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read_tick_data.R
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if(!require('BBmisc')) install.packages('BBmisc')
suppressPackageStartupMessages(require(BBmisc))
pkgs <- c('data.table', 'plyr', 'dplyr', 'magrittr', 'purrr',
'tidyr', 'stringr', 'lubridate')
lib(pkgs)
rm(pkgs)
## --------------------- Read Data -------------------------------
#dfm <- read.csv(paste0(dr, fls), skipNul = TRUE)
# start <- seq(1, 186, 31)
# stop <- start - 1
# stop <- c(stop[-1], length(fls))
# paste0('fls = fls[', start, ':', stop, ']')
llply(names(cr_code), function(x) {
dr <- paste0('data/', x, '/')
fls <- dir(dr, pattern = '.csv$')
for(i in seq(length(fls))) {
nm <- str_replace_all(fls, '.csv', '')
if(!file.exists(paste0(dr, nm[i], '.rds'))) {
assign(nm[i], read.csv(paste0(dr, fls[i]), skipNul = TRUE) %>% tbl_df)
## save dataset.
eval(parse(text = paste0("saveRDS(", nm[i], ", '", dr, nm[i], ".rds')")))
eval(parse(text = paste0("rm(", nm[i], ")")))
cat(paste0(dr, nm[i], '.rds saved!\n'))
}
}; rm(i, fls, nm)
})
## --------------------- Check Files -------------------------------
## check the number of *.rds files in directory.
drt <- dir(dr, pattern = '[^_HL].rds')
# start <- seq(1, 186, 31)
# stop <- start - 1
# stop <- c(stop[-1], length(drt))
# paste0('drt = drt[', start, ':', stop, ']')
##
drt %>% str_split_fixed('Y|W|.rds', 4) %>% tbl_df %>%
select(V2, V3) %>% filter(V2 == 2015)
## If the downloaded files are display in Asia/Tokyo timezone, then
## we can change to default UTC timezone.
#'@ Y2015W2 %>% mutate(DateTime = mdy_hms(DateTime, tz = 'Asia/Tokyo'),
#'@ DateTime = with_tz(DateTime, 'UTC'))
## --------------------- Filter Data -------------------------------
dr <- 'data/USDJPY/'
drt <- dir(dr, pattern = '[^_HL].rds')
nm <- str_replace_all(drt, '.rds', '')
# start <- seq(1, 186, 31)
# stop <- start - 1
# stop <- c(stop[-1], length(drt))
# data.frame(drt = paste0('drt = drt[', start, ':', stop, ']'), colm = ';',
# nm = paste0('nm = nm[', start, ':', stop, ']'))
for(i in seq(length(drt))) {
assign(nm[i], readRDS(paste0(dr, drt[i])))
## filter daily highest and lowest price.
#'@ assign(paste0(nm[i], '_HL'), nm[i] %>% mutate(Date = as.Date(mdy_hms(DateTime))) %>%
#'@ group_by(Date) %>%
#'@ filter(Bid == min(Bid)|Bid == max(Bid)|Ask == min(Ask)|Ask == max(Ask)) %>%
#'@ filter(!duplicated(Bid)|!duplicated(Ask)))
## Error : assign() cannot handle nm[i] %>% mutate(...) since 'nm[i]' is class character.
## convert timezone, will take time around 20 minutes for 1 million plus rows due to not vectorised handling.
#'@ eval(parse(text = paste0(
#'@ nm[i], "_HL <- ", nm[i], " %>% mutate(DateTime = mdy_hms(DateTime, tz = 'UTC')) %>% rowwise() %>% do(DateTime = with_tz(.$DateTime, tzone = 'GMT')) %>% mutate(Date = as.Date(DateTime))")))
## filter daily highest and lowest price.
eval(parse(text = paste0(
nm[i], "_HL <- ", nm[i], " %>% mutate(DateTime = with_tz(mdy_hms(DateTime), 'GMT'), Date = as.Date(DateTime)) %>% group_by(Date) %>% filter(Bid == min(Bid)|Bid == max(Bid)|Ask == min(Ask)|Ask == max(Ask))")))
eval(parse(text = paste0(nm[i], '_HL %<>% filter(!duplicated(Bid)|!duplicated(Ask))')))
