[R-SIG-Finance] [R-sig-finance] Extracting OHLC from trade price series

[Yuri Volchik]

Hi Jeff,

suppose tick data looks like this:
01/06/2007,00:35:33,2795.,9,
01/06/2007,01:13:14,2799.,1,
01/06/2007,01:13:49,2799.75,5,
01/06/2007,01:19:24,2799.75,4,
01/06/2007,01:20:00,2800.,6,
01/06/2007,01:20:00,2800.,27,
01/06/2007,01:20:20,2800.,1,
1/06/2007,01:20:33,2800.,1,

so formatted data looks:
2007/06/01 00:55:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:00:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:05:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:10:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:15:00,2799,2799.75,6,2,194,71
2007/06/01 01:20:00,2799.75,2799.75,4,1,264,36
2007/06/01 01:25:00,2800,2800,10,4,0,239
2007/06/01 01:30:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:35:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:40:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:45:00,#NA,#NA,#NA,#NA,#NA,#NA
2007/06/01 01:50:00,2794,2794,1,1,10,290
2007/06/01 01:55:00,2799,2800,2,2,116,174

I calculate some other stuff on ticks, not just OHLC.

This is my R routine, very slow and inefficient i'm sure:

number.of.intervals<-24*60/TimeFrame   #Number of time intervals in one day
seconds.in.time.interval=TimeFrame*60
times<-seq(ISOdatetime(2000,1,1,0,TimeFrame,0),by=seconds.in.time.interval,length=number.of.intervals) 
#names of time intervals
times<-format(times,"%H:%M")
d.start<-as.POSIXlt(as.Date(time(dat.)[1]))+TimeFrame*60
d.end<-as.POSIXlt(as.Date(time(dat.)[dim(dat.)[1]]))+3600*24
Index<-seq(from=d.start,to=d.end,by=TimeFrame*60)
dd<-matrix(NA,nrow=length(Index),ncol=1)
dd[Index%in%time(dat.),1]<-dat.[time(dat.)%in%Index,Col]
if (na.max>0)
dd[,1]<-remove.na(dd,max.na=na.max,interp.type=interp.type,column=1)
n.days<-length(Index)/number.of.intervals
dd<-t(dd)
dim(dd)<-c(number.of.intervals,n.days)
ddd<-zoo(t(dd),order.by=seq(from=as.Date(d.start),to=as.Date(d.end-3600*24),by='day'))
colnames(ddd)<-times
days<-unique(as.Date(time(dat.)))
ddd<-ddd[time(ddd)%in%days,]


I'm sure it can be improved. I think the design should incorporate ability
to specify custom column with price data and different aggregation
procedures to be performed on data. Would be willing to help you to develop
such functions.

Best regards,
Yuri



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