One of the biggest trends in trading today is integrating algorithms to help determine buy/sell opportunities. Experienced investor Daniel Calugar says investors can take this one step further by using computer programming to execute trades automatically based on a number of pre-defined metrics.
This is called algorithmic trading, or algotrading, and it’s taking the investment world by storm. There are many benefits to using algotrading, including the ability to remove subjectivity and emotion with automatic trading, allowing investors to scale up their operations with ease.
Today, some trading software has algorithmic trading options built right in. But, if you want to create a fully customizable algotrading operation, you’ll need to create it by scratch using computer code.
If you want to pursue this avenue, you could hire a developer to do the work for you, partner with a developer to enjoy the benefits together, or even learn how to code so you can implement your strategy on your own.
No matter which way you go, choosing which programming language to use is not trivial. Below is an in-depth look at the various coding languages you could use to power your algotrading operation.
What is Algotrading?
To understand why the programming language is important — or why a programming language is needed in the first place — you must first understand what algorithmic trading is.
Algotrading utilizes computer programming to not only identify investment opportunities in the marketplace but to execute trades automatically based on a set of criteria. The theory behind algotrading is to strip out human emotion from investing to make it as objective as possible.
To do this, traders set up algorithms based on whatever key metrics and factors they want to track. This could include following specific trends such as movements in price level and other technical indicators, identifying arbitrage opportunities to buy and sell current prices versus futures, and taking advantage of regular re-balancing that occurs at index funds.
Dan Calugar points out that algorithms can execute accurate and instant trades once the specified parameters are met, eliminating the possibility for human error and capitalizing on the absolute lowest cost and best price for all trades. They can also analyze a large data set in a matter of milliseconds, something the human brain simply can’t process.
To be successful, algorithmic trading programs must be designed and coded properly. After all, an algorithm is only as good as the programming code that creates it.
Factors to Consider About the Algotrading System
Landing on a single coding language that would be best for your algotrading system, you should consider some crucial factors. By outlining the basics of your system, what you want it to do and how you want it to do it, you’ll be able to figure out the best coding language for algotrading more easily.
Here are four of the main factors you should consider, according to Daniel Calugar.
- The Aim
First and foremost, what do you want your algotrading system to accomplish? Some questions to ask include …
- Are you looking for it to just execute trades based on specific parameters?
- Do you want it to integrate portfolio construction and/or risk management?
- Do you need a back-testing system you can use often?
Many algotrading strategies can be separated into two main categories — doing research and generating signals.
The first category involves pouring over loads of current and historical data and comparing the two to each other in hopes of identifying outliers. This part of the process will also involve significant back-testing to put a strategy into practice before you risk any real money or make any real trades. In other words, this is the experimentation part of the process.
The second category involves generating trading systems derived from the algorithm and then executing on trading orders. Most of the time, this is done through a brokerage account and will require coding language that will connect to whatever brokerage account you use.
For both of these categories, you need to consider the CPU speed you’re using and its concurrency. Network bandwidth is also a major factor, as significant latency could cause you to miss out on prime opportunities — or even execute trades at inopportune times.
- The Overall Strategy
Determining what your overall strategy is will go a long way in deciding what the best coding language for algotrading systems will be. For example, a more complex and in-depth system will take a lot more work and resources upfront to get off the ground.
Some of the factors you should consider here include …
- Volume and frequency
- Markets to analyze
- Third-party data vendors to be used
- Timeline for development of the system
- Software and hardware needs
- Connectivity to investment vendors, such as brokerages
Generally speaking, the higher the volume and frequency of the strategy, the more markets you want to analyze, the more vendors you want to connect to, and the more powerful technology you’ll need to obtain.
Frequency is perhaps the largest factor in how you must define the technology stack necessary and, as a result, the coding language you want to use, according to Dan Calugar. For example, if you wish to consistently analyze a large amount of data with refreshes quicker than a minute, you’ll typically need a system that can thrive on high performance.
Storage is another crucial factor to consider here. All the data your algotrading system will need to collect and process will have to be stored somewhere. While this brings up obvious hardware considerations, there are also coding language considerations as well.
Finally, the required depth and breadth of the back-testing system might be best served by one coding language over another.
- Risk Management
Algorithmic trading systems can integrate components of risk management and portfolio construction. These components can help to mitigate risks, reduce overall transaction costs, and preserve capital.
While these are all positive things, integrating risk management and portfolio construction components into the algotrading system can be complicated and take extra attention. Knowing exactly which components you want to incorporate from the very beginning of the design of the system will help you create a more effective system — and help you select the best coding language to do so.
Proper portfolio construction will require the implementation of linear algebra. In addition, if you want the system to consistently re-balance your portfolio, you’ll need to optimize the matrix library, so it doesn’t cause the trading system to bottleneck.
Components of risk management attempt to anticipate the potential effects of volatility and any correlation between classes of assets and trading capital. To do this, you need to use computational models similar to engines that help determine derivatives pricing.
The execution part of algotrading will take all the information from the trading signals and then send them to the brokerage — or whatever means you’re executing the trades. Most of the time, this will require connecting your algorithm to the brokerage’s FIX or API connection.
