Rule1-StockScreener

Scripts to populate a MySQL database with Rule #1 screened stocks.

Stocklists

Any stock lists that you want to query should be CSV (comma-separated-value) files and should be placed in the csv/ subdirectory.

For instance, this repository maintains a S&P 500 list in csv/s&p.csv.

Dependencies

1. Install python if it's not already on your system.
2. Install pandas via python -m pip install pandas.
3. Install MySQL
4. Remember your root password set during the install as it will be needed by the script. The script assumes it's also root. If you set a separate password, then you'll need to update the code to use that password in the connect call.

Populate the mySQL database

You will need to do this at least once, to initially populate the database. You may also want to run it periodically (monthly, once-per-quarter, annually, etc), depending on how fresh you want the data. (Most of the data does not change more than once per quarter or even annually, so it does not need to be refreshed too often.)

To populate the database, simply run populate_database.py optionally passing in a filename from the csv/ subdirectory as the command line argument:

# If no filename is provided, defaults to `s&p.csv`
python3 populate_database.py

or

# Example providing a custom filename. This assumes you put `stocks.csv` in the `csv/` subdirectory.
python3 populate_database.py 'stocks.csv'

This script will connect to mySQL using root/root as the login. It will then create a new database called isthisstockgood with a table called stocks and populate it with the Rule #1 Investing computation data for the provided stocks.

(NOTE: If using csv/s&p.csv then this will take some time. There's 500 of them. You may also see some 'ERROR' messages from the underlying API, but this is working as intended. These are data errors in the underlying code which are being logged.)

Exploring the table

The resulting table will look something like this:

Helpful queries can be found in the sql/ subdirectory.