Var

To obtain charts, make sure to add chart=True as the last parameter

Get underlying data

common.qa.var(data: pandas.core.frame.DataFrame, use_mean: bool = False, adjusted_var: bool = False, student_t: bool = False, percentile: Union[int, float] = 0.999, portfolio: bool = False) -> pandas.core.frame.DataFrame

Gets value at risk for specified stock dataframe

Parameters
----------
data: pd.DataFrame
    Data dataframe
use_mean: bool
    If one should use the data mean for calculation
adjusted_var: bool
    If one should return VaR adjusted for skew and kurtosis
student_t: bool
    If one should use the student-t distribution
percentile: Union[int,float]
    VaR percentile
portfolio: bool
    If the data is a portfolio

Returns
-------
list
    list of VaR
list
    list of historical VaR

Getting charts

common.qa.var(data: pandas.core.frame.DataFrame, symbol: str = ‘’, use_mean: bool = False, adjusted_var: bool = False, student_t: bool = False, percentile: float = 0.999, data_range: int = 0, portfolio: bool = False, chart=True) -> None

Displays VaR of dataframe

Parameters
----------
data: pd.Dataframe
    Data dataframe
use_mean: bool
    if one should use the data mean return
symbol: str
    name of the data
adjusted_var: bool
    if one should have VaR adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
student_t: bool
    If one should use the student-t distribution
percentile: int
    var percentile
data_range: int
    Number of rows you want to use VaR over
portfolio: bool
    If the data is a portfolio