moneytrack

Moneytrack is a python package that helps you track and monitor your personal finances. All you need to provide is:

• Updates of account balances over time. These can be as frequently or infrequently as you wish.
• Balance transfers between accounts.

This information is processed to infer the daily balance + interest payments per account, that can be aggregated over different time periods and/or account types. For example, you may wish to see your average rate of interest over your entire portfolio for a given year.

Installation

Moneytrack is available from PyPi, and can be installed as follows:

pip install moneytrack

Datasets

Moneytrack relies on three datasets that are updated by the user. These can be stored in separate csv files, or alternatively as an Excel workbook with multiple sheets. Example data can be found in moneytrack/sample_data. To generate a set of empty csv files with the correct headers, run the following:

import moneytrack as mt
mt.create_csv_template("moneytrack_data_dir/")

or alternatively an empty Excel workbook using:

import moneytrack as mt
mt.create_excel_template("moneytrack_data/data.xlsx")

A brief description of each dataset follows:

Balance Updates

This dataset is used to checkpoint account balances e.g. on 1st Dec, my Santander savings account had a balance of £200. These checkpoints can be used to infer the (average) interest payments each day. The balance provided for any day is assumed to be the closing balance at the end of the day after any interest payments and transfers into / out of the account.

Balance Transfers

This dataset is used to record transfers between different accounts e.g. on 12th Dec, I transferred £50 from my Santander savings account to my HSBC regular saver. If a transfer and a balance update are made on the same day, it is assumed that the update is after the transfer.

Accounts

The final dataset lists all accounts that are tracked in MoneyTrack. You must give each account a unique key that is used in the Balance Updates / Balance Transfers datasets. Here you can also store details about your accounts e.g. you could add a boolean IS_ISA column. These addtional details can later be used for filtering and grouping the data e.g. if you wanted to determine your overall interest payments from ISA/non-ISA accounts.

Tutorial

Once you have installed moneytrack and populated your datasets (as described above), you are ready to get started.

The best way to use moneytrack is from a Jupyter notebook.

Loading data

If your data is stored in an Excel workbook, it can be loaded as follows:

import moneytrack as mt
md = mt.MoneyData.from_excel("moneytrack_data.xlsx")

If you are instead storing the data in csv files (three separate files for balance updates, balance transfers and accounts), they can be loaded as follows:

import moneytrack as mt
md = mt.MoneyData.from_csv_dir("moneytrack_data/")

If the csv files are in separate directories, the mt.MoneyData.from_csv method allows you to specify the path to each dataset.

Filtering and aggregating

Once your MoneyData object has been loaded, you can apply filters and / or aggregate your data. To filter for a specific date range:

md_filtered = md["2021-01-02":"2021-04-01"]

There are also some helper functions for common date ranges e.g.

import moneytrack as mt

date_range_2020 = mt.DateRanges.calendar_year(year=2020)
dates_from_2020_02 = mt.DateRanges.from_date(year=2020, month=2)
date_range_tax_20_21 = mt.DateRanges.tax_year(year_starting=2020)

md = mt.MoneyData.from_excel("moneytrack_data.xlsx")
md_filtered = md[date_range_tax_20_21]

If you wish to filter by any account property (any column in the accounts data), then this is also possible. For example:

md_stocks = md.filter_accounts({"ACCOUNT_TYP":"STOCKS AND SHARES"})
md_filtered = md.filter_accounts({"ACCOUNT_TYP":["STOCKS AND SHARES", "EASY_ACCESS"]})

If you wish to aggregate data, you can use the groupby_accounts method, passing any account property. For example, the following will aggregate ISA and non-ISA accounts together:

md_isa = md.groupby_accounts("ISA")

Visualization

To visualize your data there are two options MoneyPlot and MoneyPlotly. The former uses matplotlib, whereas the latter uses Plotly (interactive plotting). The interface for both options is identical.

The following will show the amount of interest gained cumulatively over time:

f, ax = mt.MoneyPlot.plot(
    md,
    metric=mt.Metric.Interest,
    cumulative=True
)
ax.legend()
f.autofmt_xdate()

In addition to the 'Interest' metric, you can also display 'Balance', 'Transfers', and 'InterestRate'.

For some metrics, it is also possible to draw bar charts and pie charts:

f, ax = mt.MoneyPlot.bar(md, mt.Metric.InterestRate, normalize=False)

f, ax = mt.MoneyPlot.pie(md, mt.Metric.Balance, normalize=False)