## save dataset.
eval(parse(text = paste0(
"saveRDS(", nm[i], "_HL, '", dr, nm[i], "_HL.rds')")))
eval(parse(text = paste0("rm(", nm[i], ")")))
eval(parse(text = paste0("rm(", nm[i], "_HL)")))
cat(paste0(dr, nm[i], '_HL.rds saved!\n'))
}
test <- Y2018W9
test %<>% mutate(DateTime = mdy_hms(DateTime, tz = 'UTC')) %>% rowwise() %>%
do(DateTime = with_tz(.$DateTime, tzone = 'GMT')) %>%
mutate(Date = as.Date(DateTime))
## -----------------------------------------------------------------
## convert timezone, will take time around 20 minutes for 1 million plus rows due to not vectorised handling.
eval(parse(text = paste0(
nm[i], "_HL <- ", nm[i], " %>% mutate(DateTime = mdy_hms(DateTime, tz = 'UTC')) %>% rowwise() %>% do(DateTime = with_tz(.$DateTime, tzone = 'GMT')) %>% mutate(Date = as.Date(DateTime))")))
## check the number of *.rds files in directory.
dr <- 'data/USDJPY/'
drt <- dir(dr, pattern = '_HL.rds')
#'@ drt %>% str_split_fixed('Y|W|_HL.rds', 4) %>% tbl_df %>%
#'@ select(V2, V3) %>% filter(V2 == 2015)
nm <- str_replace_all(drt, '.rds', '')
## filter_HL() to get daily unique high low price.
filter_HL <- function(mbase) {
## filter to be unique min bid, max bid and also min ask, max ask price.
B.Min <- ddply(mbase, .(Date), summarise,
Bid = min(Bid))
B.Min.Date <- ddply(mbase, .(Date), summarise,
DateTime = DateTime[which.min(Bid)])
B.Min <- join(B.Min, B.Min.Date, by = 'Date')
rm(B.Min.Date)
B.Max <- ddply(mbase, .(Date), summarise,
Bid = max(Bid))
B.Max.Date <- ddply(mbase, .(Date), summarise,
DateTime = DateTime[which.max(Bid)])
B.Max <- join(B.Max, B.Max.Date, by = 'Date')
rm(B.Max.Date)
A.Min <- ddply(mbase, .(Date), summarise,
Ask = min(Ask))
A.Min.Date <- ddply(mbase, .(Date), summarise,
DateTime = DateTime[which.min(Ask)])
A.Min <- join(A.Min, A.Min.Date, by = 'Date')
rm(A.Min.Date)
A.Max <- ddply(mbase, .(Date), summarise,
Ask = max(Ask))
A.Max.Date <- ddply(mbase, .(Date), summarise,
DateTime = DateTime[which.max(Ask)])
A.Max <- join(A.Max, A.Max.Date, by = 'Date')
rm(A.Max.Date)
res <- bind_rows(list(B.Min, B.Max, A.Min, A.Max)) %>% arrange(DateTime)
rm(B.Min, B.Max, A.Min, A.Max)
return(res)
}
## simulate secondary filter daily high-low price.
for(i in seq(length(drt))) {
assign(nm[i], readRDS(paste0(dr, drt[i])))
assign(nm[i], eval(parse(text = paste0('filter_HL(', nm[i], ')'))))
## save dataset.
eval(parse(text = paste0(
"saveRDS(", nm[i], ", '", dr, nm[i], ".rds')")))
eval(parse(text = paste0("rm(", nm[i], ")")))
cat(paste0(dr, nm[i], '.rds saved!\n'))
}
read_HL_tick_data <- function(dr = 'data/USDJPY/', df.type = 'data.table') {
if(!require('BBmisc')) install.packages('BBmisc')
suppressPackageStartupMessages(require(BBmisc))
pkgs <- c('data.table', 'plyr', 'dplyr', 'magrittr', 'purrr',
'tidyr', 'stringr', 'lubridate')
lib(pkgs)
rm(pkgs)
#'@ dr <- 'data/USDJPY/'
## unzip dataset.
if(file.exists(paste0(dr, 'USDJPY.zip'))) {
unzip(paste0(dr, 'USDJPY.zip'), exdir = dr)
}
res <- ldply(paste0(dr, dir(dr, pattern = '_HL.rds')), readRDS)
if(df.type == 'data.table') {
res %<>% data.table
} else if(df.type == 'tbl_df') {
res %<>% tbl_df
} else {
stop("Kindly choose df.type = 'data.table' or df.type = 'tbl_df'.")
}
file.remove(paste0(dr, dir(dr, pattern = '_HL.rds')))
return(res)
}