Enhancements

[qadeer-cyber]

Add docstrings to all functions: Docstrings are essential for helping other developers understand the purpose of each function and how to use them.

Use variable names that are descriptive: It is essential to use descriptive names for variables. This makes it easier for other developers to understand the purpose of each variable and the data it contains.

Add exception handling: Exception handling helps to make the code more robust by providing an alternative action when an error occurs. The option_data function, for example, could raise an exception when it cannot retrieve data from the API.

Use f-strings for string formatting: f-strings are a cleaner and more readable way of formatting strings than the current method used in the code.

Avoid using pandas apply function with lambda functions: Using apply with lambda functions can be slower than using vectorized operations provided by pandas. In the code, apply is used to create the kind and option_type columns. These can be created using vectorized string operations provided by pandas.

Use query method to filter data: The query method provides a more readable way of filtering data in a pandas dataframe.

Use assert statements for data validation: assert statements provide a way to ensure that the data being processed is of the expected type and format.

Remove unnecessary code: Some lines of code in the option_data function could be removed to make the code more readable and faster. For example, the date_to_timestamp function is not used, and the option_data dataframe could be filtered before creating unnecessary columns.

Use matplotlib instead of plotly for static plots: plotly is a great tool for interactive plots, but it can be slower than matplotlib for static plots. The iv_smile function could use matplotlib instead of plotly for faster and simpler static plots.

Add type hints: Adding type hints helps other developers to understand the types of arguments and return values expected by each function.