Hi. I'm doing an analysis using currency tick data, and the sample size is 12 million data per year. It's in memory, but it's very inefficient because it takes a lot of time to process each one of them when we try to do complicated processing. This time, I would like to introduce the way to compile functions written in C++ as functions on R using Rcpp package to increase the processing speed.
I found some data missing from the last data collection, so I scraped it again.
We had an in-house workshop and spent the holidays locked up in the Library of Congress, fishing for prior research, which allowed us to build a subtle asset allocation model.
I implemented the much talked about Gauss regression, which is too versatile to be fun in reverse.