.. image:: https://github.com/jackmoody11/autodcf/workflows/Tests/badge.svg :target: https://github.com/jackmoody11/autodcf/actions
The autodcf package aims to make it extremely simple to create DCF models, simple and complex alike. If you are tired of running sensitivity tests to see how your model will behave with different sales growth patterns, this package is for you.
You can clone the repo from GitHub using
.. code::
git clone https://github.com/jackmoody11/autodcf
Setup the project by navigating to the project root directory and running
.. code::
python setup.py
There are a few important classes which will help you build your DCF.
The Company class encapsulates three classes: BalanceSheet, CashFlows,
and IncomeStatement. Once a Company object is created using the 3
financial statements, this can be passed to one of the DCF models (currently either SimpleDCF
or DCF).
.. code:: python
from autodcf.company import BalanceSheet, CashFlows, Company, IncomeStatement from autodcf.models import SimpleDCF
balance_sheet = BalanceSheet(assets=100, liabilities=50) cash_flows = CashFlows(capex=3) income_statement = IncomeStatement(sales=100, cogs=50, sga=25, rd=0, depreciation=4, amortization=2, interest=0, nonrecurring_cost=3, tax=4) company = Company(balance_sheet=balance_sheet, cash_flows=cash_flows, income_statement=income_statement, price_per_share=2.00, fully_diluted_shares=100) simple_dcf = SimpleDCF(change_in_nwc_to_change_in_sales=0.1, company=company, discount_rate=0.14, sales_growth=0.03, tax_rate=0.21, terminal_growth_rate=0.03, window=5) forecast = simple_dcf.forecast()
The code above will provide a filled out pandas DataFrame with estimated growth of different line items from the income statement.
io: Allow for DCF model to be exported to Exceldata: Allow users to specify what company and data source
to use and fetch necessary data for B/S, CF, and I/S behind the scenescompany: Allow for integration with financial data APIsmodels: Add different types of models (with/without synergies)