Daily in percent

#Create download pahts

year=c(2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014)
output=vector()
downloadDir="C:/Users/erik.johansson/Downloads"
setwd(downloadDir)
for(i in year){
  for(j in 1:12){
    URL=URL_GT("ftse 100", year=i, month=j, length=2)
    output=append(output, URL)
  }
}

#Create table to store ouput
URL=output
gt_results=data.frame(as.Date("10.1.2004", "%d.%m.%Y"), NA, NA, NA, 1)
colnames(gt_results)=c("Date", "SVI", "Company",  "Path", "Percentage")

for(i in 1:length(URL)){
#Download file
  gt_path=downloadGT(URL[i], downloadDir)
  
#Format csv
  gt_data=readGT(gt_path)

#Increment all by one to make percentage calculation possible
  gt_data[,2]=gt_data[,2]+1

  gt_data[which(is.na(gt_data[,2])),2]=1
  gt_data[5]=NA
  names(gt_data)[5]="Percentage"

#Calculate percentage change  
  for(j in 2:nrow(gt_data)){
    gt_data$Percentage[j]=gt_data$SVI[j]/gt_data$SVI[j-1]
  }

#Find first instance of date overlap in the new file
  date_match=which(gt_data$Date==gt_results[nrow(gt_results),1])

#To ensure that we haven't skipped a date (since the data might be on a weekly level in some cases) we do the same check for the results data
  date_match_results=which(gt_results$Date==gt_results[nrow(gt_results),1])
  
  if(length(date_match)>0) {
    gt_data_subset=gt_data[(date_match+1):nrow(gt_data),]
    gt_results=gt_results[1:date_match_results,]
    } else {gt_data_subset=gt_data}
  colnames(gt_data_subset)=c("Date", "SVI", "Company",  "Path", "Percentage")
  gt_results=rbind(gt_results, gt_data_subset)
}