Quandl: A Step-by-Step Guide – AlgoTrading101 Blog

survive Updated on May 14, 2021

Table of Contents

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What is Quandl?

Quandl is a platform that provides its users with economic, fiscal and alternative datasets.

Users can download free data, buy paid data or sell data to Quandl .
In this guide, we will cover how to extract data using Excel and Python ( individually ) .

What is Quandl’s Data Structure?

Quandl ’ second data comes in two formats : the first one being Time-series and the second one being Tables .
Have in judgment that some datasets can be shown in both formats .
Time-series
Time-series data refer to data taken over a menstruation of time .
Time-series are normally displayed using credit line graph, where the x-axis represents dates and the y-axis represents other numeric observations .
Example Quandl time series data
The Time-series format only contains numeral data which is indexed by one date field. This means that the data is sorted by dates. This allows users to retrieve the integral time-series or a detail depart of it, besides called a “ slice ” .
Most Quandl datasets are stored in the Time-series format as fiscal data largely consist of dates and observations which fit the format nicely .
Table format
Tables don ’ thyroxine only contain numeral values .
They can contain diverse uncategorized data types such as strings, numbers, dates, etc. They can besides be filtered into different fields .
Example chart of Quandl table data
Quandl ID code
Each feed of data has a short ID, besides known as a Quandl code .
For example :

  • Federal Reserve Economic Data (time-series): FRED
  • Core US Fundamentals (tables): SF1

Time-series data such as FRED is built-up of a big number of individual time-series where each of them has its own Quandl code appended to the main Quandl code, for exercise : uracil civilian unemployment rate : FRED/UNRATE .
On the other hand, Tables data such as SF1 contain one or more tables where each of them has its own Quandl code, for exercise : The Quandl code for the Core US Fundamentals table is SHARADAR/SF1 .
When it comes to Data Analysis tools, Quandl directly supports API, R, Python, Excel, Ruby and more .
The entire tilt can be found hera : hypertext transfer protocol : //www.quandl.com/tools/full-list

What are the Pros and Cons of Quandl?

The Pros:

  • Quandl offers an enormous collection of data (over 20 million datasets).
  • All datasets are available for instantaneous download in any preferred format.
  • All datasets on Quandl are available through the same API, irrespective of who originally published the data or in what format.
  • Data is transparent.
  • Datasets are easy to find and clean.
  • Some parts of Quandl is free and open for everyone.
  • New data is added weekly.
  • Quandl can be used in many programs (Excel, Python, R, Ruby, MATLAB…)
  • You can use Quandl to sell your data.

The Cons:

  • Many of the more exotic datasets are not free.
  • They offer limited amounts of help when constructing analyses or discovering information.
  • Not too beginner friendly.
  • They don’t have real-time or delayed stock price data.
  • They don’t have a master security list.

» You can use the download data to find suitable assets for Pairs trade.

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Is Quandl Free?

Quandl offers both barren and premium products .
The Quandl API is rid to use and grants access to all barren datasets .
Quandl users have to pay to access Quandl ’ second agio data products .

What are the differences between Free and Premium datasets?

As the monster is in the details, their release products come from respective “ credible and established ” sources such as central banks, government agencies, organizations, etc .
On the early pass, their bounty products come from data providers such as : AlgoSeek, Applied Research, Barchart, Brave New Coin, and CryptoCompare etc .
The full list can be seen hera : hypertext transfer protocol : //www.quandl.com/publishers
Quandl bounty datasets have a regular update time while there are no guarantee update times for release datasets and they can normally take anywhere from a few hours to weeks, months or years ( depending on the data itself ) .
Quandl ’ south premium data is curated and maintained by professional providers with decades of expertness under their belts. This all adds on the improvement of documentation, correctness, organization, singularity and structure of the data. hera we can see the domination of the bounty data .
Premium data on Quandl can be purchased via subscriptions. With them you subscribe lone to especial datasets that you need. This means that you entirely pay for what you need. Have in mind that there is no fee to use the platform itself or Quandl ’ s afford data feeds .
about all premium datasets come with some free sample data. To see a release sample data, you need to log in to your Quandl account. After clicking on any bounty data feed of your concern, the web site will take you to the home page of that feed where you will be presented with some sample data .
It needs to be mentioned that every single free data feed has a guileless radio link that leads to its reservoir. This means that we can verify the data and the source it comes from .

