sparebanken-sor-ynab/ynab.py

#!/usr/bin/env python3
"""
Bank Statement to YNAB Converter
Converts bank statements from various formats to YNAB-compatible CSV files
"""

import os
import sys
import glob
import re
import pandas as pd
from pathlib import Path

def parse_norwegian_number(value):
    """Convert Norwegian number format (comma decimal) to float"""
    if pd.isna(value) or value == '':
        return 0.0
    # Convert to string and replace comma with dot
    str_value = str(value).replace(',', '.')
    try:
        return float(str_value)
    except ValueError:
        return 0.0

def parse_norwegian_date(date_str):
    """Convert DD.MM.YYYY format to YYYY-MM-DD"""
    if pd.isna(date_str) or date_str == '':
        return ''
    try:
        # Parse DD.MM.YYYY and convert to date object
        return pd.to_datetime(date_str, format='%d.%m.%Y')
    except (ValueError, TypeError):
        print(f"Invalid date format: {date_str}")
        exit(1)

def convert_memo(original):
    original = original.replace(" Kurs: 1.0000", "")
    words = original.split(" ")

    while len(words) > 0:
        if words[0] == "":
            # It's empty
            del words[0]
        elif m := re.match(r'\*(\d{4})', words[0]):
            # It's the last four numbers of the card, ignore it
            del words[0]
        elif m := re.match(r'\d{2}\.\d{2}', words[0]):
            # It's the date. Move it to the end
            words.append(words.pop(0))
        elif (m1 := re.match(r'^[A-Z]{3}$', words[0])) and (m2 := re.match(r'[\d]+\.[\d]+', words[1])):
            # It's the original currency
            if words[0] == "NOK":
                # It's Norwegian kroner, ignoring
                del words[0]
                del words[0]
            else:
                # It's some other currency, move it to the end
                words.append(words.pop(0))
                words.append(words.pop(0))
        else:
            break

    return " ".join(words)

def parse_bank_sor(data):
    """
    Parse Sparebank 1 bank data
    Expected columns: Dato, Beskrivelse, Rentedato, Inn, Ut, Til konto, Fra konto
    """
    result = []
    
    for _, row in data.iterrows():
        if row.get('Status') != "Bokført":
            continue

        if row.get('Valuta') != 'NOK':
            raise ValueError(f"Unknown currency {row['Valuta']}")

        payee = convert_memo(row.get('Beskrivelse', ''))
        memo = convert_memo(row.get('Melding/KID/Fakt.nr', ''))

        result.append({
            'Date': parse_norwegian_date(row.get('Bokført dato')),
            'Payee': payee,
            'Memo': memo,
            'Outflow': -float(row['Beløp ut'] or '0'),
            'Inflow': float(row['Beløp inn'] or '0'),
        })
    
    return pd.DataFrame(result)

def parse_bank_sparebank1(data):
    """
    Parse Sparebank 1 bank data
    Expected columns: Dato, Beskrivelse, Rentedato, Inn, Ut, Til konto, Fra konto
    """
    result = []
    
    for _, row in data.iterrows():
        inflow = parse_norwegian_number(row.get('Inn'))
        outflow = parse_norwegian_number(row.get('Ut'))
        
        # Convert outflow to positive if negative
        if outflow < 0:
            outflow = -outflow
            
        result.append({
            'Date': parse_norwegian_date(row.get('Dato', '')),
            'Payee': row.get('Beskrivelse', ''),
            'Memo': row.get('Til konto', ''),
            'Outflow': outflow,
            'Inflow': inflow
        })
    
    return pd.DataFrame(result)


def parse_bank_norwegian(data):
    """
    Parse Norwegian bank data
    Expected columns: TransactionDate, Text, Memo, Amount
    """
    result = []
    
    for _, row in data.iterrows():
        amount = row.get('Amount', 0)
        inflow = amount if amount > 0 else 0
        outflow = -amount if amount < 0 else 0  # Make outflow positive
        
        result.append({
            'Date': row.get('TransactionDate', ''),
            'Payee': row.get('Text', ''),
            'Memo': row.get('Memo', ''),
            'Outflow': outflow,
            'Inflow': inflow
        })
    
    return pd.DataFrame(result)


# Dictionary of banks, filename patterns, and parsing functions
BANKS = {
    "SparebankenNorge": {
        "patterns": ["Transaksjoner*.csv"],
        "encoding": "latin1",
        "output_filename": "YNAB-{bank}-FROM-{first_date}-TO-{last_date}",
        "parse_function": parse_bank_sor,
        "delimiter": ";"
    },
    "Sparebank1": {
        "patterns": ["OversiktKonti*.csv"],
        "output_filename": "YNAB-{bank}-FROM-{first_date}-TO-{last_date}",
        "parse_function": parse_bank_sparebank1,
        "delimiter": ";"
    },
    "Norwegian": {
        "patterns": ["BankNorwegian*.xlsx", "Statement*.xlsx"], 
        "output_filename": "YNAB-{bank}-FROM-{first_date}-TO-{last_date}",
        "parse_function": parse_bank_norwegian
    }
    # Add more banks and patterns as needed
}


def process_bank_statement(file_path, parse_function, delimiter, encoding):
    """
    Process a single bank statement file
    
