scgFinance

Overview

scgFinance is a Python package designed to simplify personal finance management. It provides tools to import, categorise, track, visualise, and forecast financial data from bank and credit card statements. Using a combination of rule-based and machine learning approaches, it adapts to your spending patterns over time, making it easier to monitor budgets and identify trends.

Key Features

• Automatic Categorisation: Uses rules and machine learning to classify transactions, improving accuracy with more data.

• Import from Multiple Sources: Supports bank and credit card CSV files with customisable column mappings.

• Tracking and Visualisation: Generate reports, charts, and forecasts for spending patterns.

• Budget Comparison: Compare actual spending against predefined budgets.

• Metadata Tracking: Avoids re-processing files by maintaining a record of imported data.

Installation

scgFinance requires Python 3.12 or later. You can install from PyPI.

pip install scgFinance

Quickstart

scgFinance includes a utility function to set up a recommended project directory structure, complete with empty folders for your data, a default rules file for categorisation, and a template script to get started with processing. Use download_template to initialise the structure:

from scgFinance.utils import download_template

# Create the project structure in the specified root directory
download_template()

This will generate the following structure:

your_project_root/
│
├── metadata/
│   └── rules.csv            # Default categorisation rules (customisable)
│
├── raw_data/
│   ├── bank/                # Place your bank statement CSVs here
│   └── credit_card/         # Place your credit card statement CSVs here
│
└── categorise_statements.py # Template script for running the processing pipeline

After setup:

1. Download your statements from your financial institution (e.g., your bank) in a CSV format.

2. Save your statements in the raw_data/bank/ or raw_data/credit_card folders (rename, add, or delete folders depending on your needs e.g., to raw_data/HSBC/).

3. Customise metadata/rules.csv if needed based on your financial statements and common transactions

4. Modify/run categorise_statements.py to process your data.

For more details, see the User Guide.