Metadata-Version: 2.1
Name: wrds_tools
Version: 0.0.8
Summary: Various tools to create a connection to the WRDS service and download commonly used data.
Home-page: UNKNOWN
Author: Julian Barg
Author-email: barg.julian@gmail.com
License: UNKNOWN
Description: # Wrds Tools
        
        Tools for accessing compustat variables through WRDS by name.
        
        ## Setup
        
        To build a connection to the wrds server via python, a .pgpass file is required in the user's home 
        directory, with access limited to the user. To create this file, follow the instructions here: ![How to access WRDS through Python](https://wrds-www.wharton.upenn.edu/pages/support/programming-wrds/programming-python/python-from-your-computer/) (WRDS login required).
        
        After creating the file, don't forget to run "chmod 0600 ~/.pgpass" in the console to limit access, ![as also described here](https://www.postgresql.org/docs/9.5/libpq-pgpass.html).
        
        ### Using package directly from github
        
        Install import_from_github_com from your terminal to use this package directly from github.
        
        ```bash
        pip3 install import_from_github_com
        ```
        Or use your package manager (e.g., Conda).
        
        Now you can use Wrds Tools by importing it from github.
        ```python
        import wrds
        from github_com.julianbarg import wrds_tools
        ```
        
        ## Example
        Build a connection to WRDS.
        ```python
        wrds = wrds_tools.WrdsConnection()
        ```
        ```
        Loading library list...
        Done
        ```
        
        Download all S&P 500 constituents from between 2002-2007.
        ```python
        from datetime import date
        
        wrds.set_observation_period(start_date=date(year=2002, month=1, day=1), 
                                    end_date=date(year=2007, month=12, day=31))
        wrds.build_sp500()
        wrds.add_names()
        sp500 = wrds.return_dataframe()
        ```
        
        Save your sample to a .csv and excel file.
        ```python
        sp500.to_csv('sp500.csv')
        sp500.to_excel('sp500.xlsx')
        ```
        
        Run custom wrds queries.
        ```python
        db = wrds.db
        
        KLD_ratings = db.get_table('kld', 'history')
        
        # get some basic financials
        funda = db.raw_sql('select GVKEY, FYEAR, FIC, REVT, SALE, EMP, GP, CURCD from compa.funda')
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