    Args:
        file_path (str): Path to the bank statement file
        parse_function (callable): Function to parse the specific bank format
        delimiter (Optional<str>): Field delimiter
    
    Returns:
        pd.DataFrame: Processed YNAB-compatible data
    """
    file_extension = Path(file_path).suffix.lower()
    
    try:
        # Handle CSV files
        if file_extension == ".csv":
            data = pd.read_csv(file_path, delimiter=delimiter, encoding=encoding)
        # Handle Excel files
        elif file_extension in [".xlsx", ".xls"]:
            data = pd.read_excel(file_path)
        else:
            print(f"Skipping unsupported file type: {file_path}")
            return pd.DataFrame()
        
        # Call the appropriate bank-specific parsing function
        ynab_data = parse_function(data)
        return ynab_data
        
    except Exception as e:
        print(f"Error processing file {file_path}: {e}")
        raise e
        return pd.DataFrame()


def find_bank_config(filename):
    """
    Find the appropriate bank configuration for a given filename
    
    Args:
        filename (str): Name of the file to match
    
    Returns:
        tuple: (bank_name, bank_config) or (None, None) if no match
    """
    import fnmatch
    
    for bank_name, bank_config in BANKS.items():
        for pattern in bank_config["patterns"]:
            if fnmatch.fnmatch(filename, pattern):
                return bank_name, bank_config
    
    return None, None


def convert_bank_statements_to_ynab(input_files=None):
    """
    Convert bank statements to YNAB format
    
    Args:
        input_files (list): Optional list of specific files to process
                           If None, processes all files in current directory
    """
    current_directory = Path.cwd()
    output_directory = current_directory / "YNAB_Outputs"
    
    # Create output directory if it doesn't exist
    output_directory.mkdir(exist_ok=True)
    
    # Get list of files to process
    if input_files:
        print(f"Processing {len(input_files)} dragged file(s)...")
        files_to_process = [Path(f) for f in input_files if Path(f).exists()]
    else:
        print("Processing all files in current directory...")
        files_to_process = []
        # Collect all files matching any bank pattern
        for bank_config in BANKS.values():
            for pattern in bank_config["patterns"]:
                matching_files = glob.glob(str(current_directory / pattern))
                files_to_process.extend([Path(f) for f in matching_files])
    
    files_processed = False
    
    # Process each file
    for file_path in files_to_process:
        if not file_path.exists():
            print(f"File not found: {file_path}")
            continue
            
        # Find matching bank configuration
        bank_name, bank_config = find_bank_config(file_path.name)
        
        if not bank_config:
            print(f"No bank configuration found for file: {file_path.name}")
            continue
            
        print(f"Processing file: {file_path} for {bank_name}")
        
        parse_function = bank_config["parse_function"]
        delimiter = bank_config.get("delimiter", ",")
        encoding = bank_config.get("encoding", "utf-8")
        
        # Process the file
        ynab_data = process_bank_statement(str(file_path), parse_function, delimiter, encoding)
        
        if ynab_data.empty:
            print(f"No data processed for {file_path}")
            continue
        
        filename_placeholders = {
            'bank': bank_name,
            'first_date': ynab_data['Date'].min().date(),
            'last_date': ynab_data['Date'].max().date(),
        }

        file_retry_count = 0
        while True:
            output_filename = bank_config["output_filename"].format(**filename_placeholders)

            if file_retry_count > 0:
                output_filename += f" ({file_retry_count})"
            
            output_filename += ".csv"
            output_file = output_directory / output_filename

            if not output_file.exists():
                break

            file_retry_count += 1
        
        # Export to CSV for YNAB import
        ynab_data.to_csv(output_file, index=False)
        print(f"Data saved to {output_file}")
        files_processed = True
    
    if not files_processed:
        print("No files were processed. Make sure your files match the expected patterns.")


if __name__ == "__main__":
    # Check if files were dragged onto the script
    if len(sys.argv) > 1:
        # Files were dragged - process them
        files = sys.argv[1:]
        convert_bank_statements_to_ynab(files)
    else:
        # No files dragged - run normal directory processing
        convert_bank_statements_to_ynab()
    
    # Keep window open on Mac so user can see results
    input("\nPress Enter to close...")