Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/simfin/simfin-tutorials

Tutorials for SimFin - Simple financial data for Python
https://github.com/simfin/simfin-tutorials

finance financial-data google-colab notebook python simfin

Last synced: 4 days ago
JSON representation

Tutorials for SimFin - Simple financial data for Python

Awesome Lists containing this project

README 

        
# SimFin Tutorials



[Original repository on GitHub](https://github.com/simfin/simfin-tutorials)



## Introduction



[SimFin](https://github.com/simfin/simfin) is a database with financial

data such as Income Statements, Balance Sheets and Cash Flow Statements,

along with a simple Python API for downloading and using the data. These

tutorials show how to use the SimFin API and data.



## Videos



There is a [video](https://www.youtube.com/watch?v=nCY8a0KeeAE)

on YouTube with an overview of these tutorials,

and [another video](https://www.youtube.com/watch?v=zstXCc8iV2U)

on how to backtest and optimize a stock-screener based on Tutorial 7.



## Tutorials



1. Basics ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/01_Basics.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/01_Basics.ipynb))

2. Resampling ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/02_Resampling.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/02_Resampling.ipynb))

3. Growth & Returns ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/03_Growth_Returns.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/03_Growth_Returns.ipynb))

4. Signals ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/04_Signals.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/04_Signals.ipynb))

5. Data Hubs ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/05_Data_Hubs.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/05_Data_Hubs.ipynb))

6. Performance Tips ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/06_Performance_Tips.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/06_Performance_Tips.ipynb))

7. Stock Screener ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/07_Stock_Screener.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/07_Stock_Screener.ipynb))

8. Statistical Analysis ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/08_Statistical_Analysis.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/08_Statistical_Analysis.ipynb))

9. Machine Learning ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/09_Machine_Learning.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/09_Machine_Learning.ipynb))

10. Neural Networks ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/10_Neural_Networks.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/10_Neural_Networks.ipynb))



There is also a collection of small recipes ([Notebook](https://github.com/simfin/simfin-tutorials/blob/master/Small_Recipes.ipynb)) ([Google Colab](https://colab.research.google.com/github/simfin/simfin-tutorials/blob/master/Small_Recipes.ipynb))



## Downloading



If you want to run these tutorials on your own computer, then it is

recommended that you download the whole repository from GitHub,

instead of just downloading the individual Python Notebooks.



### Git



The easiest way to download and install this is by using git from the command-line:



    git clone https://github.com/simfin/simfin-tutorials.git



This creates the directory `simfin-tutorials` and downloads all the files to it.



This also makes it easy to update the files, simply by executing this

command inside that directory:



    git pull



### Zip-File



You can also [download](https://github.com/simfin/simfin-tutorials/archive/master.zip)

the contents of the GitHub repository as a Zip-file and extract it manually.



## Installation



If you want to run these tutorials on your own computer, then it is best

to use a virtual environment when installing the required packages,

so you can easily delete the environment again. You write the following

in a Linux terminal:



    virtualenv simfin-env



Or you can use [Anaconda](https://www.anaconda.com/download) instead of a virtualenv:



    conda create --name simfin-env python=3



Then you switch to the virtual environment and install the required packages.



    source activate simfin-env

    pip install -r requirements.txt



When you are done working on the project you can deactivate the virtualenv:



    source deactivate



## How To Run



Once you have installed the required Python packages in a virtual environment,

you run the following command from the `simfin-tutorials` directory to view

and edit the Notebooks:



    source activate simfin-env

    jupyter notebook



### Run in Google Colab



If you do not want to install anything on your own computer, then the Notebooks

can be viewed, edited and run entirely on the internet by using

[Google Colab](https://colab.research.google.com).



You can click the "Google Colab"-link next to the tutorials listed above.

You can view the Notebook on Colab but in order to run it you need to login using

your Google account.



All the required Python packages should already be installed on Google Colab,

except for simfin which you can install by executing the following command

at the top of the Notebook:



    !pip install simfin



If that is insufficient, then you can clone this entire GitHub repository

to your Google Colab account, and execute the following commands at the

top of the Notebook, to install all requirements:



    # Clone the repository from GitHub to Google Colab's temporary drive.

    import os

    work_dir = "/content/simfin-tutorials/"

    if not os.path.exists(work_dir):

        !git clone https://github.com/simfin/simfin-tutorials.git

    os.chdir(work_dir)

    

    # Install the required Python packages.

    !pip install -r requirements.txt



Note that you will need to run this every time you login to Google Colab.



## Testing



All the Notebooks can be run automatically and tested for errors. This is

particularly useful for developers who are making changes to the simfin

package, because it complements the unit-tests and data-tests with more

realistic use-cases.



First you need to install [nbval](https://pypi.org/project/nbval/):



    pip install nbval



Then you can execute all the Notebooks and test them for errors by running

the following command from the directory where the Notebooks are located:

 

    pytest --nbval-lax -v



Note that this will only test for errors and exceptions. It will not test

whether the new output matches the old output found in the Notebooks,

because the datasets are continually updated.



## License (MIT)



This is published under the

[MIT License](https://github.com/simfin/simfin-tutorials/blob/master/LICENSE.txt)

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](https://github.com/simfin/simfin-tutorials).