similar to: Intraday financial returns

Hi, I have an intraday timeseries of financial data (see below) which has gaps due to market opening and closing hours. I am trying to plot it, but the time gap is always visible in the plot. I tried converting data to xts, zoo, timeSeries and plotting it with different functions i.e. plot.xts, plot.zoo. The only way to make it work was with function 'chartSeries' in the quantmod package

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",

Hi, You may need to add "dec=","" in the read.csv. dat1<- read.table(text=" Time;Mid 31/01/2013 00:00;1,35679 31/01/2013 00:01;1,35678 31/01/2013 00:02;1,356785 31/01/2013 00:03;1,35689 31/01/2013 00:04;1,3569 31/01/2013 00:05;1,3569 31/01/2013 00:06;1,356885 31/01/2013 00:07;1,35691 31/01/2013 00:08;1,357

I'm trying to read intraday zoo but running into issues (again) ... what am I missing here? (the date doesn't seem to read in correctly) > head(dat) TrdDate TrdTime impliedVol 1 20090102 09:55:03 0.3610715 2 20090102 09:55:04 0.3637943 3 20090102 09:55:05 0.3752375 4 20090102 09:55:05 0.4190025 5 20090102 09:55:06 0.3696080 6 20090102 09:55:06 0.4944981 > f <-

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Hello, I have a series of intraday (high-frequency) price data in the form of POSIX timestamp followed by the value. I sucesfuly loaded that into "its" package object. I would like to create from it a regularly spaced time series of prices (for example 1min, 5min, etc apart) so i could calcualte returns. There is an interpolation function locf() that for timestamp with value NA uses last

Hello, I'm new to R and am having trouble plotting intraday data on a chart. I haven't had any success with using ideas from some other posts or other content. My data is in csv format, here's the first few rows: TimeStamp..UTC. Open High Low Close 1 2011-06-15 13:30:00:0000 127175 127500 126925 127425 2 2011-06-15 14:00:00:0000 127400 127575 127225 127225 3 2011-06-15

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I want to create a open/high/low/last plot of intraday data. I try to use the function plotOHLC from the tsteries package. I create my own multiple time series and then try to plot it. raw Data Format (file eurusd2.csv): "Date (GMT)" "Open" "High" "Low" "Last" 17-03-2008 00:00:00 1,5764 1,5766 1,5747 1,5750 17-03-2008 00:05:00 1,5749 1,5750 1,5741

Hello all, I'm trying to use zoo.read but can't figure out how to deal with the time format. (example below) would be nice if someone could help. best regards, Immanuel --------------------------- L <- "Date,Time,Open,High,Low,Close,Up,Down 05.02.2001,00:30,421.20,421.20,421.20,421.20,11,0 05.02.2001,01:30,421.20,421.40,421.20,421.40,7,0

I am new to R and am trying to solve the following problem: I have a data file containing tick-by-tick, millisecond level prices for some stocks. I have another file or two containing orders and trades, again, with millisecond time-stamps. Both of these files are irregularly spaced and the time stamps are in an iso format (<date> <time>.<millisecond>) I would like to create a

Hi I have data that is sampled (in time) with a certain frequency and I would like to express this time series as a time series of a higher (or lower) frequency with the newly added time points being filled in with NA, 0, or perhaps interpolated. My data might be regularly or irregularly spaced. For example, I might have quarterly data that I would like to handle as a monthly time series with

I work with a list of crypto assets daily closing prices in a xts class. Here is a limited example: asset.xts.lst <- list(BTCUSDT = structure(c(26759.63, 26862, 26852.48, 27154.15, 27973.45), dim = c(5L, 1L), index = structure(c(1697068800, 1697155200, 1697241600, 1697328000, 1697414400), tzone = "UTC", tclass = "Date"), class = c("xts", "zoo")), ETHUSDT

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Arnaud, Short answer may be that the tibble data structure will not be supporting row names and you may want to simply save those names in an additional column or externally. My first thought was to simply save the names you need and then put them back on the tibble. In your code, something like this: save.names <- names(my.ret.lst) result.tib <- as_tibble_col(unlist(my.ret.lst),

My code below makes a data frame with columns for date, time, and price. Time on each date runs from 1 to 4. I'd like to add a new column "ts$closingprice", which would have the closing price for that date. To find the closing price, I'd like to take the price in the row having the greatest time value for each date. Then I'd like to fill that closing price into the

Dear all, I work with (vibrational) spectra: some kind of intensity (I) over frequency (nu), wavelength or the like. I want to do fourier transform for interpolation, smoothing, etc. My problem is that the spectra are often irregularly spaced in nu: the difference between 2 neighbouring nu varies across the spectrum, and data points may be missing. Searching for discrete fourier transform

Hi, I have a bunch of irregularly spaced xts time series (with a POSIX index), and I'm trying to write a function that fillls the missing days. Using a solution suggested by Gabor Grothendieck for zoo, I wrote the following: # FD: Fill missing days FD<-function(ser) {rng<-range(time(ser)) > temp<-merge(ser,xts(,seq(rng[1],rng[2],"day"))) >

I am trying to extract one particular piece of data(Float) from all the data returned by yahooKeystats, but thus far I'm having no luck. This is what I've got so far: > library(fImport) Loading required package: timeSeries Loading required package: timeDate > data<-yahooKeystats("IBM") trying URL 'http://finance.yahoo.com/q/ks?s=IBM' Content type 'text/html;

Hi, tryCatch seems to be evaluating the all expressions wrapped in it before passing control to the error handling function. For example, the code below will try to evaluate "results" even though the call to odbcConnect fails. I was hoping that the mechanism would work in the same way as a C++ try catch structure and pass control to the catch... as soon as an exception is generated.