How Python is Used in Finance

Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options. It is simple, so it’s easy to learn since it requires a unique syntax that focuses on readability.

Python is often used as a core language for financial projects. The financial sphere is quite big and consists of various areas. In turn, each area can benefit from using this language. Let’s consider those areas more closely.


It’s simple and flexible

Python is easy to write and deploy, making it a perfect candidate for handling financial services applications that most of the time are incredibly complex. Python’s syntax is simple and boosts the development speed, helping organizations to quickly build the software they need or bring new products to market. At the same time, it reduces the potential error rate which is critical when developing products for a heavily-regulated industry like finance.

Banking software

Finance organizations build payment solutions and online banking platforms with Python as well. Python is a good choice for these areas due to its math syntax opening more space for flexibility in terms of calculations and other math-related tasks. This language is often used to write software for ATMs and enhance payment processing. For example Venmo, Stripe, Zopa, Affirm, Robinhood.

Cryptocurrency

Python can be helpful within the cryptocurrency sphere as well. The analysis of markets can be conducted with the help of this language. It’s possible to build scripts with this language that will analyze the current situation on the market and make predictions. For example with the help of the tool called Anaconda, you can get information about cryptocurrency prices and analyze it. That’s why most web applications that deal with cryptocurrency analysis take advantage of Python. For example Dash, enigma, ZeroNet, Koinim, crypto-signal.

Trading

Stock markets generate massive amounts of data that require a lot of analysis. And that’s where Python helps as well. With the help of this language Developers can use it to create solutions that identify the best trading strategies and offer actionable, predictive analytical insights into the condition of specific markets. To create the software with such capabilities can be used not only Python but Django framework based on Python. For example Quantopian, Quantconnect, Zipline, Bank trader, IBPy  etc
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Analytics Tools

It’s worth mentioning that there’re a lot of powerful libraries built with Python. Some of them are intended to enhance the work with a certain sphere be it mathematic calculations or data analytics

Python is widely used in solutions that process and analyze large datasets. Libraries such as Pandas simplify the process of data visualization and allow carrying out sophisticated statistical calculations. Thanks to libraries such as Scikit or PyBrain, Python-based solutions have equip with powerful machine learning algorithms that enable predictive analytics which are very valuable to all financial services providers. For example Iwoca, Holvi.

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2 years ago

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2 years ago

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1 year ago

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