164 lines
6.5 KiB
Python
164 lines
6.5 KiB
Python
from beancount.ingest import importer
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from beancount.core import data
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from beancount.core.number import D
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import csv
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import datetime
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class MyCSVImporter(importer.ImporterProtocol):
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"""A Beancount importer for CSV files"""
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def identify(self, file):
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return file.name.endswith('.csv')
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def file_account(self, file):
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return "Assets:Bank:PostbankGiro"
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def file_date(self, file):
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"""Returns the unique date of the file, if any."""
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# Optioneel: als je bestandsnaam een datum bevat
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return None
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def file_name(self, file):
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"""Returns the unique name of the file, if any."""
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# Optioneel: als je een unieke naam wilt baseren op het bestand
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return None
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def extract(self, file, existing_entries=None):
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"""Extracts entries from a files."""
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entries = []
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# file.contents
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with open(file.name, encoding='utf-8') as f:
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csv_reader = csv.reader(f, delimiter=';')
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# Gebruik enumerate() om een teller (index) te krijgen
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for index, row in enumerate(csv_reader):
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if len(row) < 18 or row[17] != "EUR":
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continue
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# De index begint bij 0, dus de 12e kolom is index 11.
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# Boekingsdatum = index 0
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# Ontvanger = index 3
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# Omschrijving = index 4
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# Bedrag = index 11
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# Valuta = index 17
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datum_str = row[0]
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ontvanger = row[3]
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omschrijving_str = row[4]
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bedrag_str = row[11]
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valuta = row[17]
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dag, maand, jaar = datum_str.split('.')
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transactie_datum = datetime.date(int(jaar), int(maand), int(dag))
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payee = ontvanger
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narration = omschrijving_str
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# Verwijder eerst de duizendtallen-scheidingstekens (de punten)
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bedrag_str = bedrag_str.replace('.', '')
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# Vervang daarna de komma door een punt
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bedrag_str = bedrag_str.replace(',', '.')
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bedrag = D(bedrag_str)
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currency = valuta
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meta = data.new_metadata(file.name, index)
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# Bepaal of het een inkomst of uitgave is op basis van het voorteken van het bedrag.
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if bedrag < D(0):
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# Uitgave: bedrag is negatief.
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# Geld gaat van de bankrekening naar een uitgavenrekening.
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tegenrekening = self._map_payee_to_account(payee + " " + omschrijving_str)
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#FORMAT: data.Posting(account, units, cost=None, price=None, flag=None, meta=None)
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postings = [
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#data.Posting(self.file_account(file), bedrag, currency, None, None, None),
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data.Posting(self.file_account(file), data.Amount(bedrag, currency), None, None, None, None),
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#data.Posting(tegenrekening, -bedrag, currency, None, None, None),
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data.Posting(tegenrekening, data.Amount(bedrag, currency), None, None, None, None),
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]
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else:
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# Inkomsten: bedrag is positief.
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# Geld gaat van een inkomstenrekening naar de bankrekening.
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tegenrekening = self._map_payee_to_account(payee + " " + omschrijving_str)
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#FORMAT: data.Posting(account, units, cost=None, price=None, flag=None, meta=None)
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postings = [
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#data.Posting(tegenrekening, -bedrag, currency, None, None, None),
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data.Posting(tegenrekening, data.Amount(-bedrag, currency), None, None, None, None),
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#data.Posting(self.file_account(file), bedrag, currency, None, None, None),
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data.Posting(self.file_account(file), data.Amount(bedrag, currency), None, None, None, None),
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]
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transaction = data.Transaction(
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meta=meta,
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date=transactie_datum,
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flag='*',
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payee=payee,
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narration=narration,
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tags=frozenset(),
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links=frozenset(),
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postings=postings
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)
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entries.append(transaction)
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return entries
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def _map_payee_to_account(self, payee):
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mapping = {
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#INCOME postings
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"Lohn": "Income:Salaris",
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"Gehalt": "Income:Salaris",
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"Landkreis Meissen":"Income:BasicIncome",
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#EXPENSES postings
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"Miete": "Expenses:Rent",
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"Sachsen":"Expenses:Electricity",
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"Kontoführung":"Expenses:Banking",
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"AMAZON":"Expenses:Subscriptions",
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"Allianz":"Expenses:Insurance",
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"Autohof":"Expenses:Driving",
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"Tankstelle":"Expenses:Driving",
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"ESSO":"Expenses:Driving",
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"ARAL":"Expenses:Driving",
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"Yellowbrick":"Expenses:Driving:Parking",
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"PH":"Expenses:Driving:Parking", # Narrow down
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"eBay":"Expenses:Gadgets", # Differentiate
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"MEDIA MARKT":"Expenses:Gadgets",
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"Logic Pro":"Expenses:Gadgets",
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"Thomas Klotsche":"Expenses:Household",
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"POCO":"Expenses:Furniture",
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"Tapete":"Expenses:Furniture",
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"Deutsche Post AG":"Expenses:Postdelivery",
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"Echtzeitüberw":"Expenses:Banking",
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"Apotheke":"Expenses:Drugs",
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"ALDI":"Expenses:Food",
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"Lidl":"Expenses:Food",
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"Bosch":"Expenses:Food", #BOSCH catering
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"TRANSGOURMET":"Expenses:Food",
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"Netto Marken":"Expenses:Food",
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"Rewe":"Expenses:Food",
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#Creditcards
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"AMERICAN EXPRESS":"Expenses:Creditcard",
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"CONSORS":"Expenses:Creditcard",
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#SAVINGS postings
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"Bitpanda":"Assets:Savings:Trade"
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#DEBTS
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#"111649731":"Debts:Basic income",
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# Actually booked from Dutch Account
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#"Duo studieschuld":"Debts:Student loan (NL)",
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#"DUO Studienschuld":"Debts:Student loan (NL)",
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#"Bundeskasse Halle":"Debts:Student loan (DE)"
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}
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for sleutelwoord, rekening in mapping.items():
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if sleutelwoord.lower() in payee.lower():
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return rekening
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return "Expenses:Uncategorized" |