What are the types of Free Data in Quandl?

When it comes to detached data, Quandl offers you a massive range from Prices & volume, Fundamentals, Sentiment, Derived Metrics, National Statistics, and early .
Let ’ s citation some of the rid datasets :

  • Wiki Continuous Futures – This dataset is built on top of raw data from ICE, CME, LIFFE, etc, and is curated by the Quandl community.
  • Zillow Real Estate Data – This dataset is the leading real estate and rental marketplace.
  • Federal Reserve Economic Data – This dataset includes things such as growth, employment, inflation, labor, manufacturing and many more US economic data.

What are the types of Paid Data in Quandl?

When it comes to premium data, Quandl offers datasets related to Estimates, Corporate Actions, Alternative Data, Technical Analysis and more .
Let ’ s mention some of the agio datasets :

  • Zacks Sales Estimates – This dataset has a consensus of sale estimates for over 4k US and Canadian listed companies.
  • Nasdaq Dorsey Technical Analysis Data – This dataset is made up of 50+ technical indicators and chart information for over 49k global equities, mutual funds and ETFs.
  • Sharadar Core US Equities and Fund Prices – This dataset is self-explanatory.

How to create an account in Quandl?

Having a Quandl account is significant as it allows you to :

  • Access their API, libraries and tools.
  • Download free and/or premium data in any format.
  • Access their export and visualization tools.
  • View relevant information about pricing and licensing for their premium data.

Let ’ s get you started with your Quandl report. Have in heed that it is complimentary and doesn ’ metric ton require any credit batting order data .
Let ’ s go over the steps :

  1. Go over to the Quandl website (https://www.quandl.com/) and click on the sign up button in the top right corner.
  2. A screen will open that requires your first and last name, as well as your purpose of using Quandl (business, academic or personal). So feel free to input those fields. After that, click on “Next”.
  3. Now, another screen will appear where you will input your email address and answer a question on “How will you be using this data?” After that, click on “Next”.
  4. The final screen of your signing up process will appear where you will be asked to create a password. After you have created your password and checked the terms of service box, click on “Create Account”.
  5. Now, a welcome screen will appear with your API key.

Wait Igor, what in the populace is this API winder ? ! You didn ’ metric ton prepare us for that !
Calm down, be sure to copy your key and don ’ metric ton forget to verify your e-mail address. If you pressed “ Continue ” without copying the API key, it ’ s not an issue. You can find your key in the Account Details section .
now let ’ s explain what an API winder is !
API key stands for the Application Programming Interface key and is represented as a string of code that gets passed in by a computer to an application. The program or lotion then uses the API key to identify the user to a web site .
The API key is a “ hidden ” authentication token arsenic well as a unique identifier .
think of it as your personal secret “ password ” for collecting data from Quandl .

How to access Quandl Data?

The data can be accessed using Excel, Python, R, MATLAB, Ruby and more .
We will cover the Excel and Python methods in this article .

How to extract Quandl Data in Excel?

When it comes to Excel, you can pull Quandl datasets in a count of minutes without any programming skills required .
It ’ sulfur time for us to see Quandl in action. But first, we need to set up the Quandl Plugin for Excel .
Let ’ s do the steps together :

  1. Go to this link (https://www.quandl.com/tools/excel) and click the blue “Download” button.
  2. Now, click on the downloaded “Quandl Excel Addin” and follow the installation steps.
  3. After the installation, open up your Excel program and click on the newly appeared “Quandl” in your toolbar.
  4. After that, click on the settings button and insert your API key in the newly opened window and click save (as shown in the picture below)

Example of Quandl usage in Excel

  1. Let’s get our Database. Press the “Get Data” button inside of your Quandl toolbar.
  2. After your API key gets verified (remember that?), we’ll select our Data. For this example, I’ll pick the European Stock Prices for FSE Frankfurt Stock Exchange.


