Fintech Development: How to use Python in Finance and Fintech

Common in applications ranging from risk management to cryptocurrencies, Python has become one of the finance institutions and fintech most popular programming languages. It is an excellent tool for researchers, analysts and traders thanks to its simplicity and robust modelling capability.

Companies such as Stripe, Robinhood or Zopa have used Python with success.

Python is among the top three most popular languages in financial services according to the HackerRank 2018 Developer Skills Report. The condition hasn't improved much in 2019, Python still seems to be one of the most sought after languages in the banking industry.

E-financialcareers has shown that the number of financial-related jobs mentioned by Python has nearly tripled over the last two years, from 270 to over 800. Organizations such as Citigroup also offer financial analysts and traders coding courses for Python as part of a continued education programme.

"We move faster into this world" – said Lee Waite, CEO of Citigroup Holdings CEO, in an interview. "At least, it seems important to learn the code." [source]. Python is one of the most searched after programming languages in the banking industry-notes eFinancialCareers.

Read on to learn more about how Python is used by finance companies and fintech to build cutting-edge technologies that affect the entire financial services market.

What exactly makes It such great technology for projects concerning fintech and finance?

Several Python features make it a great Finance and Fintech pick. These are the most notable ones:

Simple And Flexible

Python is easy to write and configure, making it a good candidate to tackle applications for financial services which are extremely complicated much of the time. The syntax of Python is simple and improves the speed of development, allowing organizations to easily create the applications they need or sell new products. Around the same time, it reduces the possible risk of error that is important when designing goods for a highly regulated sector such as finance.

It Allows For Quick Building Of An Mvp

The financial services sector needs to be more agile and responsive to customer requirements, offering personalized experiences and adding value-adding extra services. That's why financial institutions and fintech need a robust and scalable platform-and that 's just what Python provides. Using Python in combination with frameworks like Django, developers are able to quickly get an idea off the ground and create a solid MVP to quickly find a product/market fit. Companies can easily change parts of the code after validating the MVP or add new ones to create a flawless product.

It Connects Data Science And Economics

Languages such as Matlab or R are less common among economists who make their calculations using Python the most frequently. That's why Python dominates the financial scene with its simplicity and practicality in constructing algorithms and formulas – the incorporation of economists' work into Python-based systems is just a lot simpler. Tools such as scipy, numpy, or matplotlib allow one to perform sophisticated calculations and view results in a very accessible way.

It Has A Healthy Collection Of Tools And Ecosystem

Developers don't have to build their tools from scratch with Python, saving organizations a lot of time and money on development projects. In addition, fintech products usually require third-party integrations, and Python libraries also make that easier. Development speed improved by Python's array of tools and libraries provides a competitive advantage for companies seeking to meet growing customer demands by rapidly launching items. It's really straightforward to integrate with third parties like Truelayer (which offers access to OpenBanking APIs) or Stripe. When it comes to open-source projects, the open-source community maintains almost every Python framework-it 's possible to help with Django, Flask, OpenCV and many more. Each year, Python grows as a language and gains more popularity. All this makes it easier for talented Python developers to source and recruit, who add value to fintech or project finance. Organizations investing in solutions made with Python can be sure that their technology is stable, and will not become obsolete in the near future.

Using Python In Finance

A broad range of applications. Here are the language's most common uses within the financial services industry.

Tools For Analytics

Python is widely used in quantitative finance-solutions for processing and analyzing large data sets, large financial data. Libraries like Pandas simplify the data visualization process and allow sophisticated statistical calculations to be carried out. Python-based solutions are equipped with powerful machine learning algorithms thanks to libraries such as Scikit or PyBrain that enable predictive analytics which is very valuable to all financial services providers.

Examples of such products: Iwoca, Holvi.

Software For Banking

Finance organizations also use Python to build payment solutions and online banking platforms. Venmo is a prime example of a mobile banking platform that has developed into a full social network. Python comes in handy for developing ATM software that enhances payment processing thanks to its simplicity and flexibility.

Such products include: Venmo, Stripe, Zopa, Affirm, Robinhood

Cryptocurrency

Any business that sells cryptocurrency needs tools to perform a market analysis on cryptocurrency to get insights and predictions. The Python data science environment called Anaconda helps developers find and analyze cryptocurrency pricing, or build visualizations. That's why Python takes advantage of most web applications dealing with crypto-currency analysis.

Examples of such products: Dash, ZeroNet, enigma, koinim, crypto-signal

Developing A Trade Policy With Python

Stock markets generate massive data volumes which require a lot of analysis. And that, too, is where Python helps. Developers will use this to construct solutions that recognize the best trading approaches and provide actionable, insightful analytical insights into real market conditions. Cases of use include algorithmic exchange in fintech products;

Such products include: Quantopian, Quantconnect, Zipline, Backtrader, IBPy

Python – An Optimal Technology For Finance

Financial service is a challenging sector. Market-competitive companies need to produce goods that are safe, practical and complete compliance with state and international regulations. Attention to detail is also important as these approaches almost always require integrations with organizations, services, and banking API connections that need to operate smoothly.

Python 's clear programming syntax and stunning tool ecosystem make it one of the best technologies to handle any financial service's development process.

The Developer Skills Survey for HackerRank 2018 shows that Python is the second language developer to be learning next. The same 2019 report states that Python has dropped to third place but is still only 0.31 percent away from the second. That means the ecosystem of Python will continue to grow, providing organizations with access to a growing number of experts who will further integrate the language into the financial services and fintech field.