Software Development

Budget Analysis

Tools

Excel (VBA)

SQLite3

Data Set

July 2016 through June 2022

2022-July Summary

Quick note: the ‘Classes’ I’ve been using involve too many sub-catagories: it’s become unwieldy. Also, the naming convention was inconsistent and inflexible. So, I’m going with less catagories:

DLXxxx = daily living expenses (food, other consumables - GREEN)

HSHxxx = Household (Maintenance, gardening, large purchases - GREEN)

OTHxxx = Other expenses (Business, transport, donations, hobbies - ORANGE)

BKxxxxx = Bank-based (fees, charges - GREY)

HOLxxxx = Holidays - LILAC

INCxxxx = Revenue (income - BLUE)

LGLxxxx = Fixed expenses (Tax accountant, other legal - BLUE)

MORxxxx = Mortgage-related txns - YELLOW

The approach was to import transactions from ANZ Savings (259), my personal checking (972), credit card (568), home loan (822) and BeyondBank (647) from July 2016 through June 2022 inclusive into one spreadsheet:

Record Number
Transaction

Account

Date

Amount

Transaction Description

Transaction Code

TransNo

T-Acct

T-Date

T-Amt

T-Desc

TransCode

Final column (TransRelated) is a throw-away, exists as a kludge to make sure each row in the export CSV has equal number of columns.

Important: prior to export to CSV, to keep SQLite happy with the date field, change dd/mm/yyyy to yyyy-mm-dd.

SQLs Code

Food items for FY-2016:

SELECT TransDate, TransAmt, TransDesc FROM Transactions WHERE TransCode = ‘DLXALI’ AND TransDate BETWEEN ‘2016-07-01’ AND ‘2017-06-31’ ORDER BY TransDate;

Classes:

SELECT DISTINCT TransCode FROM Transactions ORDER BY TransCode;