Difference between revisions of "Pynomina/ledger"

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see also https://github.com/WolfgangFahl/pynomina/issues/3
 
see also https://github.com/WolfgangFahl/pynomina/issues/3
== Motivation ==
 
In the past decades the author used different personal accounting tools:
 
* [https://en.wikipedia.org/wiki/Quicken Quicken]
 
* [https://en.wikipedia.org/wiki/Microsoft_Money Microsoft Money]
 
* [https://www.wikidata.org/wiki/Q1822341 Lexware Finanzmanager]
 
* [https://www.wikidata.org/wiki/Q130438296 BankingZV]
 
  
The [https://wiki.bitplan.com/index.php/IT_Pain_Scale pain] the conversion between those tools created was finally big enough to do something about it.
 
 
== Goals ==
 
* use a computer and human-readable ledger format that is ready to survive decades
 
* convert from and to the formats of the tool of choice
 
* allow for simple sanity checks and reports
 
* allow for systematic tidy up
 
* allow for integration into a larger organizational knowledge graph
 
 
 
== Hub & Spoke Conversion ==
 
The pyNomina tool follows a '''Hub and Spoke''' model for conversion between different personal accounting file formats. The '''Ledger Book (YAML/JSON)''' format acts as the hub, with each supported format serving as a spoke. This setup simplifies conversions by allowing data to be transformed from any spoke to the hub and then to any other spoke format.
 
 
[[File:https://diagrams.bitplan.com/render/png/0xa0be5aae.png|thumb|center|Hub and Spoke Diagram]]
 
 
=== Supported Formats ===
 
{| class="wikitable"
 
! Format !! Type !! Description !! Wikidata Entry
 
|-
 
| '''Ledger Book YAML/JSON''' || '''Hub''' || Main format of pyNomina for converting between formats. || [https://www.wikidata.org/wiki/Q281876 Ledger Book]
 
|-
 
| Beancount || Spoke || A plaintext accounting format. || [https://www.wikidata.org/wiki/Q130456404 Beancount]
 
|-
 
| GnuCash XML || Spoke || An XML-based format used by GnuCash. || [https://www.wikidata.org/wiki/Q130445392 GnuCash]
 
|-
 
| Microsoft Money || Spoke || Zip File exported with mny_export script using mdb-tools || [https://www.wikidata.org/wiki/Q117428 Microsoft Money]
 
|-
 
| Finanzmanager Deluxe (QIF) || Spoke || A variant of QIF used by Finanzmanager Deluxe. || [https://www.wikidata.org/wiki/Q1822341 Finanzmanager Deluxe]
 
|-
 
| Quicken Interchange Format || Spoke || Quicken Interchange Format (QIF) || [https://www.wikidata.org/wiki/Q750657 Quicken]
 
|-
 
| pyNomina Banking ZV YAML || Spoke || A format for exporting banking data in YAML or JSON. || [https://www.wikidata.org/wiki/Q130438296 Banking ZV]
 
|}
 
  
 
== Ledger Book ==
 
== Ledger Book ==

Latest revision as of 04:58, 10 October 2024

see also https://github.com/WolfgangFahl/pynomina/issues/3


Ledger Book

The pynomina Ledger Book model consists of four main classes:

  • Book
  • Account
  • Transaction
  • Split

These are the necessary classes which work together to represent a comprehensive financial ledger records.

Class Structure

0xfec2cab6.png

Book

The Book class represents the main container for all financial data. It includes:

  • Basic information: name, owner, creation date, and source URL
  • Collections of accounts and transactions
  • Methods for managing accounts and transactions, including:
    • get_stats(): Retrieves statistics about the book
    • filter(): Filters transactions by date range
    • create_account(): Creates a new account
    • add_account(): Adds an account to the book
    • lookup_account(): Finds an account by ID

Account

The Account class represents a hierarchy of individual financial accounts within the ledger. It includes:

  • account_id: Unique identifier for the account
  • name: Human-readable account name
  • account_type: Type of account (e.g., EXPENSE, INCOME)
  • description: Optional account description
  • currency: Account currency (default: EUR)
  • parent_account_id: Optional parent account for hierarchical structure

Transaction

The Transaction class represents individual financial transactions. It includes:

  • isodate: Date of the transaction in International Standards Organization date format yyyy-mm-dd
  • description: Description of the transaction
  • splits: List of Split objects representing the movement of money
  • payee: Optional payee information
  • memo: Optional additional notes
  • total_amount(): Method to calculate the total transaction amount

Split

The Split class represents the individual components of a transaction, showing how money moves between accounts. It includes:

  • amount: The amount of money involved in the split
  • account_id: The account associated with this part of the transaction
  • memo: Optional notes for this split
  • reconciled: Boolean indicating if the split has been reconciled

Example Data

Here's an example of how the ledger model is used in practice:

Yaml Format

owner: Wolfgang Fahl
url: https://github.com/WolfgangFahl/pynomina/blob/main/nomina_examples/expenses2024.yaml
since: 2024-10-06
accounts:
  Expenses:
    account_id: Expenses
    name: Expenses
    account_type: EXPENSE
    description: 'General Expenses'
    currency: EUR
  Expenses:Food:
    account_id: Expenses:Food
    name: Dining
    account_type: EXPENSE
    description: 'Expenses for Food'
    currency: EUR
    parent_account_id: Expenses
  Cash in wallet:
    account_id: Wallet
    name: Cash in Wallet
    account_type: EXPENSE
    description: ''
    currency: EUR
transactions:
  Bakery2024-10-06_0900_1:
    isodate: '2024-10-06'
    description: Bread
    splits:
      - amount: -3.50
        account_id: Cash in Wallet
      - amount: 3.50
        account_id: Expenses:Dining
        memo: Fresh sourdough bread
  Bakery2024-10-06_0900_2:
    isodate: '2024-10-06'
    description: Buns for Breakfast
    splits:
      - amount: -2.40
        account_id: Cash in Wallet
      - amount: 2.40
        account_id: Expenses:Dining
        memo: 4 whole grain buns

Implementation

The ledger model is implemented in Python, utilizing dataclasses and type hinting for clear and maintainable code. The `@lod_storable` decorator is used to enable easy serialization and deserialization of the data in YAML and/or JSON and other formats. The core idea is that the records should be readily available an tabular "list of dicts (lod)" format.