Finance Dashboard

Personal finance dashboard for analyzing DKB (Deutsche Kreditbank) bank exports. Built with Streamlit.

Screenshot showing synthetic demo data

Table of Contents

• Why This Tool?
• Features
• Quick Start
• Categories Configuration
• Development
• Project Structure
• Exporting from DKB
• License

Why This Tool?

DKB provides CSV exports but no built-in analytics. This dashboard fills that gap with:

• Privacy-first: All data stays local, no cloud services
• German bank format support: Parses DKB's specific CSV format (Girokonto + Visa)
• Customizable categories: Define your own rules for auto-categorization
• Self-hostable: Deploy on any server for 24/7 access on your network

Features

• CSV Import: Load Girokonto and Visa credit card exports from DKB
• Auto-Categorization: Transactions automatically categorized based on keywords and IBAN rules
• Manual Overrides: Recategorize individual transactions directly in the UI
• Multiple Views:
  • Overview: Key metrics, monthly cashflow, category breakdown, daily spending
  • Spending Trends: Track category spending over time with trend analysis
  • Investments: Separate view for investment transactions (buys/sells)
  • Year Comparison: Compare spending between years by category
  • Typical Month: Average monthly budget breakdown with waterfall chart
• Transaction Clustering: Group similar merchants (e.g., all "REWE" variations) and view transactions per cluster
• Filtering: By year, date range, account, and category
• Search: Find specific transactions instantly

Quick Start

# Clone the repo
git clone git@github.com:smartschat/finance-dashboard.git
cd finance-dashboard

# Install dependencies
uv sync

# Add your DKB CSV exports to the project folder
# Files should match: *Girokonto*.csv and *Visa*.csv

# Run the dashboard
uv run streamlit run app.py

Open http://localhost:8501

On first run, categories.json is created automatically with empty configuration. Add your categorization rules via the Settings tab in the UI.

Categories Configuration

Transaction categorization is configured via categories.json (gitignored as it contains personal data):

{
  "rules": {
    "Groceries": ["rewe", "edeka", "aldi", "lidl"],
    "Transport": ["db bahn", "uber", "tier"]
  },
  "iban_rules": {
    "DE89370400440532013000": "Rent",
    "DE12345678901234567890": "Salary"
  },
  "overrides": {
    "2024-01-15_-50.00_abc123": "Shopping"
  },
  "clusters": {
    "Amazon": ["amzn", "amazon"]
  },
  "config": {
    "non_spend_categories": ["Umbuchungen", "Kreditkarte", "Investitionen"],
    "cc_settlement_patterns": ["kreditkartenabrechnung"]
  }
}

Key	Description
rules	Map category names to keyword patterns (matched against description)
iban_rules	Map specific IBANs to categories (e.g., landlord IBAN → "Rent")
overrides	Manual category assignments for specific transactions
clusters	Group similar merchant names for cleaner reporting
config	Non-spending categories excluded from totals, credit card settlement patterns

Special Categories

Some category names have special functionality:

Category	Behavior
Investitionen	Dedicated Investments tab with buy/sell tracking and cumulative charts
Abonnements	Subscription analysis table in Overview tab
Kreditkarte	Auto-assigned to credit card settlements (detected via cc_settlement_patterns)
Sonstiges	Default category for transactions matching no rules
Umbuchungen	Internal transfers (default excluded from spending calculations)
Gehalt	Salary — no special behavior, but suggested in IBAN rules dropdown

To use the Investments or Subscriptions features, create categories with these exact names (German spelling).

Development

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=finance_dashboard

# Lint and format
uv run ruff check
uv run ruff format

# Install pre-commit hooks
uv run pre-commit install

Project Structure

finance-dashboard/
├── app.py                 # Main Streamlit dashboard
├── finance_dashboard/     # Core library modules
│   ├── config.py          # Configuration loading
│   ├── data/              # CSV parsing and loading
│   └── categorization/    # Rules, overrides, clustering
├── tests/                 # pytest tests
├── categories.json        # Category rules (gitignored)
├── *.csv                  # DKB export files (gitignored)
└── pyproject.toml         # Project dependencies

Exporting from DKB

1. Log into DKB online banking
2. Go to your account → Umsätze (transactions)
3. Set date range and click "Exportieren" → CSV
4. Repeat for each account (Girokonto, Visa)
5. Place CSV files in the project folder

License

MIT