Advancements in digital technology have changed the landscape of a number of industries, and trading is no different.
In today’s trading environment, traders are likely to encounter quantitative trading at some point. Quantitative trading includes high frequency trading and algorithmic trading and involves learning coding languages and programming.
This has set new traders on a path to learn coding for trading, but has also sparked a debate about the best programming language for financial trading.
The truth is that there is no one “best” programming language, because all of them have different advantages. Additionally there are also a number of considerations to take into account, including the research tools, the portfolio optimiser and the risk management involved.
Below we’ve listed the five most popular programming languages that traders are likely to consider, and the advantages of using each of them.
When creating a trading programme, latency – the momentary delay between data being transferred following an instruction it has been given to initiate the transfer – is a key consideration for traders.
Latency is important for trading programmes because that momentary delay could mean the difference between entering or exiting a trade at the optimal time.
One programming language that has a specific focus on latency is C++, because it provides speed through offering developers control. It does this through numerous methods including:
- Precise memory management: C++ gives traders full control over when to allocate, and when to free up, memory. As programs run, they collect “garbage”, such as objects and scripts that are no longer needed by the application. If an application collects too much garbage it will clog too much memory, ultimately causing memory exhaustion and resulting in the programme crashing. In C++, traders can choose how much memory they wish to allocate and when, which negates running into memory management penalties in runtime, helping to keep the application running smoothly.
- Control over the size of data types: Sometimes different data structures serve different purposes. In C++ traders can control the size of these types, for example by giving a trader the ability to represent a series of Booleans as a bit array if they wished. Smaller data structures can sometimes save on speed if the structure is small enough to fit into the CPU cache, and other times, shrinking the total size of the data structure will fit it into the main memory without jeopardising performance, so having this control is invaluable for traders.
- Separating algorithms and data: C++ allows for code to be shared without compromising performance. For example, in C++ a complex algorithm could be implemented that created different instances in accordance with different bit depths. Whereas in other programming languages, different functions may have to be created for each bit depth. Adding extra functions makes coding longer, and more complex.
- Static typed language: C++ is a static typed language which means it is easier for the compiler to optimise because more is known regarding the data used. This allows the compiler to aggressively optimise through using data types and const keywords. C++ once again opts to let programmers choose how much or how little information they give to the compiler.
These savings on speed are often the reason why C++ is used in production, as it can be compiled, tested and then installed into production servers.
There have long been reports that Java is the most popular programming language on Wall St because of its usage for data modelling, low latency execution and simulations.
Questions have also long circulated about Java’s efficiency when compared to C++ and Python, but its advantages are undeniable. They include:
- Highly portable code: Highly portable code means that a developer can write the code once and have it run anywhere. That’s because Java code runs on a virtual machine, allowing it to run on Windows, Linux, and MacOS operating systems without needing layers of adjustment first.
- Built in memory management: Java has garbage collection built in and this is executed in two ways. In the first stage Java will identify which pieces of memory are being used, and which aren’t. In the second stage, it removes the objects which it has marked as garbage collectable. However the built in garbage collector is something traders should be aware of. Whilst most applications won’t cause the collector a problem, in a high-frequency trading system, the built in collection could cause intrusive latency spikes, so it’s worth monitoring.
- Handles beefier graphics and security: Java has a much better processing power output when compared to other programming languages because it’s able to handle enhanced security, like encryption, and graphics better. Traders that want to produce a trading system with an aesthetically pleasing GUI could opt for Java without compromising on processor performance.
- Machine learning: Machine learning often brings up comparisons between Java and Python. In Python, the speed with which Java would be able to execute certain algorithms would be unmatched by Python. Alternatively however, the speed with which the algorithms would be developed in Python would be unmatched by Java, so it is entirely dependent on what the trader is looking for. Whether execution, or development speed.
C# differs from C++ by sharing more similarities with Java than it does the object-orientated focus of C++. In trading programmes especially, C# and Java are often used interchangeably for the same functions.
