The Pythoneers
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The Pythoneers

Python in Action: Finance (3 practical use cases)

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One of our recent issues explored Python’s use cases in three fields of scientific research, showing that Python has applications outside of traditional computer science. Today, we’ll talk about a non-scientific field that Python has also heavily influenced in recent years: finance.

Python has become a key tool in the financial industry for analyzing data, conducting predictive analyses, building financial applications, and automating securities trading. As a robust and flexible language with a diverse ecosystem of libraries, Python can be used to solve a wide range of finance-related problems. This is why so many organizations, from major banks like J.P. Morgan and Bank of America to innovative fintech companies like Stripe and Venmo, use Python.

While finance professionals use Python extensively, you don’t need to be an investment banker to learn and apply Python’s finance-based tools. Below, we’ll explore Python’s use cases in the financial sector and learn about some open-source tools that you can use to start working with financial data.

Python in Finance: Use Cases

Python is sometimes used in financial applications, including mobile payment apps like Venmo. Python code can be written quickly, and it has useful debugging tools that help to minimize errors. Python web frameworks like Django and Flask can also streamline the development process. Java may still be the most widely used programming language in this field, but Python is growing in popularity.

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Quantitative finance involves analyzing and visualizing large datasets. As previous issues of Grokking Python have explained, Python has excellent tools for these tasks, making it a great alternative to spreadsheet applications like Excel. Excel is a user-friendly tool for storing and manipulating data, but Python’s libraries facilitate data analysis on a much larger scale. The Pandas library can be used to import and analyze financial data, and there are a wide range of Python-based machine learning tools that can process that data in service of predictive analyses. These processes help financial institutions make data-driven decisions.

This is why major financial institutions increasingly want their analysts and investment bankers to be conversant with Python and its finance-based tools. Python’s applications in the industry go well beyond app development.

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Algorithmic trading is the use of algorithms to automate securities trading. There are a lot of advantages to algorithmic trading. It tends to be very fast and streamlined, potentially resulting in greater profits than conventional trading. Python is a popular tool for developing and implementing these algorithms, and it has libraries designed for that purpose. Some of these libraries focus on backtesting: applying a trading strategy to historical data to test how the strategy would have performed. Evaluating trading strategies through backtesting yields data that can improve automated trading systems.

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Python Libraries for Finance

Financial institutions and fintech companies regularly use major Python libraries like NumPy, SciPy, Scikit-learn, and Pandas. These are general-purpose libraries that can assist with tasks across a wide range of fields, including finance. NumPy and SciPy have powerful computational capabilities, Scikit-learn is useful for predictive analytics, and Pandas allows for the importation and management of large datasets.

In addition to these core general-purpose libraries, there are also many Python libraries designed specifically for finance. Here are some worth exploring:

  • Pyalgotrade is an algorithmic trading library.
  • Pyfolio provides tools for analyzing financial portfolios, with a focus on performance and risk analysis.
  • ffn is a library that focuses on quantitative financial analysis.
  • Bt is built on ffn; it provides tools for backtesting quantitative trading strategies.
  • Finmarketpy has tools for analyzing market data and backtesting trading strategies.
  • Pyfin is an options pricing library.
  • TIA is a backtesting and technical analysis library.
  • Analyzer is a backtesting and financial analysis library.
  • Backtrader is another backtesting library.
  • TA-Lib and QuantLib are popular finance-oriented libraries written in C++. Both have Python wrappers.

Getting Started

Python and other programming languages can be used to build innovative solutions to the myriad finance-related problems that banks, corporations, and individuals face. There’s a lot to explore at the intersection of finance and technology. Maybe you’re hoping to pursue a career in finance or fintech, or maybe you’re just curious about Python’s tools for working with financial data — either way, there are some great resources out there to help you learn more.

Here are a few projects and courses that introduce users to Python’s data analysis and visualization tools and how they can be applied to finance.

If there’s a Python use case you’d like us to cover in a future issue of Grokking Python, let us know in the comments.

As always, happy learning!

This post is from Grokking Python, a free newsletter available on Substack from Educative, the world’s best learning platform for software developers. It’s where we’ll share our team’s best Python-related info and resources — stuff we think could really enhance your understanding.

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