Gone are the days when being a trader meant closing a deal over the phone, signing documents and exchanging bank information. Technology has made things far more complex, and these days, if you want to be a serious trader, you have to know how to get the most out of these latest technologies. This translates into accumulating a significant amount of knowledge in the realm of quantitative trading, including both high-frequency and algorithmic trading. Trading no longer a matter of merely dealing in money; it also incorporates dealing in several other skills, particularly computer programming. You need to be able to write your own trading algorithms and work with those created by others.
Here are the five languages you should know if you want to get ahead as a trader.
This language is particularly accessible if you’ve already worked with C. Tackling C++ without this stepping stone is a bit of a daunting task, but should you decide to go for it, the rewards are great, especially since demand for C++ is likely to remain high for the foreseeable future. Latency-sensitive components of high-frequency trading are usually coded in C++, which is good for processing high volumes of data, and many banks use it for their legacy systems.
Java is a rival to C++ in low-latency execution, but its popularity in data modelling and simulations has made it one of the most in-demand programming languages on Wall Street. If you’ve got your eyes set on the financial capital of the world, Java is one of your safest bets.
Don’t be confused: C# is not the same as C++. C# is actually more closely related to Java as a low-level platform-neutral object-oriented programming language. This similarity often makes C# and Java appropriate for the same functions in trading. C++ in the other hand, is a higher-level component-oriented language.
While Python is a high-level language, it is also easy for beginners. If you’d never dealt with code before, Python is possibly the best place to start. It’s slower than either C++ or C#, but it comes with high-performing libraries, which makes it really good for research and prototyping.
Any trading algorithm you might create or work with is useless if it hasn’t been thoroughly tested using sample data. R is the perfect language for creating programs that help you collect statistics and analyze the resulting data, which is crucial for the generation of trading signals and maximizing strategy returns.
Learning a new skill is always a net positive. While programming languages may not be simple or easy (particularly if you’re new to them), the ability to be a good trader involves being adaptable, and having knowledge of two or more of these vital programming languages. Essentially, it’s simple: the more you know, the more you’ll be capable of.