In my last post I said I was going to look at data wrangling my data, and this post outlines what I have done since then.
My problem was that I have numerous csv files containing historical data with different date formats and frequency, e.g. tick level and hourly and daily OHLC, and in the past I have always struggled with this. However, I have finally found a solution using the R quantmod package, which makes it easy to change data into a lower frequency. It took me some time to finally get what I wanted but the code box below shows the relevant R code to convert hourly OHLC, contained in one .csv file, to daily OHLC which is then written to a new .csv file.
library("quantmod", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
price_data = read.csv( "path/to/file.csv" , header = FALSE )
price_data = xts( price_data[,2:6] , order.by = as.Date.POSIXlt( strptime( price_data[,1] , format = "%d/%m/%y %H:%M" , tz = "" ) ) )
price_data_daily = to.daily( price_data , drop.time = TRUE )
write.zoo( price_data_daily , file = "path/to/new/file.csv" , sep = "," , row.names = FALSE , col.names = FALSE )To finally achieve such a small snippet of working code I can’t believe how much time I had to spend reading documentation and looking online.
This next code box shows Octave code to load the above written .csv file into Octave
fid = fopen( 'path/to/file' , 'rt' ) ;
data = textscan( fid , '%s %f %f %f %f' , 'Delimiter' , ',' , 'CollectOutput', 1 ) ;
fclose( fid ) ;
eurusd = [ datenum( data{1} , 'yyyy-mm-dd' ) data{2} ] ;
clear data fidHopefully, in both cases, manipulating the format strings “%d/%m/%y %H:%M” and ‘yyyy-mm-dd’ in these two respective code snippets will save you the hours I spent.
Useful links that helped me are: