PersonalFinance v1.0.0

This is the first release of PersonalFinance, a tool meant to make it easier to keep track of your finances. It was purposely build to work with any type of export from your bank by making use of the configuration files and the amount of mumbo jumbo is also limited to ensure that it makes perfect sense how the model categorizes your transactions. In case it doesn't work for you, please reach out to me via one of the channels found here so I can help to make it work.

It outputs an Excel file as follows:

Besides that, you don't have to continue in Excel if you are handy with Python as all created datasets can be directly accessed in Python as well. All of the datasets can be accessed through the related get functions for example:

cashflows.get_period_overview(period='yearly')

Which returns:

Yearly	Totals	Income	Investing	Charity	Government	Health and Insurance	Housing	Study	Subscriptions	Transactions	Transport	Sports	Shopping	Groceries	Food and Drinks	Holidays	Cultural	Festivals, Clubs and Concerts	Other
2014	149.46	1222.75	0	0	0	-75.41	0	-95.7	-131.42	469.12	-77.7	-82.91	-650.32	-319.46	-278.28	-163.07	0	71.67	260.19
2015	789.73	1242.6	0	0	-127.57	-71.59	-1026.65	1108.65	-31.79	578.43	-251.82	-4.51	-1286.13	-149.76	-218.76	0	-14.48	0	1043.11
2016	1306.27	4993.12	0	0	-39.64	0	518.6	-2334.47	-20.61	-11.02	-44.48	-47	-1192.55	-193.12	-140.6	-281.97	0	-28.3	128.31
2017	-352.76	6258.63	0	0	0	-974.74	-1396.04	-859.6	-83.95	51.26	-222.98	-257.71	-2146.88	-680.85	-89.78	-883	-53.22	-109	1095.1
2018	-1237.81	12989.7	-1.04	0	-356.92	-1220.38	-1235.84	-2462.28	-420.47	221.27	-305.25	-34.51	-2057.27	-1209.5	-931.88	-1042.69	-80.68	-93.65	-2996.43
2019	8754.51	29320.7	0	0	-311.95	-1300.17	0	-1288.88	-292.23	-1063.32	-1130.1	-413.42	-3692.94	-2098.15	-1362.4	-701.8	-230.32	-179.51	-6501
2020	-1170.22	34069.3	-8430.84	-250.08	-59.7	-1113.59	0	-13.83	-22.87	-246.95	-9873.4	-331.94	-4743.16	-2373.74	-1489.41	-635.22	-63.8	0	-5591.02
2021	2354.07	34372.5	-12231.2	-273.87	888.03	-144.25	-52.87	-70.02	-210.36	-1198.2	-1184.15	-30.12	-4145.31	-3529.78	-2758.37	-748.1	-159.17	0	-6170.67
2022	19802.3	93827.3	-25007	-274.27	-812.78	-1339.41	-8110.85	-2.74	-785.28	-2142.96	-3092.08	-87.76	-14984.6	-3670.8	-6591.52	-3657.21	-359.38	-75.52	-3030.89
2023	-8997.16	60268.2	-1016.73	-180.57	-24546.8	-1001.21	-13886.2	-8370.02	-1601.86	789.25	-1824.88	-609.54	-2386.02	-2919.07	-5753.73	-4268.35	-476.46	-480.95	-732.3

And the following:

cashflows.get_transactions_overview(period='weekly')

Which returns:

Weekly	Date	Name	Value	Description	Category	Keyword	Certainty
2023-09-04/2023-09-10	2023-09-10	thuisbezorgd - Omitted due to Privacy Reasons	-12.55	thuisbezorgd - Omitted due to Privacy Reasons	Food and Drinks	thuisbezorgd	100%
2023-09-04/2023-09-10	2023-09-10	Tinq - Omitted due to Privacy Reasons	-53.81	Tinq - Omitted due to Privacy Reasons	Transport	Tinq	100%
2023-09-11/2023-09-17	2023-09-12	geldmaat - Omitted due to Privacy Reasons	-18.43	geldmaat - Omitted due to Privacy Reasons	Transactions	geldmaat	100%
2023-09-11/2023-09-17	2023-09-13	asr - Omitted due to Privacy Reasons	12.2	asr - Omitted due to Privacy Reasons	Income	asr	100%

These datasets make it possible to plot the spending pattern over time for each category. This can be simply by selecting the column and using .plot() from Pandas but it also possible to create a larger overview as shown below:

import matplotlib.pyplot as plt

# Obtain the Quarterly Cashflow Overview
quarterly_cashflows = cashflows.get_period_overview(period='quarterly')

# Define the colormap
cmap = plt.get_cmap('tab20c')

# Create the figure and axes
fig, axes = plt.subplots(
    nrows=2,
    height_ratios=[6, 1],
    figsize=(30, 10))

# Plot the data per category
quarterly_cashflows.plot.bar(
    stacked=True,
    colormap=cmap,
    title="Quarterly Cashflow Overview",
    ax=axes[0])

# Calculate the totals
totals = quarterly_cashflows.sum(axis=1)

# Plot the totals
totals.plot.bar(
    color=['g' if x >= 0 else 'r' for x in totals],
    ax=axes[1])

# Format the plot by rotating labels and adjusting space
plt.xticks(rotation=45)
fig.subplots_adjust(wspace=0, hspace=0)

This returns the following plot: