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* add first iteration of content for Terminal Pro * Update index.md * Update dashboards.md * Update folders.md * Update grouping.md * Update report.md * Update templates.md * Update quick-start.md * Update index.md * Update index.md * Update home.md * Update news.md * Update index.md * Update index.md * Update index.md * Update data-provider.md * Update chat-with-widget.md * Update data-manipulation.md * Update forecasting.md * fix: images self closing tags * fix: only pro visitors can see pro * inter font * improve SEO content and restructuring * add new package for toggle on tutorials * move folder around * improve main page of each prod * small updates and improvements * fix logo above * small improvement in headtitle for portfolio funcs * fix typos from OpenAI's GPT-4 * fix _category_ json * remove generic SEO words * add script that generates SEO for documentation * add missing file * remove sdk warning message * fix links for andrew --------- Co-authored-by: jose-donato <zmcdonato@gmail.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: andrewkenreich <andrew.kenreich@gmail.com>
3.0 KiB
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3.0 KiB
Vendored
title, description, keywords
| title | description | keywords | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| var | This Docusaurus page contains Python source code for 'var/model' and 'var chart' functions. Each section provides detailed explanations of parameters and returns, and links to the respective source code. The functions are a part of quantitative analysis of a specified stock dataframe. |
|
import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';
Gets value at risk for specified stock dataframe.
Source Code: [link]
openbb.qa.var(data: pd.DataFrame, use_mean: bool = False, adjusted_var: bool = False, student_t: bool = False, percentile: Union[int, float] = 99.9, portfolio: bool = False)
Parameters
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | pd.DataFrame | Data dataframe | None | False |
| use_mean | bool | If one should use the data mean for calculation | False | True |
| adjusted_var | bool | If one should return VaR adjusted for skew and kurtosis | False | True |
| student_t | bool | If one should use the student-t distribution | False | True |
| percentile | Union[int,float] | VaR percentile | 99.9 | True |
| portfolio | bool | If the data is a portfolio | False | True |
Returns
| Type | Description |
|---|---|
| pd.DataFrame | DataFrame with Value at Risk per percentile |
Prints table showing VaR of dataframe.
Source Code: [link]
openbb.qa.var_chart(data: pd.DataFrame, symbol: str = "", use_mean: bool = False, adjusted_var: bool = False, student_t: bool = False, percentile: float = 99.9, data_range: int = 0, portfolio: bool = False)
Parameters
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | pd.Dataframe | Data dataframe | None | False |
| use_mean | bool | if one should use the data mean return | False | True |
| symbol | str | name of the data | True | |
| adjusted_var | bool | if one should have VaR adjusted for skew and kurtosis (Cornish-Fisher-Expansion) | False | True |
| student_t | bool | If one should use the student-t distribution | False | True |
| percentile | int | var percentile | 99.9 | True |
| data_range | int | Number of rows you want to use VaR over | 0 | True |
| portfolio | bool | If the data is a portfolio | False | True |
Returns
This function does not return anything