As you will see in later tutorials you can even control the versions of these libraries by creating your own virtual environments. When you install Ananconda you get immediate access to over 1500 Python libraries including NumPy, SciPy, Pandas, Beautiful Soup and Requests. Anaconda works well with Jupyter Notebooks.īy using IPyKernel you can quickly and easily hook up your virtual environments to your notebooks.Conda also allows you to create and manage your virtual environments. It can install from pre-built conda packages and it can build from source code. To quote their website Anaconda is a Python and R distribution that aims to provide everything you need (python wise) for data science tasks.Ĭonda is a package manager that allows you to install, upgrade and uninstall all your Python libraries. Anaconda comes with everything you need to get started analysing your data.The main reasons for this are discussed below. There is an excellent tutorial on for this method here.Ĭurrently we recommend using the Anaconda Python distribution by Continuum Analytics. If you are familiar with programming and installing software then you might prefer to install the Official Python Distribution. If you plan to use Python to explore algorithmic trading then this article will show you how to get an environment up and running in the simplest way. With that in mind it is better to choose the method based on how you intend to use the programming language.

There are many different approaches you can take to installing Python, and there are plenty of contradictory opinions on the best appraoch. XKCD's image for a typical Python Environment In fact there are now so many options for installing Python that it is easy to get confused. These days the problem is largely solved. Problems with GCC compilers, cross dependencies between libraries and operating system intricacies all played a role in making the job of installing Python much harder than it needed to be. In 2013 when the article was first written, installing Python was not a trivial task. In the early days of QuantStart we posted an article on setting up an Algorithmic Trading Research Environment with Ubuntu Linux and Python. This article has been updated to reflect the change. At time of writing Pandsa DataReader no longer supports Yahoo data due to an API change.

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