3. Click the blue “ Next ” clitoris and select your preferable stock. I ’ ll use the Zooplus ( ZO1_X ). Click “ Next ” .

4. After that, a new window will open that will ask you to specify what Columns will your dataset have. I selected the Date, Open, High, Low and Close column. Click “ Next ”.


5. nowadays, we ’ ll select the filters that we want for our data. As I
preceptor ’ thymine want to make the steps besides complicated, I won ’ thymine apply any filters .
6. After clicking the “ Next ” clitoris, you are given a Placement option aka where do you want your data to be placed at in your Excel tabloid. When you specify your Excel Sheet and Cell, click the “ Insert ” button .

7. Voilà ! Your data is ready ! If it didn ’ metric ton appear for you, don ’ thyroxine panic ! Just press the “ Refresh Sheet ” button that is found in your Quandl toolbar .

For the future exercise, the steps are the like but the dataset will be different. I ’ ll show you a Corporate Annual Report data for the Facebook Inc. dataset :

The next example will be different. We ’ ll use the Quandl “ Formulas ” option to code what we need and want .
Let ’ s follow the steps :
1. Let ’ s speculate that we want to download the daily conclusion prices for two assets in the last 3 days ( as shown in the mental picture ). First we need to specify that we want the close prices by writing the following string ( = ’ your cell ’ & ” /CLOSE ” ) in our formula Excel section. Be sure to do this for both assets. You can simply drag the cell with the written code to the early cell .

2. now, let ’ s add our Start and End dates. For this I ’ ll use the “ nowadays ( ) ” convention in Excel. For the Start date we will write “ today ( ) -3 ” which means past 3 days and for the end date we will just use “ today ( ) ” .

3. This tone requires us to use the “ qseries ” recipe which, in our exemplar, looks like this “ =QSERIES ( D2 : E2, B1 : B2 ) ” As you can see, the D2 : E2 specifies our asset trickle and the B1 : B2 part specifies our Start and End date. After the formula is written, refresh the tabloid as we did in our past exercise, and you ’ re done .


If you want to change gorge up, and front at the data for the past 3 years, in the Start date field write “ =today ( ) -365*3 ”. Be sure to play with Quandl so you get a bent of it !

How to extract Quandl data in Python?

For this part, I ’ ll be using the Jupyter Notebook from the Anaconda lotion for Data Science .
More info on how to set up your Jupyter notebook : Preparing Your Python Coding Environment
first, we need to install the Quandl software for Python. For this, use the dominate “ blip install quandl ” .

Let ’ s show a few examples :

Example 1: WTI Crude Oil prices

For this exemplar, we want to get the WTI Crude Oil price from the United States department of Energy :
1. We need to import all the packages we ’ re going to use. For this example, I ’ ll import Quandl, NumPy and Pandas. eminence that you need to have NumPy and Pandas installed. They come pre-installed with the Anaconda program and the codes go like this : “ shoot install NumPy ” and “ pip install Pandas ” .
2. After that, we need to activate our API key. Use the follow command : quandl.ApiConfig.api_key = “ YOUR_API_KEY ”

3. The following step is to use the following command “ mydata = quandl.get ( “ EIA/PET_RWTC_D ” ) ” .
It is of utmost importance to know the Quandl code ( “ EIA/PET_RWTC_D ” ) of each dataset you want to use .
You can find the Quandl code for each dataset under the software documentation page of each dataset. case : hypertext transfer protocol : //www.quandl.com/data/EIA-U-S-Energy-Information-Administration-Data/documentation

If you want to get the lapp datum, but as a NumPy array, you can use this code : mydata = quandl.get ( “ EIA/PET_RWTC_D ”, returns= ” numpy ” )

Example 2: US GDP Data

For our next example, let ’ s download a firearm of macroeconomic data with specific dates and save it as a .csv charge .
Downloading data from a specific date range in Quandl

