search for: periodici

Hi Everyone. I am downloading intraday Bloomberg data from R. The code I give is: library(zoo) library(chron) library(RBloomberg) conn<-blpConnect(show.days="trading",na.action="previous.days",periodici ty="daily") dat<-blpGetData(conn, "VG1 Index", c("LAST_PRICE"), start=as.chron(as.Date("2006-9-01", "%Y-%m-%d")),barfields="OPEN", barsize=10,retval="data.frame") blpDisconnect(conn) write.table(dat,"Z:\h1.csv",se...

...erg command. The object "intra" here is a zoo object. I need to convert this zoo object into a data frame, called bb. " library(zoo) library(chron) library(RDCOMClient) library(RBloomberg) conn<-blpConnect(show.days="trading",na.action="previous.days",periodici ty="daily") intra<-blpGetData(conn, c("NOK1V FH Equity","AUA AV Equity"), "LAST_PRICE", start=as.chron(as.Date("10/11/2006", "%m/%d/%Y")),end=as.chron(as.Date("12/5/2006","%m/%d/%Y")),barfields="VO LUME",...

look at the spectrums before you do the cbind - I would not suggest letting R wrap the data to fill in a data frame. I would suggest using something that you "know how it acts" in the frequency domain like zero. You are probably introducing periodicies that are not real, and I would suggest not to go down this path. As for finding commonalities amongst signals- it all depends on what a commonality is in the signal of interest. I am sure if you refine your question an answer can be found. I have used beam forming to look for common peaks amon...

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will