semvec/pypi-packages huggingface dataset:
quant-risk 1.1.1
Quantitative functions in Python
License: MIT
Keywords: Quantitative,Risk,Portfolio

[![Documentation Status](https://readthedocs.org/projects/quant-risk/badge/?version=latest)](https://quant-risk.readthedocs.io/en/latest/?badge=latest) ![GitHub](https://img.shields.io/github/license/QAM-ATC/Risk)
# Risk
Welcome to the Quant-Risk package!

Please head over to https://quant-risk.readthedocs.io/en/latest/ to view the latest documentation for the package

To set up your environment:
1. install pipenv by:
``` {python}
pip install pipenv
```
2. then go to the main directory of this repo and do:
``` {python}
pipenv shell
```
3. Update your setup tools package by:
```{python}
python -m pip install -U pip setuptools
```
3. Now, to install the package, run the command:
``` {python}
pipenv install quant_risk
```
4. To exit the virtual environment do:
``` {python}
exit
```

Since, PyPortfolioOpt requires  Visual Studio C++, do the following:
1. Before installing and setting up your environment, install Visual Studio Build Tools from here: https://visualstudio.microsoft.com/downloads/
choose the Community version if you have Windows
2. From the available softwares, select "Visual Studio Build Tools 2019"
3. Now your pipenv shell should be able to install PyPortfolioOpt

Note: The above is for Windows users, for Mac users please see: https://osxdaily.com/2014/02/12/install-command-line-tools-mac-os-x/

To run a jupyter notebook instance with this venv:
1. Activate your venv by:
``` {python}
pipenv shell
```
2. run the command:
``` {python}
pipenv install jupyter
```
3. run the command:
``` {python}
pipenv run jupyter notebook
```
All done!
An instance of Jupyter notebook should now open up in your browser


### *Version 1.1.1*
Fixed minor import errors

---

### *Version 1.1.0*
Added risk parity portfolios and covariance shrinkage

---
### *Version 1.0.2*
Removed Quandl dependency and risk free rate function

---
### *Version 1.0.1*
First published working build

---
### *Version 1.0.0*
Initial Build

---
IRonPyB 0.2.1
A sample test package
License: MIT
Requires Python: >=2


<!-- README.md is generated from README.Rmd. Please edit that file -->

IRon
====

[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)

A Python Package for solving **I**mbalanced **R**egressi**on** tasks

Introduction
------------

Imbalanced domain learning has almost exclusively focused on solving classification tasks, where the objective is to predict cases labelled with a rare class accurately. Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values.

The methods contained in this package are: - an automatic and non-parametric method to obtain such relevance functions, building on the concept of relevance proposed by Torgo and Ribeiro (2007); - visualisation tools; - suite of evaluation measures for optimisation/validation processes; - the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks.

Requirements
------------
For windows python32 is needed.

Installation
------------

<!-- You can install the last released version from [CRAN](https://CRAN.R-project.org) with: -->
<!--``` {r, eval=FALSE} -->
<!-- install.packages("IRon") -->
<!-- ``` -->
<!-- You can also install the latest development version from GitHub: -->
<!-- ```{r, eval=FALSE} -->
<!-- install.packages("devtools") -->
<!-- devtools::install_github("nunompmoniz/IRon") -->
<!-- ``` -->
To install from github use the following command lines:

``` r
pip install IRon  # You need to install this package!

```



References
----------

-   Rita Ribeiro and Nuno Moniz (2020). "Imbalanced Regression and Extreme Value Prediction". Machine Learning. 109, pp 1803â€“1835, Springer. https://doi.org/10.1007/s10994-020-05900-9
-   LuÃ­s Torgo and Rita Ribeiro (2007). "Utility-based regression". Proceedings of ECML/PKDD 2007, J. N. Kok, J. Koronacki, R. Lopez deMantaras, S. Matwin, D. Mladenic, and A. Skowron, Eds., pp. 597â€“604