The rutils package contains functions for:
extracting names and columns from time series,
calculating the end points of a time series,
applying lags to vectors and matrices,
calculating row differences of vectors and matrices,
recursively binding a list of objects into time series,
recursively applying a function to a list of objects,
plotting time series with custom axis range and background shading.
The rutils package also includes a dataset with OHLC time series data for a portfolio of symbols. The data is contained in an environment called etf_env, which includes:
sym_bols: a vector of strings with the portfolio symbols,
individual time series VTI, VEU, etc., containing daily OHLC prices for all the sym_bols,
price_s: a single xts time series containing daily closing prices for all the sym_bols,
re_turns: a single xts time series containing daily returns for all the sym_bols.
Each individual xts time series contains the columns: Open prices, High prices, Low prices, Close prices, trading Volume, Adjusted prices.
Install package rutils from github:
install.packages("devtools") devtools::install_github(repo="algoquant/rutils") library(rutils)
Install package rutils from source on local drive:
install.packages(pkgs="C:/Develop/R/rutils", repos=NULL, type="source") # Install package from source on local drive using R CMD R CMD INSTALL C:\Develop\R\rutils library(rutils)
Build reference manual for package rutils from .Rd files:
system("R CMD Rd2pdf C:/Develop/R/rutils") R CMD Rd2pdf C:\Develop\R\rutils
The rutils package contains a dataset of daily OHLC time series in xts format, for a portfolio of stock symbols. The time series are contained in an environment called etf_env. The data is set up for lazy loading, so it doesn't require calling
data(etf_data) to load it before being able to call it.
# get first six rows of OHLC prices head(etf_env$VTI) # plot chart_Series(x=etf_env$VTI["2009-11"])
Extract the name of an OHLC time series from its first column name:
# get name for VTI get_name(colnames(rutils::etf_env$VTI))
Calculate a vector of equally spaced end points for a time series:
# calculate end points with initial stub interval calc_endpoints(etf_env$VTI, inter_val=7, off_set=4)
Extract columns of prices from an OHLC time series:
# get close prices for VTI get_col(etf_env$VTI) # get volumes for VTI get_col(etf_env$VTI, col_name="vol")
Apply a lag to a vector or matrix:
# lag vector by 2 periods lag_it(1:10, lag=2) # lag matrix by negative 2 periods lag_it(matrix(1:10, ncol=2), lag=-2)
Calculate the row differences of a vector or matrix:
# diff vector by 2 periods diff_it(1:10, lag=2) # diff matrix by negative 2 periods diff_it(matrix(1:10, ncol=2), lag=-2)
Calculate the time differences of an xts time series and pad with zeros:
# calculate time differences over lag by 10 periods rutils::diff_xts(etf_env$VTI, lag=10)
Recursively rbind a list of objects:
# create xts time series x_ts <- xts(x=rnorm(1000), order.by=(Sys.time()-3600*(1:1000))) # split time series into daily list list_xts <- split(x_ts, "days") # rbind the list back into a time series and compare with the original identical(x_ts, do_call_rbind(list_xts))
Recursively apply a function to a list of objects:
# create xts time series x_ts <- xts(x=rnorm(1000), order.by=(Sys.time()-3600*(1:1000))) # split time series into daily list list_xts <- split(x_ts, "days") # rbind the list back into a time series and compare with the original identical(x_ts, do_call(rbind, list_xts))
Apply a function to a list of objects, merge the outputs into a single object, and assign the object to the output environment:
do_call_assign( func_tion=clo_se, sym_bols=etf_env$sym_bols, out_put="price_s", env_in=etf_env, env_out=new_env)
Plot an xts time series with custom y-axis range and with vertical background shading:
chart_xts(etf_env$VTI["2015-11"], name="VTI in Nov 2015", ylim=c(102, 108), in_dex=index(etf_env$VTI["2015-11"]) > as.Date("2015-11-18"))
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