A clean, modern personal finance tracking application built with Python and Tkinter. Designed for easy transaction entry and data analysis.

• Simple Transaction Entry: Clean interface for logging income and expenses
• Calculator-Style Amount Entry: Type digits naturally, no need for "$" or decimal points
• Always-Visible Calendar: Quick date selection
• Vendor Autocomplete: Suggests vendors from your transaction history
• Customizable Categories: Easy-to-edit YAML configuration
• Excel Storage: All data stored in Excel format for easy analysis
• Duplicate Detection: Warns when similar transactions exist
• Keyboard Shortcuts: Fast data entry with Enter, Ctrl+N, Ctrl+Q

• Python 3.11+
• pip

1. Clone the repository:

   git clone https://github.com/justinschubeck/finance-tools.git
   cd finance-tools

2. Create and activate virtual environment:

   python3.11 -m venv venv
   
   # On Windows:
   venv\Scripts\activate
   
   # On Mac/Linux:
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

Basic usage (creates/uses default file: 2025.xlsx):

python main.py

Command-line options for file selection:

# Use current year file (e.g., 2025.xlsx if running in 2025)
python main.py --year

# Use current month file (e.g., 2025_11.xlsx if running in November 2025)
python main.py --month

# Use a custom file name (auto-adds .xlsx if not provided)
python main.py --file Name
python main.py --file test.xlsx

# Show help and all options
python main.py --help

1. Select Type (Income or Expense)
2. Click a Category button
3. Pick the Date from the calendar
4. Enter Vendor name
5. Type Amount (calculator-style: just digits)
6. Select Payment Medium
7. Add optional Note
8. Press Enter or click "Add Transaction"

• Enter - Add transaction
• Ctrl+N - Clear fields
• Ctrl+Q - Quit

A standalone utility for optimizing loan payments across multiple loans:

python loan_payment_calculator_gui.py

This tool distributes payments among 6 loans to equalize daily interest across all loans, helping you optimize your debt payoff strategy. It uses symbolic mathematics to solve the optimization problem.

Edit config/categories.yaml:

expense:
  - "Restaurants"
  - "Gas & Fuel"
  - "Your New Category"  # Just add it to the list!

income:
  - "Salary"
  - "Side Hustle"

Edit the medium section in config/categories.yaml:

medium:
  - "Cash"
  - "Credit Card"
  - "Your Payment Method"

Transactions are saved to Excel files in the entries/ directory:

• Yearly files: YYYY.xlsx (e.g., 2024.xlsx, 2025.xlsx)
• Monthly files: YYYY_MM.xlsx (e.g., 2025_11.xlsx for November 2025)
• Custom files: Any name you specify (e.g., Justin.xlsx, test.xlsx)
• Files are created automatically if they don't exist
• Columns: Type, Category, Date, Vendor, Medium, Amount, Note

finance-tools/
├── main.py                       # Application entry point
├── loan_payment_calculator_gui.py # Standalone loan payment optimizer
├── config/
│   └── categories.yaml           # Category configuration
├── models/
│   ├── category.py               # Category management
│   └── transaction.py            # Transaction data model
├── data/
│   └── repository.py             # Excel I/O operations
├── gui/
│   ├── main_window.py            # Main application window
│   └── widgets/                  # Reusable GUI components
└── entries/
    └── *.xlsx                    # Transaction data files

Run quick financial summary:

python analyze.py --year

Create 30+ charts and plots:

python analyze.py --year --all

Create detailed HTML report with all insights:

python analyze.py --year --report

Launch Streamlit dashboard for interactive analysis:

python analyze.py --dashboard
# Or directly:
streamlit run analysis/dashboard.py

Time-Based Analysis:

• Monthly cash flow (income vs expenses with net)
• Daily spending heatmap calendar
• Cumulative spending by category
• Transaction frequency timeline
• Seasonal patterns by quarter
• Moving averages (30-day and 90-day trends)

Category Analysis:

• Category spending pie chart
• Category treemap (hierarchical)
• Category trends over time
• Transaction amount box plots
• Category correlation matrix
• Need vs want comparison

Vendor Analysis:

• Top vendors bar chart
• Vendor frequency vs amount scatter
• Vendor spending timeline
• Recurring transaction detection

Payment Method Analysis:

• Payment medium breakdown
• Medium vs category heatmap
• Card utilization timeline
• Cash vs credit comparison

Comparative Analysis:

• Income vs expense waterfall
• Month-over-month comparison
• Weekday vs weekend patterns
• Year-over-year trends

Advanced Analytics:

• Sankey cash flow diagram
• Transaction distribution histograms
• Multi-dimensional bubble charts
• Radar charts (monthly profiles)
• Anomaly detection
• Savings rate gauge
• Spending forecast

# List all available plot types
python analyze.py --list-plots

# Generate specific plot
python analyze.py --year --plot monthly_cash_flow

# Generate report for specific file
python analyze.py --file 2024.xlsx --report

# Generate plots with date filter
python analyze.py --year --all --date-range 2025-01-01 2025-06-30

# Quick summary
python analyze.py --month --quick-summary

• pandas - Data manipulation
• openpyxl - Excel file handling
• PyYAML - Configuration management
• tkcalendar - Calendar widget
• numpy - Numerical operations
• sympy - Symbolic mathematics (for loan calculator)
• tkinter - GUI framework (included with Python)

• plotly - Interactive visualizations
• streamlit - Interactive dashboard
• seaborn - Statistical plotting
• scikit-learn - Forecasting and analytics
• kaleido - Static image export

This project is for personal use.

Justin Schubeck

• Email: jschubeck7@gmail.com
• LinkedIn: justinschubeck
• Project: github.com/justinschubeck/finance-tools

Majority Claude Code