Metadata-Version: 2.1
Name: riskybusiness
Version: 0.0.2
Summary: A Python Library containing various functions to analyse the risk of a business.
Home-page: https://rajathkotyal.github.io
Author: Rajath Kotyal
Author-email: rajathkotyal@gmail.com
License: UNKNOWN
Description: # Analyse the risk of a business using Risky Business
        
        ## Functions :
        1. Sharpe Ratio
        2. Returns & Volatility
        3. Risk by Return Ratio
        4. Compounded Percentage
        5. Annual Drawdown
        6. Skewness & Kurtosis
        7. Value Added Risk (VaR - Historic, Gaussian, Cornish-Fisher)
        8. CVaR - Historic
        9. VaR Comparison Plot
        
        **Important** : Read the DOCUMENTATION section below before implementing any of the functions.
        
        ## Installation  
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install riskybusiness
        
        ```bash
        pip install riskybusiness
        ```
        
        ## Usage
        
        ```python
        import riskybusiness as rb
        rb.FunctionName(dataset = Your_Dataset)
        ```
        > Make sure the dataset is loaded using pandas with the necessary columns.
        
        A sample program using all the functions is displayed in risky.ipynb
         - Open using Jupyter NB or Google Colab
         - This file contains the output samples of all the functions present in the library.
        
        [github link](https://github.com/rajathkotyal/RiskyBusiness)
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
        
        
        
        
        
        ! DOCUMENTATION !
        
        # Welcome!
        In order to use the RiskyBusiness library make sure you have installed it using :
        ```bash
        pip install riskybusiness
        ```
        
        To use the functions, Please import the library into your ipynb file using :
        ```python
        import riskybusiness as rb
        ```
        
        ### Additional Libraries that need to be imported :
        1) pandas
        2) numpy
        3) matplotlib.pyplot
        4) from scipy.stats import norm
        
        ### Important :
        1) Make sure the dataset is imported with pandas.
        2) There are NaN values present (tip : use the dropna() function*)
        3) Specify the required columns during/after import but BEFORE running the functions.
        
        ## List of Functions available in the Risky Business Library :
        
        1.
        ```python
        rb.annual_volatility(dataset)
        ```
        ### Description
        Returns the Annual Volatility of each column in the Dataset.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        2.
        ```python
        rb.compoundperc(dataset)
        ```
        ### Description
        Returns the Compound percentage of each column in the Dataset.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        3.
        ```python
        rb.month_annualize(month_risk)
        ```
        ### Description
        Returns the annualized return of each column in the Dataset given the monthly risk.
        
        ### Parameters
        month_risk - the monthly risk factor.
        
        
        4.
        ```python
        rb.annual_volatility(dataset)
        ```
        ### Description
        Returns the annualized volatility of each column in the Dataset.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        5.
        ```python
        rb.returns_month(dataset)
        ```
        ### Description
        Returns the Monthly returns of each column in an Annual Dataset.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        6.
        ```python
        rb.annualized_ret(dataset)
        ```
        ### Description
        Returns the Annualized returns of each column in a Monthly Dataset.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        7.
        ```python
        rb.sharpe(dataset,riskfree_rate):
        ```
        ### Description
        Returns the Annualized returns of each column in a Monthly Dataset.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        riskfree_rate - the riskfree_rate of your country.
        
        
        8.
        ```python
        rb.get_date(dataset):
        ```
        ### Description
        Use if your dataset has unorganised date formats.
        Returns the dataset with yyyy-mm-dd format.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        9.
        ```python
        rb.drawdown(dataset)
        ```
        ### Description
        Takes a time series of asset returns.
        Computes & returns a Dataframe that contains Wealth index , Previous Peaks & Drawdown Value.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        10.
        ```python
        rb.skewness(dataset)
        ```
        ### Description
        Computes & returns the skewness of each column.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        11.
        ```python
        rb.kurtosis(dataset)
        ```
        ### Description
        Computes & returns the kurtosis of each column.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        12.
        ```python
        rb.var_historic(dataset)
        ```
        ### Description
        Computes & Returns the historic Value at Risk at a specified level
        i.e. returns the number such that "level" percent of the returns
        fall below that number, and the (100-level) percent are above.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        13.
        ```python
        rb.cvar_historic(dataset)
        ```
        ### Description
        Computes & Returns the Conditional VaR of a Series or DataFrame.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        14.
        ```python
        rb.var_gaussian(dataset)
        ```
        ### Description
        Computes & Returns the Parametric Gauusian VaR of a Series or DataFrame
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        15.
        ```python
        rb.var_fisher(dataset)
        ```
        ### Description
        The VaR is returned using the Cornish-Fisher modification
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
        
        16.
        ```python
        rb.plotvar(dataset)
        ```
        ### Description
        Plots the comparison bar graph between the 3 VaR methods - Historic, Gaussian, Cornish-Fisher.
        
        ### Parameters
        dataset - Name of the dataset you imported.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
