Teller

Background

I thought it would be cool to be able to run SQL queries on my banking data. RBC supposedly has an API, but I tried signing up months ago and never got an account:

The next best thing seemed to be downloading my transaction history in .csv format from RBC online banking, but the data only goes back a couple months.

I wanted to be able to run queries on all transactions since I set up the account (how much money have I spent on sushi?) so that was no good.

This tool was my last resort: parse PDF e-statements. It works for savings, chequing, and visa accounts. The chequing/savings statements are the same format and the visa ones are different. I expect the chequing/savings parser to work for everyone, but I'm not sure if different visa types will work because the statements might be formatted differently. If you want to create a PR to support a different style of account/statement that'd be cool.

Disclaimer: The data that this tool spits out might not be accurate.

The parsers check the transactions they find against the opening/closing balances reported on the e-statements and will fail out if they notice a discrepancy, so the data is probably good if nothing fails when you run the tool.

An idea for an extension of this project would be to automate downloading/importing the .csv reports from RBC online every X days. Parsing transaction data from the .csvs would be much more robust and might as well replace pdf parsing once you have your historical data.

How to use

• Clone this repo

git clone [email protected]:bnwlkr/Teller.git
cd Teller

• (optional, recommended) Use a virtualenv

python3 -m venv venv
source venv/bin/activate

• Install dependencies

(venv) pip install -r requirements.txt

A Java runtime is required to run tabula-py, which you can download it here.

• Download all your e-statements (v boring)

• Put the downloaded pdfs into the statements/savings, statements/chequing, statements/visa directories.

Note: You can put the statements anywhere on your computer as long as the statements' parent directories are called chequing, savings, visa. Future improvement: detect account type. Directory structure looks like this:

statements
├── chequing
│   └── XXXXXXXXX-2020May25-2020Jun25.pdf
|   ...
├── savings
│   └── XXXXXXXXX-2020May25-2020Jun25.pdf
|   ...
└── visa
    └── XXXXXXXXX-2020May12-2020Jun10.pdf
    ...

The tool uses the names of the files to ascertain some date info so pls don't change them.

• Run it!

(venv) python teller.py -d statements teller.db

If you put the statements somewhere else, specify the path to their parent directory with the -d option.

After a while, teller.db, a sqlite3 database file, will contain all the transaction data. You can just leave the data there, and later add new statements and rerun with the same .db file - the tool will manage uniqueness of transactions in the database (duplicate files are fine). I recommend rerunning later with only new statements to save time.

You can use the sqlite3 CLI to run queries, but I recommend using DB Browser for SQLite.

Now you can have fun running queries and feeling bad about your spending habits. For example:

SELECT sum(amount) FROM transactions WHERE description LIKE '%Dunbar Sushi%'

P.S. If you know a way to get all RBC transaction data in a computer-readable format instead of this nonsense, just don't tell me.

Example Queries

• How much interest have I earned in my savings account?

SELECT sum(amount) FROM transactions WHERE account_type='savings' AND description LIKE '%interest%'

• What's the biggest purchase I've ever made on my credit card?

SELECT datetime(timestamp, 'unixepoch'), MIN(amount), description FROM transactions WHERE account_type='visa'

Planned

• Add more examples
• Change from storing unix timestamps to datetime strings for ease of use
  • For now you can see the date of a transaction in a more readable format by including datetime(timestamp, 'unixepoch') as a column in your query.
• Detect account type from statements instead of relying on directory structure
• Automations