MetaCash

MetaCash lets you import, label, manipulate, and analyze historical transactions from multiple financial accounts with the superpowers of Pandas, Jupyter, ipywidgets, and matplotlib.

With MetaCash you can:

• Load transactions from bank statements, credit, debit, prepair, and online accounts
• Maintain a complete and consistent archive of your historical transactions
• Label transactions with regular expression and handle different views such as "personal" and "freelance"
• Explore transactions with an interactive web dashboard and as data frames for for deep dives
• Manipulate transactions safely with transaction frames

Project status

• Used extensively by author
• Documentation needs improvement
• Contributions welcome! see Contributing section

Screenshots

Selecting target period

Exploring transactions

Transaction frames

Transaction frames are Pandas data frames where each row represents a transaction with these columns:

• timestamp: timestamp of transaction
• balance: rolling balance
• amount: amount of transaction
• currency: currency of transaction
• description: description of transaction
• type: type of record (t: regular, ib: initial balance, fb: final balance)
• label.category: transaction category

The first and records are of type ib and fb, respectively. The other intermediate records are of type t. The first and last records, as well as the rolling balance, ensure consistency and completeness with merge operations.

Setting up the local environment

MacOS

Install pyenv

First:

brew update
brew install pyenv
brew install pyenv-virtualenv

Add to ~.zshrc:

if command -v pyenv 1>/dev/null 2>&1; then
  eval "$(pyenv init -)"
  export PYENV_VIRTUALENV_DISABLE_PROMPT=1
fi

if which pyenv-virtualenv-init > /dev/null; then eval "$(pyenv virtualenv-init -)"; fi

• Install a specific version of Python: pyenv install 3.6.10
• Create virtualenv: pyenv virtualenv 3.6.10 metacash
• Activate virtualenv: pyenv activate metacash
• Update pip: pip install --upgrade pip
• Install packages: pip install -r requirements.txt

Fabric commands

• fab build: build image
• fab start: start image
• fab kill: terminate container
• fab test-pep8: run PEP8 test
• fab test: run all tests

To install ipywidgets:

pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension
apt install npm build-essential
jupyter labextension install @jupyter-widgets/jupyterlab-manager@2.0

Contributing

1. Fork it
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Create a new Pull Request