Following on from my previous post about
downloading historical data, this post shows how previously downloaded data may be updated and appended with new, more recent data without having to re-download all the old data all over again.
The main function to do this, HisPricesDates, downloads data between given dates as function inputs and is shown below.
HisPricesDates = function( Granularity, DayAlign, TimeAlign, AccountToken, Instrument, Start, End ){
% a typical Oanda API call might look like
% https://api-fxtrade.oanda.com/v1/candles?instrument=EUR_USD&granularity=D&start=2014-03-21&end=2014-04-21&candleFormat=midpoint&includeFirst=false
% which is slowly built up by using the R paste function, commented at end of each line below
httpaccount = "https://api-fxtrade.oanda.com"
auth = c(Authorization = paste("Bearer",AccountToken,sep=" "))
QueryHistPrec = paste(httpaccount,"/v1/candles?instrument=",sep="") % https://api-fxtrade.oanda.com/v1/candles?instrument=
QueryHistPrec1 = paste(QueryHistPrec,Instrument,sep="") % https://api-fxtrade.oanda.com/v1/candles?instrument=EUR_USD
qstart = paste("start=",Start,sep="") % start=2014-03-21
qend = paste("end=",End,sep="") % end=2014-04-21
qcandleFormat = "candleFormat=midpoint" % candleFormat=midpoint
qgranularity = paste("granularity=",Granularity,sep="") % granularity=D
qdailyalignment = paste("dailyAlignment=",DayAlign,sep="") % dailyAlignment=0
qincludeFirst = "includeFirst=false" % includeFirst=false
QueryHistPrec2 = paste(QueryHistPrec1,qgranularity,qstart,qend,qcandleFormat,qincludeFirst,qdailyalignment,sep="&")
InstHistP = getURL(QueryHistPrec2,cainfo=system.file("CurlSSL","cacert.pem",package="RCurl"),httpheader=auth)
InstHistPjson = fromJSON(InstHistP, simplifyDataFrame = TRUE)
Prices = data.frame(InstHistPjson[[3]])
Prices$time = paste(substr(Prices$time,1,10),substr(Prices$time,12,19), sep=" ")
colnames(Prices) = c("TimeStamp","Open","High","Low","Close","TickVolume","Complete")
Prices$TimeStamp = as.POSIXct(strptime(Prices$TimeStamp, "%Y-%m-%d %H:%M:%OS"),origin="1970-01-01",tz = "UTC")
attributes(Prices$TimeStamp)$tzone = TimeAlign
return(Prices)
}
This function is called by two
R scripts, one for downloading daily data and one for intraday data.
The daily update script, which is shown next,
% cd to the daily data directory
setwd("~/Documents/octave/oanda_data/daily")
all_current_historical_data_list = read.table("instrument_daily_update_file",header=FALSE,sep="",colClasses=c("character","Date","numeric") )
for( ii in 1 : nrow( all_current_historical_data_list ) ) {
instrument = all_current_historical_data_list[ ii , 1 ]
% read second column of dates in all_current_historical_data_list as a date index
date_ix = as.Date( all_current_historical_data_list[ ii , 2 ] )
todays_date = as.Date( Sys.time() )
% download the missing historical data from date_ix to todays_date, if and only if, date_ix != todays_date
if( date_ix + 1 != todays_date ) {
new_historical_data = HisPricesDates( Granularity = "D", DayAlign, TimeAlign, AccountToken, instrument,
date_ix , todays_date )
% the new_historical_data might only try to add incomplete OHLC data, in which case do not actually
% want to update, so only update if we will be adding new, complete OHLC information
if ( nrow( new_historical_data ) >= 2 & new_historical_data[ 2 , 7 ] == TRUE ) {
% now do some data manipulation
% expect date of last line in Instrument_update_file == date of first line in new_historical_data
if ( date_ix == as.Date( new_historical_data[ 1 , 1 ] ) ) { % this is the case if true
new_historical_data = new_historical_data[ -1 , ] % so delete first row of new_historical_data
}
% similarly, expect last line of new_historical_data to be an incomplete OHLC bar
if ( new_historical_data[ nrow( new_historical_data) , 7 ] == FALSE) { % if so,
new_historical_data = new_historical_data[ -nrow( new_historical_data) , ] % delete this last line
}
% append new_historical_data to the relevant raw data file
write.table( new_historical_data , file = paste( instrument , "raw_OHLC_daily" , sep = "_" ) , row.names = FALSE , na = "" ,
col.names = FALSE , sep = "," , append = TRUE )
added_data_length = nrow( new_historical_data )
new_last_date = as.Date( new_historical_data[ added_data_length , 1 ] )
% and amend Instrument_update file with lastest update information
all_current_historical_data_list[ ii , 2 ] = new_last_date
all_current_historical_data_list[ ii , 3 ] = all_current_historical_data_list[ ii , 3 ] + added_data_length
} % end of download if statement
} % end of ( date_ix != todays_date ) if statement
} % end of for all_current_historical_data_list loop
% Write updated Instrument_update_file to file
write.table( all_current_historical_data_list , file = "instrument_daily_update_file" , row.names = FALSE , col.names = FALSE , na = "" )
has if statements as control structures to check that there is likely to be new daily data to actually download. It does this by checking a last_update date contained in an "instrument_daily_update_file" and comparing this with the current OS system time. If there is likely to be new data, the script runs and then updates this "instrument_daily_update_file." If not, the script exits with nothing having been done.
