How is python used in the financial industry?
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Common in applications that range from risk management to cryptocurrencies, Python has become one of the most popular programming languages for Fintech Companies. Its simplicity and robust modeling capabilities make it an excellent tool for researchers, analysts, and traders. Python has been used with success by companies like Stripe, Robinhood or Zopa. According to the HackerRank 2018 Developer Skills Report, Python was among the top three most popular languages in financial services. In 2020 Python still appears to be one of the most wanted languages in the bank industry. eFinancialCareers showed that during the last two years the number of finance-related jobs mentioning Python has almost tripled, growing from 270 to more than 800. Organisations like Citigroup now offer Python coding classes to banking analysts and traders as a part of their continuing education program. “We’re moving more quickly into this world” – Lee Waite, the CEO of Citigroup Holdings CEO, said in an interview. “At least an understanding of coding seems to be valuable”. Read on to find out more about how finance organizations and fintechs are using Python to create cutting-edge solutions that impact the entire financial services sector. What makes Python such a great technology for fintech and finance projects?Several features of Python make it a great pick for finance and fintech. Here are the most significant ones: It's simple and flexiblePython is easy to write and deploy, making it a perfect candidate for handling financial services applications that most of the time are incredibly complex. It allows building an MVP quicklyThe financial services sector needs to be more agile and responsive to customer demands, offering personalized experiences and extra services that add value. That's why finance organizations and fintechs need a technology which is flexible and scalable – and that's exactly what Python offers. Using Python in combination with frameworks such as Django, developers can quickly get an idea off the ground and create a
solid MVP to enable finding a product/market fit quickly. One example of successfully following the MVP approach could be the Clearminds platform which was developed using Python and
Django. Now they offer financial advice and investment tools. It bridges economics and data scienceLanguages such as Matlab or R are less widespread among economists who most often use Python to make their calculations. That why's Python rules the finance scene with its simplicity and practicality in creating algorithms and formulas – it's just much easier to integrate the work of economists into Python-based platforms. It has a rich ecosystem of libraries and toolsWith Python, developers don't need to build their tools from scratch, saving organizations a lot of time and
money on development projects. It's popularPython is surrounded by a vibrant community of passionate developers who contribute to open-source projects, build practical tools, and organize countless events to share knowledge about the best practices of Python development. There is the Python Weekly newsletter or the PySlackers
Slack channel. For official community information, one can visit the Python.org community section. Not to mention sites dedicated to learning Python and sharing Python knowledge like RealPython or DjangoGirls which also have their own communities. Python comes in handy in a broad range of applications. Here are the most popular uses of the language in the financial services industry. Analytics toolsPython
is widely used in quantitative finance - solutions that process and analyze large datasets, big financial data. Libraries such as Pandas simplify the process of data visualization and allow carrying out sophisticated statistical calculations. Examples
of such products: Iwoca, Holvi. Banking softwareFinance
organizations build payment solutions and online banking platforms with Python as well. Venmo is an excellent example of a mobile banking platform that has grown into a full-fledged social network. Examples of such
products: Venmo, Stripe, Zopa,
Affirm, Robinhood CryptocurrencyEvery business that sells cryptocurrency needs tools for carrying out cryptocurrency market data analysis to get insights and predictions. Examples of such products: Dash, enigma, ZeroNet, koinim, crypto-signal Building a stock trading strategy with PythonStock markets generate massive amounts of finance data that require a lot of analysis. And that's where Python helps as well. Developers can use it to create solutions that identify the best stock trading strategies and offer actionable, predictive analytical insights into the condition of specific markets. Use cases include algorithmic trading in fintech products, Examples of such products: Quantopian,
Quantconnect, Zipline, Backtrader,
IBPy Wrap up: Python, an optimal technology for financeThe financial industry is a challenging one. Organizations that want to compete on the market need to develop products that are secure, functional, and fully compliant with state and international regulations.
How is Python helpful in finance?Python is an ideal programming language for the financial industry. Widespread across the investment banking and hedge fund industries, banks are using Python to solve quantitative problems for pricing, trade management, and risk management platforms.
How Python language is used in finance?Python is widely used in quantitative finance - solutions that process and analyze large datasets, big financial data. Libraries such as Pandas simplify the process of data visualization and allow carrying out sophisticated statistical calculations.
How relevant is Python in financial markets?The growing importance of Python tools for financial markets reflects the large ecosystem of data science libraries, such as NumPy or pandas. Many funds use Python to model financial markets, with banks including JP Morgan and Bank of America also hosting extensive Python-based infrastructure.
Do you need Python for finance?Learning financial programming with Python is becoming a requirement. Finance and banking have a reputation for very high salaries, so the job field attracts a large number of applicants. If you're one of them, you should know Python is hugely popular for finance — and still growing in popularity.
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