI  BYC우          001465 BYC1우선주             BYC(1P)         KR700146…
## 10 KOSPI  BYC            001460 BYC보통주              BYC             KR700146…
## # ℹ 2,631 more rows

If want to get current version of stock code, add fresh = TRUE.

code_get(fresh = TRUE)
## # A tibble: 2,746 × 6
##    market name           code   name_full              name_eng        code_full
##    <chr>  <chr>          <chr>  <chr>                  <chr>           <chr>    
##  1 KOSDAQ 마이크로컨텍솔 098120 (주)마이크로컨텍솔루션 Micro Contact … KR709812…
##  2 KOSDAQ 포스코엠텍     009520 (주)포스코엠텍         POSCO M-TECH C… KR700952…
##  3 KOSPI  AJ네트웍스     095570 AJ네트웍스보통주       AJ Networks Co… KR709557…
##  4 KOSPI  AK홀딩스       006840 AK홀딩스보통주         AK Holdings, I… KR700684…
##  5 KOSPI  BGF리테일      282330 BGF리테일보통주        BGF Retail      KR728233…
##  6 KOSPI  BGF            027410 BGF보통주              BGF             KR702741…
##  7 KOSPI  BNK금융지주    138930 BNK금융지주보통주      BNK Financial … KR713893…
##  8 KOSPI  BYC우          001465 BYC1우선주             BYC(1P)         KR700146…
##  9 KOSPI  BYC            001460 BYC보통주              BYC             KR700146…
## 10 KOSPI  CJ우           001045 CJ1우선주              CJ(1P)          KR700104…
## # ℹ 2,736 more rows

Stock data

tqk_get() function provide stock data. First parameter named x is korean stock code like “005930” is samsung. Result of code_get() function has code column for x parameter on tqk_get() function.

code_get() %>% 
  dplyr::filter("삼성전자" == name) %>%
  dplyr::pull(code) %>% 
  tqk_get(from = "2018-05-01") -> ss
ss
## # A tibble: 1,313 × 6
##    date        open  high   low close   volume
##    <date>     <int> <int> <int> <int>    <int>
##  1 2023-08-28 66800 67000 66500 66500  4010121
##  2 2023-08-25 67100 67400 66900 67100  7032462
##  3 2023-08-24 68300 68700 67900 68200 15044463
##  4 2023-08-23 66700 67100 66400 67100  9549352
##  5 2023-08-22 67200 67700 66300 66600 10500242
##  6 2023-08-21 66600 67100 66300 66600  9720067
##  7 2023-08-18 66000 66700 65800 66300 11745006
##  8 2023-08-17 66300 66800 66000 66700 10778652
##  9 2023-08-16 66700 67100 66300 67000 13174578
## 10 2023-08-14 67500 67900 66900 67300  9352343
## # ℹ 1,303 more rows

Built-in dataset

{tqk} has built-in dataset called SHANK that is data to 2017-09-07 with Samsung Elect, Hyundai Motor, Amore pacific, Naver, Kakao.

SHANK %>%
  dplyr::distinct(symbol)
## # A tibble: 5 × 1
##   symbol
##   <chr> 
## 1 SS    
## 2 HYD   
## 3 AMP   
## 4 NVR   
## 5 KKO
SHANK
## # A tibble: 22,259 × 7
##    symbol date          open    high     low   close volume
##    <chr>  <date>       <int>   <int>   <int>   <int>  <int>
##  1 SS     2017-09-07 2350000 2411000 2350000 2406000 193530
##  2 SS     2017-09-06 2338000 2359000 2335000 2350000 216221
##  3 SS     2017-09-05 2312000 2345000 2298000 2338000 234322
##  4 SS     2017-09-04 2289000 2318000 2275000 2302000 158870
##  5 SS     2017-09-01 2323000 2332000 2315000 2324000 212834
##  6 SS     2017-08-31 2311000 2332000 2300000 2316000 220234
##  7 SS     2017-08-30 2319000 2320000 2298000 2310000 150260
##  8 SS     2017-08-29 2282000 2304000 2258000 2304000 252473
##  9 SS     2017-08-28 2351000 2362000 2298000 2305000 199242
## 10 SS     2017-08-25 2394000 2394000 2336000 2351000 224871
## # ℹ 22,249 more rows