If you frequently:

• ✅ make shared orders with friends
• ✅ utilize many bank accounts

then Lunni can help you to better organize and analyze your finance data.

Feel free to run the project and import this example PKO statement file.

1. Import .csv bank statements:
   • mBank [UTF-8]
   • mBank savings (so-called "Cele") [Windows-1250]
   • PKO [Windows-1250]
   • ING Bank Śląski [Windows-1250]
2. Transaction Table
   • View transaction chronologically sorted in a table
3. Transaction Notes
   • Assign notes e.g. to document ambiguous transactions
4. Transaction Merging
   • Merge transactions to combine incoming money with expenses
   • Useful for handling shared orders with friends
5. Transaction Categories
   • Define transaction categories of type:
     • POSITIVE: Incomes
     • NEGATIVE: Expenses
     • IGNORE: Hidden in reports and the transaction table
6. Category Matchers
   • Automate categorization process by assigning categories to transactions using regex
7. Export to .csv
   • Export financial data to analyze it in external tools, for example Tableau, PowerBI, Google Data Studio

The format is the following:

Where calculated_amount is the resulting amount after merging transactions.

• ❌ Support multiple users and separate the data

Docker is required.

1. Create a prod.env file in /server directory with the following structure:

SECRET_KEY=placeholder-key
ENV_NAME=production

• SECRET_KEY This key is used by Django in critical cryptographic use cases. If you plan to deploy the app, generating your own is a must! You can do it for example at https://djecrety.ir.

• ENV_NAME Specifies which Django settings variant to load. Possible values: production. Any other value will fall back to development environment by default.

2. Run docker-compose up to build the application
3. Visit the application at http://localhost:80

Python 3.11 is required. Please note the following guide assumes you're using Unix/macOS.

1. Change directory to /server
2. Create a virtual Python environment in order to install all packages locally: python3 -m venv env
3. Activate the virtual environment: source env/bin/activate
4. Install dependencies: python3 -m pip install -r requirements.txt
5. python3 manage.py migrate
6. python3 manage.py runserver
7. Back-end server is running at http://localhost:8000

NodeJS 18.4 is required.

1. Change directory to /client
2. npm install
3. npm start
4. Front-end server is running at http://localhost:3000

• Gradually add unit tests to cover newly found bugs