https://img.shields.io/pypi/v/quandl_fund_xlsx.svg https://img.shields.io/travis/robren/quandl_fund_xlsx.svg Documentation Status Updates

A unofficial CLI tool which uses the Quandl API and the Sharadar Essential Fundamentals Database to extract financial fundamentals, Sharadar provided ratios as well as calculate additional ratios. Results are written to an Excel Workbook with a separate worksheet per ticker analysed.


For a given ticker, fundamental data is obtained using the Quandl API and the Sharadar Fundamentals database. This data is then used to calculate various useful, financial ratios. The ratios include

  • Profitability indicators
  • Financial leverage indicators
  • Free and Operating Cash flow indicators.

Some REIT specific ratios such as FFO are very roughly approximated. These specific ratios are only roughly approximated since certain data, namely Real estate sales data for the period does not appear to be available via the API (It’s often buried in the footnotes of these companies filings).

The output excel worksheet for each ticker processed is divided into three main areas:

  • Sharadar statement indicators. This is data obtained from the three main financial statements; the Income Statement, the Balance Sheet and the Cash Flow Statement.
  • Sharadar Metrics and Ratio Indicators. These are quandl provided financial ratios.
  • Calculated Metrics and Ratios. These are calculated by the package from the Sharadars data provided and tabulated by the statement indicators and the ‘Metrics and Ratio’ indicators.

The python Quandl API provides the ability to return data within python pandas dataframes. This makes calculating various ratios as simple as dividing two variables by each other.

The calculations support the data offered by the free sample database (formerly referred to by Sharadar as the SF0 database), and the paid for SF1 database. The coverage universe is the same for both the sample data and the paid database. The key difference being, support as well as a much richer set of so-called Dimensions (timeperiods). For example the sample data is taken from the annual filings of companies, whereas the paid data allows for Trailing Twelve Month as well as quarterly data.

Note: For quarterly data, many of the ratios using income and cash flow statement values in the numerator will be inaccurate when using quarterly data e.g EBITDA/Intereset expense or Total Debt/ Cash Flow from Operations.


The generated Excel workbook with one sheet per ticker.


Some bespoke metrics and ratios calculated based on Sharadar fundamentals.


pip install quandl_fund_xlsx


You will need a Quandl API key. This maybe obtained by signing up, for free at Quandl Signup. The key will then be available under “profile” when logging into Quandl. This key allows for access to sample data for many of the datasets.

If you have have a key for the free sample data set the QUANDL_API_SF0_KEY environment variable to the value of your key.

If you have paid for access to the Sharadar fundamentals data set, then set the QUANDL_API_SF1_KEY in the environment.

export QUANDL_API_SF0_KEY='YourQuandlAPIKey'


export QUANDL_API_SF1_KEY='YourQuandlAPIKey'

For windows the setx command is used to set environment variables..

Usage of the quandl_fund_xlsx CLI command

quandl_fund_xlsx -h

quandl_fund_xlsx (-i <ticker-file> | -t <ticker>) [-o <output-file>]
                                                                [-y <years>] [-d <sharadar-db>]
                            [--dimension <dimension>]

quandl_fund_xlsx.py (-h | --help)
quandl_fund_xlsx.py --version

-h --help             Show this screen.
-i --input <file>     File containing one ticker per line
-t --ticker <ticker>  Ticker symbol
-o --output <file>    Output file [default: stocks.xlsx]
-y --years <years>    How many years of results (max 7 with SF0) [default: 5]
-d --database <database>    Sharadar Fundamentals database to use, SFO or
                                                        SF1 [default: SF0]
--dimension <dimension>     Sharadar database dimension, ARY, MRY, ART, MRT [default: MRY]
--version             Show version.
quandl_fund_xlsx -t INTC -o intc-MRY.xlsx
{'--database': 'SF0',
'--input': None,
'--output': 'INTC-MRY.xlsx',
'--ticker': 'INTC',
'--years': '5'}
('Ticker =', 'INTC')
2017-08-22 06:08:59,751 INFO     Processing the stock INTC
2017-08-22 06:09:06,012 INFO     Processed the stock INTC

ls -lh excel_files
total 12K
-rw-rw-r-- 1 test test 8.7K Aug 22 06:09 intc-MRY.xlsx

Local Development

This section is only of relevance if you wish to hack on the code yourself, perhaps to add new ratios or display other Sharadar provided data values.

It’s recommended to setup a virtual environment and perform the installation within this. Use pip to install the requirements but not the package.

pip install -r requirements_dev.txt

# Run the CLI by running as a module
python -m quandl_fund_xlsx.cli -t MSFT

# Run the tests

If you wish to install the package locally within either a virtualenv or globally this can be done once again using pip.

pip install -e .

# Now the CLI is installed within our environment and should be on the
# path
quandl_fund_xlsx -t MSFT

How to get help contribute or provide feedback

See the contribution submission and feedback guidelines <ref-contributing>


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.