The intraday update script doe not have the checks the daily script has because I assume there will always be some new intraday data available for download. In this case, the last_update date is read from the "instrument_update_file" purely to act as an input to the above HisPricesDates function. As a result, this script involves some data manipulation to ensure that duplicate data is not printed to file. This script is shown next and is heavily commented to explain what is happening.
% cd to the hourly data directory
setwd("~/Documents/octave/oanda_data")
all_current_historical_data_list = read.table("instrument_hourly_update_file",header=FALSE,sep="",colClasses=c("character","Date","numeric") )
for( ii in 1 : nrow( all_current_historical_data_list ) ) {
instrument = all_current_historical_data_list[ ii , 1 ]
% read second column of dates in all_current_historical_data_list as a date index
date_ix = as.Date( all_current_historical_data_list[ ii , 2 ] )
todays_date = as.Date( Sys.time() )
% download the missing historical data from date_ix to todays_date. If date_ix == todays_date, will download all
% hourly bars for today only.
new_historical_data = HisPricesDates( Granularity = "H1", DayAlign, TimeAlign, AccountToken, instrument,
date_ix , todays_date + 1 )
% the new_historical_data will almost certainly have incomplete hourly OHLC data in its last line,
% so delete this incomplete OHLC information
if ( new_historical_data[ nrow( new_historical_data ) , 7 ] == FALSE ) {
new_historical_data = new_historical_data[ -nrow( new_historical_data ) , ]
}
% read the last line only of the current OHLC file for this instrument
file = paste( instrument , "raw_OHLC_hourly" , sep = "_" ) % get the filename
system_command = paste( "tail -1" , file , sep = " " ) % create a unix system command to read the last line of this file
% read the file's last line
old_historical_data = read.csv( textConnection( system( system_command , intern = TRUE ) ) , header = FALSE , sep = "," ,
stringsAsFactors = FALSE )
old_historical_data_end_date_time = old_historical_data[ 1 , 1 ] % get the date value to be matched
new_historical_data_date_times = as.character( new_historical_data[ , 1 ] ) % vector to search for the above date value
ix = charmatch( old_historical_data_end_date_time , new_historical_data_date_times ) % get the matching index value
% delete that part of new_historical_data which is already contained in filename
new_historical_data = new_historical_data[ -( 1 : ix ) , ]
% append new_historical_data to the relevant raw data file
write.table( new_historical_data , file = paste( instrument , "raw_OHLC_hourly" , sep = "_" ) , row.names = FALSE , na = "" ,
col.names = FALSE , sep = "," , append = TRUE )
added_data_length = nrow( new_historical_data ) % length of added new data
new_last_date = as.Date( new_historical_data[ added_data_length , 1 ] ) % date of last update
% and amend Instrument_update file with lastest update information
all_current_historical_data_list[ ii , 2 ] = new_last_date
all_current_historical_data_list[ ii , 3 ] = all_current_historical_data_list[ ii , 3 ] + added_data_length
} % end of for all_current_historical_data_list loop
% finally, write updated Instrument_update_file to file
write.table( all_current_historical_data_list , file = "instrument_hourly_update_file" , row.names = FALSE , col.names = FALSE , na = "" )
There is one important thing to point out on lines 29 to 33, which is that this section of code relies on a
Unix based command, which in turn means that this almost certainly will not work on Windows based OSes. Windows users will have to find their own hack to load just the last line of the relevant file, or put up with loading the whole historical data file and indexing just the last line.
