Will data science knowledge be useful in algorithmic trading?

Data Science and Algorithmic Trading: A Perfect Match?

 

These days, data science seems to be all the rage. Many aspiring traders want to know whether it’s going to improve their chances of making money in the markets.

The answer may surprise you; in fact, using data science techniques can actually make you a better trader, as long as you use them right.

Here’s how data science can help your algorithmic trading skills—and how you can use those skills to achieve success in the financial markets.

 

Data scientists should know about computer networks

It’s more than just knowing how to set up a server or troubleshoot a problem—having a background in computer networks can help you solve data problems that go beyond day-to-day operations.

Data networking is crucial to trading operations, especially when it comes to processing trading data quickly and in real-time.

For example, if you’re designing an algorithmic trading system, it will likely require sending information back and forth between computers at different locations. If you don’t know how networks work, your data science program will miss an important component of data analysis.

 

They should have programming skills

More than 90% of traders who use algorithmic trading systems rely on statistical analysis.

Though you don’t have to be a full-blown data scientist to create these tools, you do need a good understanding of statistics. If you aren’t well versed in basic statistics, don’t worry! It’s something that can be learned quickly with an open mind.

A good place to start is by reading books or taking classes on probability theory and linear regression.

As for programming skills, any language will work as long as it’s used for building algorithms (Python, R, C++). But if you want to get ahead of the curve and learn how to build your own algorithm from scratch—using machine learning—then learning Python is a must.

Another useful skill is to know how to program bots using low-level languages like C/C++ or Assembly. This way, you won’t need outside libraries when coding complex systems, which will make your system run faster and more efficiently.

Once you’ve mastered these concepts, there are plenty of free resources online where you can find information about strategies that other data scientists have developed over time. Be sure to check out Kaggle, Quantopian, QuantConnect, Quantiacs, and Zipline.

There are also many university courses available online via Coursera, Udacity, Udemy, edX, etc.

 

They must have an understanding of statistics

Statistical knowledge is vital in algorithmic trading, as it enables traders to identify patterns and draw conclusions from historical data. One of these patterns could be an increased tendency for a stock price to move up on Tuesdays.

Armed with that information, a trader could set up an algorithm designed to buy shares at regular intervals on Tuesdays, after which they’d sit back and wait for profits to roll in. Of course, what works one day might not work another; that's why statistical learning is such an important part of algorithmic trading.

If you're looking to get into algorithmic trading, consider getting some basic knowledge of statistics. You can start by taking a look at our list of free courses.

And if you're interested in honing your skills further, check out Coursera's Data Analysis Specialization or its Machine Learning Specialization—both are great options for those who want to improve their analytical capabilities.

 

They should understand modern programming languages

While it is possible to create advanced algorithms without any programming language knowledge, a trader who wants to implement data science into their trading strategy should at least understand how to program in one of today’s most popular languages.

It doesn’t matter if they do not use any of that knowledge while developing their algorithmic trading systems; as long as they understand how programming works on a basic level, they will be able to identify which types of data science work best for their strategies.

For example, some traders develop algorithms using Bayesian statistical methods while others may use big data frameworks. While neither method is better than another when it comes to making trades, it is important for a trader to understand both so that they can pick what will work best for them.

 

They should focus on signal processing

Machine learning is a high-level approach to identifying patterns in data. It’s used to build models that learn from data, rather than making explicit rules (such as a computer program) or using expert knowledge (like an expert trader).

With machine learning, there are two main tasks: feature extraction — which uses statistical analysis to find features that characterize your data — and pattern recognition. Although it can work well with other approaches for creating trading algorithms, machine learning can also be done by itself.

As opposed to older approaches, it doesn't require advanced mathematical skills nor does it require a lot of computing power. This makes it easier for traders without PhDs in statistics to develop their own algorithmic trading strategies.

 

Understanding machine learning methods is important

Machine learning techniques are becoming more popular, as their improved performance can provide a competitive advantage for businesses.

But not all machine learning models are equally suited to every task; when developing one for a particular purpose, it's important to have some understanding of algorithms that can be applied.

You don't need to become an expert in machine learning—an awareness of when a particular method might be useful is often enough—but it's good to know something about these methods if you're going to use them on your trading desk.

It may even help you spot opportunities if you come across promising-looking methods that are less well-suited than they seem. Finally, knowledge of machine learning will make your communications with data scientists easier, since both parties will understand each other's terminology and approach.

Machine learning and AI both play a role here

The first can help you identify strong trading opportunities and make your trading approach more automated.

The second, meanwhile, can help to speed up some of these processes—which can be incredibly useful in an industry where fractions of a second could mean significant losses or gains. Additionally, AI could also play a role in determining who might have access to information on trading algorithms.

These new developments could have interesting legal implications around insider trading and there’s already been some talk about banning them altogether.

AI is likely going to play an important role here; identifying whether a person is simply skilled at forecasting or whether they have access to non-public information will be extremely difficult for humans but much easier for machines.

 

Conclusion

Although data science and algorithmic trading seem like a match made in heaven, data science knowledge is not necessary for becoming an algorithmic trader. In reality, most institutional traders do not have a data science background.

However, it is possible to become an algorithmic trader with a general knowledge of computer programming and machine learning. The important thing to note is that while you might be able to learn everything you need on your own through books or online courses, that can take years.

So if you are serious about making algorithmic trading your career path, consider formal education instead.

There are schools like New York Code + Design Academy (NYCDA) that offer programs specifically geared towards those interested in algorithmic trading careers as well as those interested in Data Analytics roles more generally.