Build discounted cash flow (DCF) models with ease. Allows for advanced manipulation of future growth for more accurate equity valuations.
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
git clone https://github.com/jackmoody11/autodcf
Setup the project by navigating to the project root directory and running
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).
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,
company = Company(balance_sheet=balance_sheet,
simple_dcf = SimpleDCF(change_in_nwc_to_change_in_sales=0.1,
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.
Add docs and deploy using doctr to GitHub Pages site (automate deployment with Travis CI)
Publish package to PyPi
io: Allow for DCF model to be exported to Excel
data: Allow users to specify what company and data source to use and fetch necessary data for B/S, CF, and I/S behind the scenes
company: Allow for integration with financial data APIs
models: Add different types of models (with/without synergies)
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.