What is Python for Finance?

Python is one of the most popular programming languages in the world. Among its many uses, professionals in finance and finance technology use Python for its artificial intelligence, machine learning, data visualization, and other data analysis and data science capabilities. In this overview, you’ll learn more about what Python for finance is, what it can do, who uses it, and how to learn it so you can add this skill to your professional toolbox.

What Can You Do with Python for Finance?

Python is an open-source programming language that has been in use for over 30 years. This free-to-use programming language enjoys massive popularity thanks to its many uses. Python is used for web development, data science, data analytics, and more. In the finance industry, Python is used by Traders, Analysts, and Researchers, as well as companies like Stripe and Robinhood. Python’s simplicity and flexibility make it a popular programming language in the finance industry because it makes creating formulas and algorithms far easier than comparable programming languages. Python libraries and tools also make it easier to integrate programs with third parties, a common need in FinTech. 

Python’s analytics tools, such as the Pandas library, allow for the creation of data visualizations and interactive dashboards that reference large quantities of data. The Python libraries PyBrain and Scikit allow for machine learning algorithms that enable predictive analytics. You’ll find Python programming at work in cryptocurrency, stock trading, banking apps, and more.

How Do You Download/Get Python for Finance? How Much Does it Cost?

Python is an open-source programming language, meaning that it is freely available for anyone to use. You can download the latest version of Python through the nonprofit Python Software Foundation at their website. You will also find free documentation on the website including guides to help beginners start learning the Python programming language. The Python Software Foundation also manages a community of developers and volunteers who help suggest and implement improvements for the next version of Python. Python has been in use for over 30 years and therefore has a strong and active community willing to offer advice to those starting out with the language. 

Python for finance typically uses the following Python packages: 

• NumPy: the fundamental library needed for scientific computing with Python. NumPy adds support for large, multi-dimensional matrices and arrays. It also adds a large collection of high-level mathematical functions to manipulate data structures. NumPY is free to use.
• SciPy: a library that provides algorithms for scientific and technical computing. This library includes algorithms for integration, optimization, interpolation, linear algebra, special functions, ODE solvers, signal and image processing, and more. SciPy is free to use.
• Pandas: a library used for data analysis and manipulation, particularly through its data structures and operations. It is also used for manipulating time series and numerical tables. Pandas is free to use.
• statsmodels: this Python module enables the estimation of statistical models, performance of statistical tests, and exploration of statistical data. This package is free to use.
• Quandl: this platform offers financial, economic, and alternative datasets. Some data is free to download, but users can also buy and sell data. Quandl offers a free and premium version.
• Zipline: an algorithmic library for trading applications, and an event-driven system for backtesting. Zipline is free to use.
• Pyfolio: this library is used for risk and performance analysis of financial portfolios. Pyfolio is free to use.
• TA-Lib: this library is used to analyze historical stock market data to make predictions about future prices or market direction. Finance professionals use this information to plan their investment strategy accordingly. TA-Lib is free to use.
• Matplotlib: a comprehensive library for Python and NumPy. It is used to create animated, interactive, and static visualizations. Matplotlib is free to use.

Most of the libraries above are open-source, meaning they are available for public use at no charge. Due to their public accessibility, these libraries enjoy active communities of developers and programmers who find new ways to use and improve these Python libraries.

Anaconda is a distribution of the Python programming language for scientific computing. Some Anaconda instances are free to use while others are offered as software as a service. 

Python programmers can use the Jupyter Notebook, a free web application, to edit and run notebook documents. 

Google Research’s Colaboratory (“Colab” for short) permits anyone to write and execute arbitrary Python code to analyze and visualize data. Colab works well for data analysis and machine learning.

You can also use Visual Studio Code as a free Python source code editor.

What Are the Benefits of Learning Python for Finance?

Learning Python for finance can revolutionize how organizations process financial data. Python has numerous finance functionalities including analytics, banking software, stock trading strategy, and cryptocurrency. Pandas, a Python library, permits complex statistical analysis and simplifies the data visualization process. Other libraries like Scikit-learn and PyBrain enable solutions that use machine learning algorithms for predictive analytics, allowing for scientific financial forecasts. Python is also the programming language behind mobile banking apps and ATM software, cryptocurrency analysis, and stock trading based on analytical predictions. 

Python for finance serves as an important skill for certain career paths. Traders, Analysts, Quantitative Researchers, Finance Associates, Data Scientists, Software Engineers, and others in the finance industry can benefit by learning about Python’s finance industry uses.

Read more about why you should learn Python for finance. 

Python for Finance Careers

Python for finance is used by Financial Analysts, Risk Managers, and Portfolio Managers. It is also used in finance technology, also known as FinTech, by those who work with data, such as Data Scientists.

According to the U.S. Bureau of Labor Statistics (BLS), the median income for a Financial Analyst is around $95,000 annually. BLS predicts that demand for Financial Analysts will increase by 9% between 2021 and 2031, making this a great time to pursue this promising career. So what does a Financial Analyst do? Financial Analysts guide individuals and businesses regarding money matters for the purpose of attaining profit and achieving long-term financial stability. 

Financial Managers, such as Risk Managers and Portfolio Managers, make an average of $130,000 per year according to BLS. BLS also projects demand for this job will grow by 17% between 2021 and 2031. The exact nature of a Financial Manager’s role depends on their area of focus, but generally Financial Managers create reports, develop plans for meeting long-term financial goals, and direct investment activities.

