Programming Languages for Quants

Programming Languages for Quants

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022) presents an overview of popular programming languages used in quantitative finance.


Finance as an industry has always been very responsive to new technologies. The past decades have witnessed the inclusion of innovative technologies, platforms, mathematical models and sophisticated algorithms solve to finance problems. With tremendous data and money involved and low risk-tolerance, finance is becoming more and more technological and data science, blockchain and artificial intelligence are taking over major decision-making strategies by the power of high processing computer algorithms that enable us to analyze enormous data and run model simulations within nanoseconds with high precision.

This is exactly why programming is a skill which is increasingly in demand. Programming is needed to analyze financial data, compute financial prices (like options or structured products), estimate financial risk measures (like VaR) and test investment strategies, etc. Now we will see an overview of popular programming languages used in modelling and solving problems in the quantitative finance domain.


Python is general purpose dynamic high level programming language (HLL). It’s effortless readability and straightforward syntax allows not just the concept to be expressed in relatively fewer lines of code but also makes it’s learning curve less steep.

Python possesses some excellent libraries for mathematical applications like statistics and quantitative functions such as numpy, scipy and scikit-learn along with the plethora of accessible open source libraries that add to its overall appeal. It supports multiple programming approaches such as object-oriented, functional, and procedural styles.

Python is most popular for data science, machine learning and AI applications. With data science becoming crucial in the financial services industry, it has consequently created an immense demand for Python, making it a programming language of top choice.


The finance world has been dominated by C++ for valid reasons. C++ is one of the essential programming languages in the fintech industry owing to its execution speed. Developers can leverage C++ when they need to programme with advanced computations with low latency in order to process multiple functions fasters such as in High Frequency Trading (HFT) systems. This language offers code reusability (which is crucial in multiple complex quantitative finance projects) to programmers with a diverse library comprising of various tools to execute.


Java is known for its reliability, security and logical architecture with its object-oriented programming to solve complicated problems in the finance domain. Java is heavily used in the sell-side operations of finance involving projects with complex infrastructures and exceptionally robust security demands to run on native as well as cross-platform tools. This language can help manage enormous sets of real-time data with the impeccable security in bookkeeping activity. Financial institutions, particularly investment banks, use Java and C# extensively for their entire trading architecture, including front-end trading interfaces, live data feeds and at times derivatives’ pricing.


R is an open source scripting language mostly used for statistical computing, data analytics and visualization along with scientific research and data science. R the most popular language among mathematical data miners, researchers, and statisticians. R runs and compiles on multiple platforms such as Unix, Windows and MacOS. However, it is not the easiest of languages to learn and uses command line scripting which may be complex to code for some.


Scala is a widely used programming language in banks with Morgan Stanley, Deutsche Bank, JP Morgan and HSBC are among many. Scala is particularly appropriate for banks’ front office engineering needs requiring functional programming (programs using only pure functions that are functions that always return an immutable result). Scala provides support for both object-oriented and functional programming. It is a powerful language with an elegant syntax.

Haskell and Julia

Haskell is a functional and general-purpose programming language with user-friendly syntax, and a wide collection of real-world libraries for user to develop the quant solving application using this language. The major advantage of Haskell is that it has high performance, is robust and is useful for modelling mathematical problems and programming language research.

Julia, on the other hand, is a dynamic language for technical computing. It is suitable for numerical computing, dynamic modelling, algorithmic trading, and risk analysis. It has a sophisticated compiler, numerical accuracy with precision along with a functional mathematical library. It also has a multiple dispatch functionality which can help define function behavior across various argument combinations. Julia communities also provide a powerful browser-based graphical notebook interface to code.

Useful Resources


QuantInsti: Python for Trading

Bankers by Day: Programming languages in FinTech

Julia Computing: Julia for Finance

RExamples: R Basics

Related posts on the SimTrade blog

   ▶ Walia J. Quantitative Finance

   ▶ Walia J. Quantitative Risk Management

   ▶ Walia J. Value at Risk

   ▶ Gupta A. The Black-Scholes-Merton model

About the author

The article was written in October 2021 by Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022).

This entry was posted in Contributors, Financial techniques and tagged , , , . Bookmark the permalink.

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