An API-Interface is much more reliable than webscraping.
You will find much more API Interfaces to financial databases in Python, especially when you are using financial algorithm backtesting portals like Quantopian.com. And the best thing is: You can still use R in Python by the RPY2 library...
I guess, the more pertinent terms for what you are for would be
Stock Screening with [R]
Stock Selection with [R]
Quantitative Stock Selection with [R]
As free R-API-Access to financial portals are in a dynamic change (and I temporarily do not have such a project), I currently cannot recommend...
Trump has introduced steel and alu tariffs, that economically do not make any sense. The increase of metal prices for the whole economy will counteract the benfits for the metal producers a multiple times. So buen provecho!
The German R Forum is planning an R conference, in which - among others - Rpy2 may be a topic.
Therefore we are looking for an Rpy2 referent.
He/ she should have the following skills:
Knowledge of RPy2
Being familiar with understanding problems of R migrators, e.g. due to own migration path...
There are several robust regression methods like LAR-(aka LAV-, LAD-, L1-Norm-)Regression, Quantil-Regression, M-Estimator, ... They are assumed to be especially appropriate for data, that does not fulfill the 5 OLS conditions.
The major part of the robust regression literature (I read)...
Ease of learning for beginners.
Graphical user interfaces.
Sorted libraries instead of uncontrolled package rank growth.
THOUSANDS more API interfaces to other applications, e.g. Webserver, Nvidia CUDA, ...
Future safety. Python will still be serviced for decades.
Summarizing, good numerical...
I have found the following Matlab Code for generating autocorrelated random values, with a defined autocorrelation on lag 1.
A=[1,-a1]; # A=[1,-a1] <=> A= c(1, -a1)
I know I can generate two correlated random variables x1 an x2 using
x2= c* z1 + sqrt(1- c^2)* z2
z1: standardnormal random variable
z2: standardnormal random variable
c= Correlation(x1, x2)
But how can I generate an autocorrelated variable x, autocorrelated on x(t-1) ?