How to Learn Python for Finance

Learning Python for finance involves the use of complex, advanced Python programming skills. Therefore, learning in an instructor-led course often proves the best outcomes. Instructor-led courses can meet in person or virtually. Both options provide a set meeting time and instant communication. This allows you to ask questions, receive personalized feedback, collaborate with classmates, and work through hands-on assignments in real-time. Some live online classes also come with additional benefits like one-on-one mentoring, portfolio and resume reviews, job search assistance, and flexible financing options. If you are new to Python programming, you’ll want to start with live online Python classes before advancing to live online FinTech classes. Students looking for in-person learning can search all Python classes with the Classes Near Me tool. 

Another way to learn Python for finance is through on-demand/self-paced courses. These courses work best for those who have difficulty meeting at the times offered by in-person and live online classes. Asynchronous classes permit you to choose the time and pace of your Python for finance training. Most on-demand/self-paced classes consist of pre-recorded video content with some textual content as well. Another advantage of on-demand classes is their affordability. Most online learning subscriptions cost between $30 and $60 per month. You can even find some free classes available. Explore and compare different on-demand Python classes to find which options work best for you. 

Noble Desktop offers free resources on learning Python. The Get Started in Data Science seminar offers a high-level overview of data science and its many professional uses. 

Read the full guide on how to learn Python for finance.

A Brief History of Python for Finance

Python was developed in the early 1990s and has enjoyed popularity around the world for more than 30 years. As an open-source, general-purpose programming language, Python serves as the programming language for web development, data analysis, data science, and more. This versatile programming language plays a large role in the finance industry and in finance technology (FinTech). Thanks to Python’s popularity and long history, you can easily find free resources such as documentation and active communities. 

Popular Python for finance libraries include Pandas and NumPy. Pandas was first released in 2008 with the intention of enabling superfast data analysis projects. NumPy was created by Travis Oliphant in 2005 as a Python library for working with arrays. Both of these Pythonic libraries are open-source and free to use.

Comparable Programming Languages for Finance

Professionals who use Python for finance can often benefit by expanding their skillset to include comparable programming languages. Java and SQL are two other programming languages often used in FinTech and the finance industry. This section will provide a detailed summary of how Java and SQL apply to finance and FinTech, and how learning Python compares to learning these other programming languages.

Python’s popularity in FinTech stems from the programming language’s free availability, simplicity, flexibility, and approachability. Programmers consider Python a beginner-friendly language even for those without prior programming knowledge. However, Python for finance uses Python skills that go beyond programming fundamentals and therefore prove more challenging to learn. For this reason, many people seeking to learn Python for finance benefit from learning from an instructor who can answer questions and provide step-by-step guidance with hands-on activities. Python’s uses in finance include data science, data analysis, artificial intelligence, and machine learning. This programming language enables financial tools to process large amounts of financial data. This data helps predict economic conditions, stock market behavior, and more, which in turn allows individuals and organizations to plan accordingly. Learn more about Python programming with Noble Desktop’s Python Learning Hub. 

Java is the most popular programming language in FinTech thanks to its ability to manage huge quantities of data, its advanced security features, and its versatile uses. Java powers ecommerce platforms, banking apps, and trading algorithms. Java also runs on any machine, making it a flexible programming language that can be used across teams. Learn more about Java programming with Noble Desktop’s Java Learning Hub where you can discover Java careers, how to learn this programming language, and how Java is used in different professions

Structured Query Language (SQL) allows programs to communicate with databases. SQL is used to locate, store, retrieve, and manipulate financial data. Many recruiters look for SQL programming knowledge when reviewing resumes for Financial Analyst positions. Knowledge of SQL also benefits those seeking other types of finance jobs including professions that work with data processing platforms and statistical modeling. Learn more about SQL, its uses, and how to learn it with Noble Desktop’s SQL Learning Hub. 

Learn Python for Finance with Hands-on Training at Noble Desktop

Noble Desktop offers in-person and live online classes that help you master Python for finance. You can start by learning the Python programming basics, then progress to advanced Python uses, or you can explore classes that specialize in teaching the financial uses of Python programming. Noble’s classes offer many benefits including expert instructor guidance given in real-time, small class sizes, and free retake options.

If you do not have previous experience with Python programming, Noble’s Python for Data Science Bootcamp provides the foundational knowledge needed before you learn Python for finance. This bootcamp covers Python programming basics including loops, objects, and functions, handling different types of data, using conditional statements, using object-oriented programming, data visualizations, making predictions, and more. Once you have completed this bootcamp, you can proceed to the Python for Finance Bootcamp in which you will learn how to gather and manipulate financial data using Python’s major financial libraries.

Looking to launch a new career using Python for finance? Noble Desktop’s FinTech Bootcamp prepares students for entry-level positions in financial technology and data science. This certificate program includes multiple courses in which you will learn about Python for data science, automation, data visualization, machine learning, and finance. You will also learn about financial modeling.

Learn more about Noble Desktop’s live online Python classes and live online Finance classes to compare different courses and options.

Key Insights

• Python is a free, open-source programming language with many uses in the finance industry and FinTech. Uses include data analysis, data science, artificial intelligence (including machine learning), and data visualization.
• Python libraries used in finance include Pandas, NumPy, and more. Nearly all of these libraries are free to use. 
• Comparable programming languages for finance include SQL and Java. 
• You can receive comprehensive Python for finance training through an in-person or live online course with Noble Desktop.