I am new to Julia and would like to try it to deal with financial time
series. I read there has been a good bunch discussions within the Julia
community about it (for example, some interesting ones on indexing).
As a test, I would like to read some 100MB .csv file containing prices into
an array (or data-frame?), computing their logarithmic returns and
eventually compute a covariance matrix.
Something that in R I might do like:
## Read CSV file and store contents in a dataframe:
## - fields are separated by semicolons,
## - first line contains column names,
## - first column contains row names,
## - decimal separator is a comma.
prices <- read.table ("prices.csv",
sep = ";", header = TRUE, row.names = 1, dec = ",")
## Convert prices into logarithmic returns by applying the diff function on
## the log of the prices:
returns <- apply (log (prices), 2, diff)
## Compute the covariance matrix for the logarithmic returns:
returns_covariance <- cov (returns, use = "pairwise.complete.obs")
As far as the current state of the art is concerned, which are the latest
packages that are considered a reference at the moment? Is it TimeSeries.jl?
