company-fundamentals 0.0.4

A package to standardize XBRL into fundamentals data

Company Fundamentals

A minimal python package to construct company fundamentals such as EPS, P/E, EBITDA, Gross Margin and more.

Installation

pip install companyfundamentals

Usage

Takes dictionaries with taxonomy and concept, then standardizes and calculates fundamental values. Powers the datamule project.

sample_simple_xbrl = [
{'taxonomy': 'us-gaap', 'name': 'NetIncomeLoss', 'value': '120000', 'period_start_date': '2024-01-01', 'period_end_date': '2024-12-31'},
{'taxonomy': 'us-gaap', 'name': 'NetIncomeLoss', 'value': '100000', 'period_start_date': '2023-01-01', 'period_end_date': '2023-12-31'},
]

fundamentals = construct_fundamentals(data=sample_simple_xbrl, taxonomy_key='taxonomy', concept_key='name, start_date_key='period_start_date', end_date_key='period_end_date', categories=None)
print(fundamentals)

Returns a dictionary of fundamentals

{'incomeStatement': {'netIncome': [{'value': '120000', 'period_start_date': '2024-01-01', 'period_end_date': '2024-12-31'}, {'value': '100000', 'period_start_date': '2023-01-01', 'period_end_date': '2023-12-31'}], 'netIncomeGrowth': [{'value': 0.2, 'period_start_date': '2024-01-01', 'period_end_date': '2024-12-31'}]}}

Use categories to subset what fundamentals you would like to construct

fundamentals = construct_fundamentals(data=sample_simple_xbrl, taxonomy_key='taxonomy', concept_key='name, start_date_key='period_start_date', end_date_key='period_end_date', categories=['incomeStatement'])

Package Design

1. Mappings are stored as a dictionary in mappings.py. This is used to standardize different xbrl reporting taxonomies.
2. Calculations are stored as a dictionary in calculations.py. This is used to determine how fundamentals are calculated.

TODO

1. Bug testing
2. More Fundamentals
3. Performance Improvements