blp | Python BLP code | Machine Learning library

I am trying to extract data from Bloomberg PRTU, at a specific point in time.

The following works for the current portfolio:

Is there a way to get this generic function to work?

I am having trouble running an extract using the xbbg package with the following function. It does not generate an error message but it returns an empty dataframe when a similar excel function works.

I want to get xAxis from database which is result of distinct query so my chart can display data dynamically.

for static mpchart i have no problem with that. I called this function in onCreate.

I am trying to pull data from Bloomberg using Python API. API package comes with example codes and the programs that only requires local host work perfectly. However, the programs that uses other authorization ways are always stuck with the error:

I am new in Python.. I am trying to pull the latest prices using PX_LAST below, which works perfectly using:

I'm trying to download a file generated from spring boot in the assets of the angular project. When I call the spring API from angular services, angular CLI recompiles the project after the creation of the file in assets angular folder, and then it reloads the page before getting the response from spring boot API.

I tried to call the spring boot API from angular in many ways:

• calling the api in the ngOnInit() of my component
• calling the api in the constructor of my component
• calling the api in a separate function
• using async await in the download function

I don't know ho to proceed

spring boot

Problem: I'm making an aggregate market share variable in a car market with 286 distinct models sold and a total of 501 cars sold. This group share is based on only on car characteristic: cat= "compact", "midsize", "large" and yr=77,78,79,80,81, and the share, a small double variable; a total of 15 groups in the market.

Closest answer I've found: by mishabalyasin on community.rstudio: "Calculating rowwise totals and proportions using tidyeval?" link to post on community.rstudio.

Applying the principle of select-split-combine is the closest I've come to getting the correct answer is the 15 groups (15 x 3(cat, yr, s)):

I've been experimenting with the R package 'biglasso' for high-dimensional data. However, the results I'm getting don't match the results I get for the LASSO functions from 'hdm' or 'glmnet. The documentation for biglasso is also really poor.

In the example below, the results from hdm and glmnet are very close but not exact, which is expected. However, biglasso doesn't drop the 'share' variable. I've tried all the different screen settings, and it doesn't make a difference. Any thoughts on how to get biglasso to be more consistent with the others? Thanks!

EDIT: for a given value of lambda, the results are highly similar. But each method seems to select a different lambda.. which for hdm makes sense, given that it's intended for causal inference and isn't concerned with out-of-sample prediction. hdm uses a different objective function from Belloni et al. (2012), but I'm not sure why cv.biglasso and cv.glmnet would differ so much. If I run biglasso without a screening rule, they should be maximizing the same objective function just with random diffs in the CV folds, no?

EDIT 2: I've edited the code below to include F. Privé's code to make glmnet use an algorithm similar to biglasso, and some additional code to make biglasso mimic glmnet.