In financial services, a quant has to do the impossible: Create certainty out of uncertainty. Use data analysis to uncover the almost imperceptible signals that indicate everything from the risk incurred by a potential investment to the most profitable time to sell stocks.
Delivering accurate, decision-driving insights is one of a quant’s most important functions. So, in an ideal world, they would only be measured on their accuracy.
But that’s not how the financial services industry works. In this sector, everything can change in a nanosecond. Every moment is an opportunity to increase profits and grab competitive advantage — or lose it all.
This is the tension at the heart of the quant’s role: Quants can’t just deliver accurate insights. They also need to deliver them fast. A perfectly accurate insight is useless if it arrives too late.
It’s this balancing act between accuracy and timeliness that underlies most of the challenges faced by quants and quant devs. Let’s dive into four of the biggest ones — and consider how quant devs can start to solve them.
It’s hard enough to build a financial model sophisticated enough to pinpoint risk in a changing market or spot the precise moment that a trade should be made. It’s even harder to do so efficiently.
Quant models are some of the industry’s most complex simulations. Building them takes huge amounts of complex data and intense mathematical computation. And, once they’re built, they need to be rigorously validated and approved before they are put into use.
With such a complicated process to follow, small inefficiencies in processes, hardware, or software can add up to hours or days of wasted time.
Stability and security
Quants are constantly handling highly sensitive data — data that, if leaked, could lead to anything from customer identity theft to insider info reaching the hands of a company’s competitors.
Protecting this sensitive information requires quants to complete a complex series of tasks, covering everything from encryption to tight data access controls. The challenge is making this process as secure as possible, without the constant safety checks slowing down either the software or an individual quant.
There are few industries more highly regulated than finance. Since quants deal with such high volumes of sensitive data, they need to be way ahead of the game when it comes to complying with those regulations. At least, if they want to avoid fines and keep their jobs.
Keeping track of all those regulatory requirements and complying with them is hard enough, particularly for quants or build tool experts whose clientele spans multiple countries and legislative regions.
But once again, quants are always under pressure to combine compliance with speed and accuracy. They’re looking for ways to protect sensitive data and make sure it is still usable and accessible enough to extract insights from it.
More data, higher standards
By their very nature, quant builds are complicated things. They have to be, to capture the subtle nuances of financial markets, corporate risk, and the day-to-day strategies of financial services companies.
But these already complex models are becoming even more intricate. As data volumes explode, quants need more sophisticated statistical techniques to extract accurate insights. Of course, this also gives quants the opportunity to build more holistic, more accurate models that take a wider number of factors into account. But these new and improved models take longer to build, and more experimentation to get right. Which means any inefficiency in the build process is multiplied 10 times over.
There is one other factor driving the development of these increasingly complex models: the popularity of quants themselves.
Hiring quants and quant devs is becoming a matter of course for financial services companies. It’s now generally accepted that financial services companies need quantitative analysis on their side if they want to keep up with the competition.
But no finserv company is happy to simply keep pace. They want to outstrip the rest of the pack. They need their quants to develop models that give them a real competitive advantage. Which means that quants are under constant pressure to do more, dive deeper, predict further into the future — all without losing pace or allowing the company to fall behind.
The power of C++ acceleration tools for quant devs
Everyone involved in quant is on a constant quest to move faster — without sacrificing the accuracy of their insights or the security of their processes. It’s a constant push to shave precious nanoseconds off every stage of the process, cut out inefficiencies, and use compute power as effectively as possible.
That’s part of the reason why quant devs rely so heavily on C++: It’s an efficient, secure language that makes it relatively easy to move fast without breaking things.
But there’s still room for improvement. In one 2022 survey, C++ devs reported that long builds were their second-biggest pain point. Those long builds are frustrating in any sector, but in the highly time-pressured world of quantitative analysis, they’re a major headache.
That’s why C++ dev acceleration tools are becoming a crucial tool for quant. The best accelerators cut out the inefficiencies and delays that slow down an already complex and time-consuming process.
Want to find out more about how a C++ dev accelerator can help quant devs tackle their biggest challenges? Check out our whitepaper to find out:
- How quant devs can find a C++ accelerator that’s right for them
- How a C++ accelerator works in action (including a case study that shows the power of Incredibuild’s accelerator for a team of quants)
- How to make sure your C++ accelerator implementation is a success