Example 3: Facebook Annual Report Data

Let ’ s download a Corporate Annual Report for Facebook. The steps are the same as in our first Python example, but with a little writhe. Our Quandl code for this one is “ WIKI/FB ” .
We specify the years we want to go through and get the data we ’ ll indigence to preprocess it. This means that we need to extract lone the annual share of it .
For this, we shall use the follow command : mydata = quandl.get ( “ WIKI/FB ”, collapse= ” annual ” )
now our data looks like this :

now you are all equipped to go on a Quandl coding journey in Python !
“May the code be with you!” – Obi Wan Kenobi

Quandl in other programming languages

Quandl is besides equipped for early programming languages like R, MATLAB, Ruby and others .
In this article, we won ’ metric ton go into them as it would make it excessively long. But R, Ruby and MATLAB enthusiasts worry not, as I ’ ll provide you with utilitarian links where you can learn how to use Quandl in your programs !
R – hypertext transfer protocol : //www.quandl.com/tools/r
Ruby – hypertext transfer protocol : //github.com/quandl/quandl-ruby
MATLAB – hypertext transfer protocol : //github.com/quandl/Matlab

What are the limitations for Quandl downloads?

Quandl needs to limit their users when it comes to downloading so the API doesn ’ deoxythymidine monophosphate get over flooded and the whole service crashing down .
anonymous users are limited to 20 calls per 10 minutes and 50 calls per day ; while authenticated users have a specify of 300 calls per 10 seconds, 2k calls per 10 minutes and 50k calls per day .
When it comes to the loose authenticate users, they have a concurrence limit of one which means that they can alone make one call at a time and have an extra call wait in the queue .
As for the premium users, they have a limit of 5k calls per 10 minutes and a limit of 720k calls per sidereal day. They are besides limited to 10 bulk downloads per hour .

What are some Quandl alternatives?

When it comes to price and corporate fundamentals, there are the be : yahoo finance, IEX, Alpha Vantage, Polygon, TradingView…
More info : Google Finance API and 9 Alternatives
When it comes to Alternative data, there are the adopt : M Science, Advan, Thinknum, YipitData, Tegus, Arch Metrics and many more .
here is the full list – hypertext transfer protocol : //alternativedata.org/data-providers/

More on Quandl’s background

According to Quandl, they have over 400,000 users. These ranges from the world ’ randomness top hedge funds to investment banks and assorted asset managers .
Quandl ’ randomness specialization is in the field of data that gets collected over time and is categorized as economic, sociable and fiscal data – this includes things like : stock prices, crime rates, unemployment rates, respective populations, etc .
They manage to do this by indexing millions of numerical datasets from around the internet. For exemplar, if you click on a specific dataset that is indexed in Quandl, it will go to its original dataset informant, extract the newest version of it, clean it, and give it back to you in whatever shape you want .
Quandl is striving for the democratization of data and making it easier to find and use for all its users. Their goal is to provide their service to versatile types of users and professions .
Can you believe that they even offer data pertained to some basic sport statistics ? !
Ok Igor, that all sounds sweet, but how does Quandl acquire data ?
well, Quandl acquires its data by having versatile homo curators that point their bots at particular datasets. recently, they ’ ve been looking for more curators that are similar to Wikipedia ’ s volunteer editors .
You can besides become a seller and sell your data to Quandl. For this, the technical requirements aren ’ metric ton hard-and-fast but you need to create, text file and maintain the data yourself with a “ professional standard of quality ” .
The good thing is that the list of your data to Quandl doesn ’ t come with any fees. You besides get back from the Quandl team for free and your data is viewed as your intellectual property .
As a Quandl seller, you have full transparency of who ’ s buying/accessing your data. You are besides provided with a seller splashboard that shows you all the sales, subscribers, revenues and other information in real-time.

Read more: Forum Policy

Download Code

Download the Python code used : hypertext transfer protocol : //github.com/Lucas170/Quandl-Guide-AlgoTrading101

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