Depending on the API’s quality, and the source that’s providing it, you may need to use a specific coding language. If you’re not already using the coding language that the API works with, then you may need to integrate additional code into your algorithm or create an API wrapper specific to the language you want to use.
The biggest determining factors in the execution stage are the frequency by which the algorithm must conduct trades and their volume. Keep in mind that when you’re utilizing algotrading to its fullest potential, you could send orders in the hundreds by the minute. To this point, it’s critical that your algorithm, and the coding language that backs it, perform to high standards.
If there’s even a little slippage in this regard, your profitability could be affected dramatically.
What is the Best Coding Language for Algotrading?
Understanding the components of algotrading and what you would like your system to accomplish is the first step in determining the best coding language for your system. The next step is matching your requirements with the best programming language.
Below, Daniel Calugar outlines five of the best coding languages for algotrading.
While C++ is considered by many to be middle-of-the-road when it comes to programming language complexity, it is a highly efficient coding language for specific tasks. For instance, HFT — or high-frequency trading — is very sensitive to latency, and C++ does an excellent job of processing a vast amount of data simultaneously.
Many consider C++ to be the industry standard when it comes to HFT. The execution speed is a big advantage of using this coding language and allows you to scale quite easily.
Another massive positive of C++ is that the legacy systems that many large financial institutions still use are written in this language. This makes connecting your algotrading system to external systems much simpler. You can work around this aspect, but it does take a little additional work.
One of the downsides of C++ is that it does take a little while to learn the code. As a result, it’s not used widely for machine learning or data science systems. It often takes a lot more effort to code an algotrading system in C++ than some of the other coding languages below.
If you’re going with C++, in other words, you’re doing so because you need both high and expressive performance out of your system. Before you proceed with this language, though, it’s vital that you have a solid plan and plenty of resources.
While Python certainly isn’t as fast as C++ (or even C#), it’s a high-level coding language used extensively in quant trading. In addition, python has many libraries that are considered high-performance, which is why it’s much easier to carry out prototyping and research on it.
A considerable advantage of using Python is that it’s a coding language that is much easier to learn than the others on this list. This means that even complete beginners with next to no coding language background could pick up Python much quicker than other languages. This could be very important to you if you plan to write the code yourself or be involved in any way.
Python is an excellent fit for both machine learning and data science and is highly productive. As a result, traders throughout the industry turn to Python as their coding language of choice for all systematic trading systems.
Many of the most common tasks related to trading can rely on the mature libraries that Python offers, including back-testing, statistics, and analyses based on time series.
One of the main reasons traders opt for Python when writing their own algotrading systems, explains Dan Calugar, is that most investment programs provide Python libraries of their own for trading purposes. This makes connecting your algotrading system to your brokers’ systems to execute on trades extremely simple.
A downside of Python is that it doesn’t handle tasks that require a lot of memory very well. And, again, compared to some of the other coding languages mentioned here, it doesn’t execute at nearly the same speed. This is definitely something to keep in mind if you plan to execute HFTs.
Speaking of C#, it’s another coding language that you could consider for algotrading, Daniel Calugar says. While C# sounds very similar to C++ in name, the two have many differences. In fact, C#, at its core, is a higher-level coding language.
That being said, it is easier to learn than both C and C++ and does provide the speed that is often adequate for most HFT systems. However, a major downside to C# is that there aren’t a lot of accessible resources available that focus specifically on algotrading. In other words, if you want to go with C#, you may be figuring out how to use it for your system on your own.
Programmers use Java mainly for simulations, data modeling, and execution with low latency. While C++ is often considered the gold standard for HFT, many people argue that Java is the better fit here.
While Java wouldn’t be considered to operate as fast as C++, it is considered fast enough for most algotrading systems. Server-side, Java is considered to be lightning-quick.
This means it may not be the best choice if you plan to scale up relatively quickly. If you want to expand your trading practice significantly and start with Java, you may need to write an entirely new algotrading system from scratch using a different coding language.
A final coding language to consider for your algotrading system is R, which is a programming language that’s open source. Primarily, it’s used for data analytics and statistics, which makes it a rather natural fit for algotrading — which is based so heavily in these two areas.
Any algorithm used for trading that is worth its weight in gold, as they say, would need to be best tested extensively using a sampling of different datasets. This is one of the biggest strengths of the R coding language. It can easily be used to design a program that generates extensive trading signals and helps maximize investment returns.
Another significant benefit of using the R coding language is that it is relatively easy to learn. There are many straightforward and simple online resources available for free. Many of these resources are geared specifically toward using R to code an algotrading model.
The downside to using R is that it does have its limitations for HFT and more complex scenarios. In addition, it isn’t a coding language that’s widely used by financial institutions and brokers, either so it would again require some extra programming and development to “jive” with the external systems that you would need to hook into so that you can execute on trades.
Even still, Dan Calugar says the R coding language is worth considering when designing and programming your algotrading system from scratch.
About Daniel Calugar
Dan Calugar is a well-rounded investor with experience in computer science, business, and law. His interest in investing grew while working as a pension lawyer, and he used his technical skills to create programs that assisted him in finding more successful investment techniques. Outside of work, he likes to stay active, spend time with loved ones, and volunteer with Angel Flight.