Python has been widely adopted by the financial industry in recent years. Thanks to its ease of use and shallow learning curve, Python is being implemented to build risk management, trading systems, and other financial service applications.
Through this article, you will learn all about the importance of Python for finance and the reasons why this programming language is being implemented in this industry. You will also learn about the different education options available for this career path and the steps you should take to become a Python developer in the financial industry. Let’s get started.
What Is Python?
Python is an interpreted, high-level, object oriented programming language that is designed to be simple, flexible, and easy to learn. It is one of the most popular and widely used programming languages today.
Because it was designed to be a general-purpose language, it is used in a variety of industries and for a variety of purposes, including task automation and data analysis. It also supports several modules and packages that encourage code reuse.
What Is Python Used for in Finance?
In the financial industry, Python can be used to design banking software for online banking, payments, and ATMs for a faster payment process. It can also be used for data analysis, to predict growth and profitability, and to break down complex structured and unstructured data into simple and easy-to-understand data.
Python can also be used for cryptocurrency. Analysts can use it to analyze or predict the trading market like algorithmic trading strategies using Python’s ecosystem Anacondas. This is beneficial for trading apps that deal with cryptocurrency analysis using Python. It is also used in quantitative finance to perform complex statistical calculations and predictive analytics.
How Long Will It Take to Learn Python for Finance?
It typically takes a few weeks to a few months to learn Python in-depth, including mastering important functions like converting a string to integer. However, how long it takes will depend on how much time and energy you dedicate to it. You can choose to learn through a coding bootcamp or an online learning platform. Coding bootcamps take around six weeks to nine months for completion depending on the bootcamp and your learning format.
An online course may take anywhere around a couple of hours to a few months. The majority of online courses are pre-recorded training videos that last for about two to 16 hours but may require more time to fully grasp the content.
Why Should You Learn Python for Finance?
Python is being widely used in a variety of fields for many purposes. In the financial industry, Python is being used for data analysis and the creation of a variety of software and applications, which translates to a higher demand for Python developers with financial expertise. Some of the main reasons for learning Python for finance are listed below.
It Is Simple and Flexible
Python was designed to be relatively easy to install, write, and understand. Dealing with complex financial applications can be demanding, which makes Python’s easy syntax the ideal option. It allows financial organizations to introduce new applications quickly due to its high development speed. Python also allows for easy detection and correction of calculation errors.
It Is Adaptable and Cross-Platform
Python is a cross-platform programming language that can be integrated with many other platforms, software products, application domains, and other popular programming languages. It can also be used for machine learning and AI projects due to its flexibility, making it a preferred option for financial organizations.
It Offers Vast Resources
One of Python’s best features is the numerous open-source libraries and tools available to financial organizations to conduct data analysis. This means that developers do not need to build products from scratch, but can instead adapt existing code according to an organization’s needs. Some of the main libraries include Pandas, Anaconda, and NumPy.
How Can I Learn Python for Finance?
There are many different educational paths you can choose from to learn Python for finance. They range from traditional college degrees to self-paced online programs and courses. Below is a brief description of the most common options so you can pick the one that adapts to your needs and career goals.
The best Python coding bootcamps offer fast-paced, immersive training to equip students with the skills needed to start a career as Python developers. If you join a coding bootcamp to learn Python for finance, you’ll have a chance to gain hands-on experience. Many bootcamps offer career services to help you put together a portfolio and land a job in your field after graduation.
Online Python courses are another great way to learn to code with Python. You can enroll in one or more of the available courses on any of the numerous online training programs. The programs are typically self-paced, which means the duration will depend on you.
Some online courses are available for free, while others are paid and often come with a certificate of completion. They also offer hands-on training to allow students to practice what they learn and some of them also offer access to a community where students can ask questions when they feel stuck or need further assistance.
Reading relevant books is a good way to learn any skill. You can learn the basics through books or use them to supplement your learning through an online course or bootcamp.
Some of the best books for this topic include Python for Finance by Yves Hilpisch, Basic Python in Finance: How to Implement Financial Trading Strategies and Analysis Using Python by Bob Mather, and Python for Finance Cookbook by Eryk Lewinson.
Top Python for Finance Libraries
Python has numerous libraries that you will find helpful during your learning process. The majority of them are open-source and have been known to be highly useful to professionals at any stage in their careers.
- NumPy. NumPy is a Python library that focuses on basic array operations. It is widely used in computational finance, academics, and the tech industry and is mostly used for scientific computing.
- Pandas. This library focuses on fundamental data types and their methods for advanced statistical functionality. Pandas, like the Python programming language, provides high-performance, user-friendly data structures, and analysis tools.
- SciPy. SciPy is an open-source library used for mathematics, science, and engineering. It is also used for advanced scientific and financial calculations.
- Pyfolio. Pyfolio can be used to provide risk analysis reports and financial performance results. It offers an easy way of creating tear sheet models and bayesian analysis.
- Statsmodel. This library makes use of SciPy packages to perform more advanced statistical model testing. It is used to validate the results of statistical and diagnostic analyses against those of existing statistical packages.
How to Learn Python for Finance: A Step-by-Step Guide
If you’re committed to learning Python for finance, we’ve created a short guide to help you get started. Follow these steps to pick up the skills you need and build a successful career as a Python developer for the financial industry.
Learn the Basics
You need to start by learning the basic concepts of Python. This includes how to install it, the basic Python syntax, and control flow. You can do this by watching any of the several Python tutorials available on the Internet. You can also enroll in a bootcamp or beginner’s online class to assist you with this. This will give you a good foundation to avoid complications in the future.
At this stage, you should also become familiar with the libraries that will be useful for this field, including Pandas, NumPy, and SciPy. There are websites and online platforms available to help with this.
Learn Python for Finance
After getting basic knowledge of Python and gaining confidence in your skills, you should then begin to learn about Python specifically for finance. Make sure to familiarize yourself with the ways it is currently being implemented in a finance context through software and applications, and study the ways these are built.
You can also utilize massive open online course platforms and the vast online Python community to find support.
Learn Advanced Python Features
Once you are comfortable working with Python for finance, then it’s time to move on to learn the things that will truly make you stand out. You should proceed to learn how Python is used for machine learning, AI, and alternative data. At this stage, you should also work on software design, version control, testing, and deploying.
Work on Projects and Build a Portfolio
The best way to learn to code is through hands-on practice. You can research beginner Python for finance projects and start with those, taking it one step at a time until you can work on more advanced projects. These projects will not only help you solidify your skills, but will also allow you to build a portfolio that you can use to showcase your talents to potential employers.
Apply for Entry-Level Roles
Once you have worked on projects and built a solid portfolio, you can begin to apply for entry-level roles in the field of your choice and forge a rewarding career path. Make sure you research the roles you are applying for thoroughly and curate your portfolio accordingly.
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Start Learning Python for Finance Today
Python is a great tool to build apps and conduct data analysis for financial organizations. It also offers the advantage of being easy to learn and use, even if you are a complete beginner. You also stand to earn good money in this line of work, as the average annual salary of a Python developer is $123,536, according to Payscale.
This field is also promising in terms of job security, with the job outlook for web developers being 22 percent according to the Bureau of Labor Statistics. Overall, learning Python for finance can help you build a stable, lucrative career.
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