SimFin - Simple financial data for Python
SimFin makes it easy to obtain and use financial and stock-market data in Python. It automatically downloads share-prices and fundamental data from the SimFin server, saves the data to disk for future use, and loads the data into Pandas DataFrames.
Once the simfin package has been installed (see below), the following Python program will automatically download all Income Statements for US companies, and print the Revenue and Net Income for Microsoft.
import simfin as sf from simfin.names import * # Set your API-key for downloading data. # If the API-key is 'free' then you will get the free data, # otherwise you will get the data you have paid for. # See www.simfin.com for what data is free and how to buy more. sf.set_api_key('free') # Set the local directory where data-files are stored. # The dir will be created if it does not already exist. sf.set_data_dir('~/simfin_data/') # Load the annual Income Statements for all companies in USA. # The data is automatically downloaded if you don't have it already. df = sf.load_income(variant='annual', market='us') # Print all Revenue and Net Income for Microsoft (ticker MSFT). print(df.loc['MSFT', [REVENUE, NET_INCOME]])
This produces the following output:
Revenue Net Income Report Date 2008-06-30 6.042000e+10 17681000000 2009-06-30 5.843700e+10 14569000000 2010-06-30 6.248400e+10 18760000000 2011-06-30 6.994300e+10 23150000000 2012-06-30 7.372300e+10 16978000000 2013-06-30 7.784900e+10 21863000000 2014-06-30 8.683300e+10 22074000000 2015-06-30 9.358000e+10 12193000000 2016-06-30 9.115400e+10 20539000000 2017-06-30 9.657100e+10 25489000000 2018-06-30 1.103600e+11 16571000000 2019-06-30 1.258430e+11 39240000000
We can also load the daily share-prices and plot the closing share-price for Microsoft (ticker MSFT):
# Load daily share-prices for all companies in USA. # The data is automatically downloaded if you don't have it already. df_prices = sf.load_shareprices(market='us', variant='daily') # Plot the closing share-prices for ticker MSFT. df_prices.loc['MSFT', CLOSE].plot(grid=True, figsize=(20,10), title='MSFT Close')
This produces the following image:
The best way to install simfin and use it in your own project, is to use a virtual environment. You write the following in a Linux terminal:
You can also use Anaconda instead of a virtualenv:
conda create --name simfin-env python=3
Then you can install the simfin package inside that virtual environment:
source activate simfin-env pip install simfin
If the last command fails, or if you want to install the latest development version from this GitHub repository, then you can run the following instead:
pip install git+https://github.com/simfin/simfin.git
Now try and put the above example in a file called
test.py and run:
When you are done working on the project you can deactivate the virtualenv:
If you want to modify your own version of the simfin package, then you should clone the GitHub repository to your local disk, using this command in a terminal:
git clone https://github.com/simfin/simfin.git
This will create a directory named simfin on your disk. Then you need to create a new virtual environment, where you install your local copy of the simfin package using these commands:
conda create --name simfin-dev python=3 source activate simfin-dev cd simfin pip install --editable .
You should now be able to edit the files inside the simfin directory and
use them whenever you have a Python module that imports the simfin package,
while you have the virtual environment
Two kinds of tests are provided with the simfin package:
Unit-tests ensure the various functions of the simfin package can run without raising exceptions. The unit-tests generally do not test whether the data is valid. These tests are mainly used by developers when they make changes to the simfin package.
The unit-tests are run with the following commands from the root directory of the simfin package:
source activate simfin-env pytest
Data-tests ensure the bulk-data downloaded from the SimFin servers is valid. These tests are mainly used by SimFin's database admin to ensure the data is always valid, but the end-user may also run these tests to ensure the downloaded data is valid.
First you need to install nbval, which enables support for Jupyter Notebooks in the pytest framework. This is not automatically installed with the simfin package, so as to keep the number of dependencies minimal for normal users of simfin. To install nbval run the following commands:
source activate simfin-env pip install nbval
Then you can run the following commands from the root directory of the simfin package to execute both the unit-tests and data-tests:
The following command only runs the data-tests:
pytest --nbval-lax -v tests/test_bulk_data.ipynb
provide more realistic use-cases of the simfin package, and they can
also be run and tested automatically using
pytest. See the tutorials'
README for details.
This is published under the MIT License which allows very broad use for both academic and commercial purposes.
You are very welcome to modify and use this source-code in your own project. Please keep a link to the original repository.