A definitive guide to learning Python for Algorithmic Trading

  • Financial data: Financial data forms the core of each and every algorithmic trading project. Python offers a number of packages that do a great job in handling and working with structured financial data of any kind (intraday, end-of-day, high-frequency, you name it.). Let’s look at some such packages and libraries that are extensively build to ease down your workload while working with unstructured financial data:
  • numpy — Numpy is the most fundamental library for scientific computing using Python. It is used for numerical programming and finds an extensive use in finance as well as academia.
  • scipy — SciPy supplements the popular Numeric module, Numpy. It is a Python-based ecosystem of open-source software for mathematics, science, and engineering. It is also used extensively for financial and scientific computations.
  • pandas — The pandas library offers easy-to-use, high-performance data structures for data analysis. Pandas focus on the fundamental data types and their methods, leaving other packages to add more sophisticated statistical functionality
  • Real-time data: Algorithmic trading requires dealing with fast in-coming real-time data. This involves a little bit of socket programming (preferably with ZeroMQ). Python comes in extremely handy while visualizing this real-time data to derive actionable insights. Here are some visualization libraries that help in analyzing this real-time data:
  • matplotlib: It is the O.G. of Python data visualization libraries. Although it is over a decade old, it is still most extensively used for plotting. It was designed to closely resemble MATLAB. Being the first visualization library, matplotlib supports various other packages and libraries that are built on top of it or are designed to work in tandem with it.
  • seaborn: It harnesses the power of matplotlib to create beautiful and aesthetically pleasing charts in a few lines of code. Seaborn offers various default styles and color pallets, but since it is built on top of matplotlib, it is recommended to have a fair idea of the working of matplotlib to tweak seaborn’s defaults.
  • Online platforms: Trading isn’t possible without a trading platform. If you’re looking to get going with algorithmic trading using Python, you’re expected to have an idea of the various trading platforms, to choose which one is the best for you. The most popular trading platforms are:
  • Automation: Automation is what makes the algorithmic trading beautiful as well as challenging. You should be well versed with how to deploy Python in the cloud and how to set up an environment appropriate for automated, algorithmic trading. Let’s look at a couple of such automated hosting platforms that are extensively used today:
  • Quantiacs: Quantiacs is an open-sourced Python platform which provides a toolbox for you to develop and backtest your trading ideas. It also offers free and clean financial data and allows you to develop as many strategies as you want, and the most profitable ones can be submitted in the Quantiacs algorithmic trading competitions.
  • Quantopian: Quantopian is another popular open source python platform for testing and developing trading ideas and strategies. It allocates capital for selected trading algorithms and you get a share of your algorithm’s net profit. It is also supported by an extremely active community wherein trading ideas and problems get discussed among the members.




We are a 501(c)(3) non-profit organization dedicated to inspiring women to excel in technology careers. https://www.womenwhocode.com/

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Set the goal and prepare for it.

Your own language in your favourite IDE. Thanks to LSP.

Web Scrapping using Python

The power of Python method Decorator!

How to Choose Load Balancers based on UseCases

Kubernetes Integration with Python-CGI

Learn By Doing — build 25 websites and real mobile apps using HTML, CSS, Javascript, PHP, Python…

Hotpot Fund Weekly Report NO.17

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Women Who Code

Women Who Code

We are a 501(c)(3) non-profit organization dedicated to inspiring women to excel in technology careers. https://www.womenwhocode.com/

More from Medium

How to make an after-hours market scanner with Selenium and Yahoo

Using Python to scrape Wikipedia for World Cup 2022 match information

Introduction to the Basics of Python 3-Part 19

How to predict Football Games with Python (Kinda)