I do not have experience with Julia. I'm interested in languages built for time series data specifically. APL derivatives shine in this regard, in the sense that they provide the bare metal language composed of a few very powerful verbs and adverbs. For example, I could whip up a simple linear regression package in about 10 lines of tight code. These languages also lend themselves to parallelism in a very straightforward manner by means of the "parallel apply" verb. In addition , the brevity that languages like q offer is simply unparalleled.
Julia is a little friendlier for people who aren't used to APL's point-free style, but it is designed for performance, going as far showing you how functions are compiled to LLVM and native assembler in the REPL.
