Programming (& Scripting) Languages used in Quantitative Finance

Compare the most used programming/scripting languages in Quant Finance:

-Python – Most widely used, great for backtesting & ML, but not the fastest.
-C++ – Essential for high-frequency trading, ultra-low latency, but has a steep learning curve.
-Java – A staple for risk management & trading infrastructure in big banks.
-R – The best for statistical modeling & used in academia, but not for real-time trading.
-Julia – Fast, efficient, rising in quant research, but niche.
-MATLAB – Used for derivatives pricing & simulations, but expensive.
-KDB+/Q – The go-to for tick data storage & real-time analytics, used by big banks.

The best way to put your programming skills to the test is by working on real-world projects. Want to explore how these languages are used in quant finance? Here’s a list of 15 hands-on project ideas to help you apply your knowledge and build something impactful. Download the handbook here (includes project overview, how to build it, resources and applications of each): shop.beacons.ai/quantify_your_career/b553a402-4121…

Programming languages comparison image download: www.linkedin.com/posts/quantifyyourcareer_quantfin…

Call Matlab code from python: www.linkedin.com/feed/update/urn:li:activity:72975…

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