Advantages of traders learning C# include:
- C# is a Compiled Language: Compiled languages are trading programs automatically translated by the machine. This means that programs written in C# will perform significantly faster. If a trader’s programme must run at maximum efficiency with little latency, C# is a great option.
- C# is a versatile and rich language: C# has a multitude of high level constructs which mean it is expressive whilst still allowing a developer to do low-level optimisations through the use of pointers and value types. Whilst comparisons are often drawn between its performance against C++, its versatility is a direct reason that any performance difference between itself and C++ is incredibly minor.
- C# is forgiving: C# is an excellent language for new developers, or for the development of new applications. This is because the language prevents developers from making crucial mistakes through exception handling. This useful tool ensures an application reaches production faster than if it were created in C++ where mistake correction is mostly manual. However its built in exception handling does create additional latency because it is working alongside the CLI (Common Language Infrastructure).
Python trading is an incredibly popular language in the quantitative finance community because it has made it easy to build intricate statistical models. This is because Python makes scientific libraries such as Pandas, NumPy, and Pybacktest readily available.
Python is also viewed as the language least likely to give quantitative developers trouble due to its ease of ability and focus on writing shorter code that is more effective. The main advantages of Python are:
- A functional programming approach: Because of its functional approach, Python makes it easier to both write and evaluate algorithmic trading structures. The functional code can then easily be applied to dynamic algorithms used in algorithmic trading. This means that complex trading platforms that may have cost considerable time or expense using C# or C++ could ultimately be developed in less time using Python.
- Seamless coding experience: Python code is able to be edited in live time in a step by step format, which enables thorough and comprehensive debugging. Additionally Python has a considerable focus on clarity which means its code is designed to be as concise and simple as possible. Both of these factors are vital when it comes to trading because a code that is able to be quickly scanned by a human for errors or changes will save crucial time.
- APIs are prewritten: Many APIs (Application Programming Interface) are readily available in Python. Popular packages include graphing package, Matplotlib, GUI framework, TkInter, and scientific calculator NumPy. This takes considerable time out of coding in an API or a developer having to make their own.
- Automation through AI: A dream for some quantitative traders and developers is to have a programme that runs hands-off. That means that a bot takes over and executes trading decisions. To do that developers must experiment with exotic techniques such as Neural Networks. Just like the APIs, Python is already integrated with Neural Networks. Additionally if a developer wanted to scan Twitter for trading updates, Python handles this sort of scraping activity with ease, and again only requires a few lines of code to do so.
R is another object-oriented programming language like both C# and C++. Its primary function is for statistical analysis and data exploration, but it’s an incredibly versatile language used by not just programmers, but anyone conducting data analysis.
- Usability: R’s statistical models can be written using only a few lines of code, which makes for better programming efficiency. In addition, the same piece of functionality can be written a variety of different ways in R, and still achieve the same result giving it greater flexibility and quicker development speeds.
- Quicker learning curve: Whilst R has a steep learning curve at the start, R can eventually be run with a set of basic commands. That’s because even short snippets of code written in R can perform operations that would otherwise be considered tedious in other languages. Ultimately this means that R requires minimal programming skills.
- Large number of Open Source Packages: R has always provided a multitude of free packages for both statistical analysis and visualisation. The most popular libraries are Dplyr, Zoo and Ggplot2, but there are many more.
- Large developer community: if you want to do quant finance work, it is very, very hard to go past R. The developer community is fantastic and helpful and there are some really exciting packages being produced at the moment. There is also a great data analytics community as a whole and lots of fun stuff to bash around with on EC2 and MapReduce with low setup overhead.
Pinpointing the “best” coding language is almost impossible. Different programming languages function entirely differently and each come with different strengths. C++ improves latency, Java is more portable, and Python is easier, but which one an algo trader uses is entirely dependent on the type of algorithm or programme they wish to build.
At Alphachain Academy, we teach you to code in our Algorithmic Trader Programme. You’ll learn to code your own algorithmic strategy that will eventually go live and trade using a $20,000 accredited Alphachain Academy account. Find out more here.