Three Ways Quants Revolutionize Financial Services Software

Joseph Sibony
Joseph Sibony reading time: 5 minutes
September 7, 2023

The financial services software industry loves quant.

Why? Because it’s a source of certainty — or, at least, something like certainty — in an industry that’s inherently unpredictable.

Markets shift. Profits rise and fall. Deals are made, fall through, and are made again. From the trading floor to the boardroom, a nanosecond can be the difference between wild success and major losses.

And, without quantitative analysis, it’s almost impossible to predict when, where, and how any of these things could happen.

Better strategies, better outcomes: The power of quant

Quantitative methods allow companies to make decisions based on data and statistical analysis, rather than intuition or guesswork. Extracting insights that lead to more effective strategies and better outcomes.

This is particularly true when it comes to optimizing the performance of financial software systems. Using mathematical models and algorithms, developers can support quant by designing systems that process transactions more efficiently, calculate risk and make predictions more accurately, handle larger volumes of data, and provide faster, more accurate results.

In such a fast-paced market, these improvements can deliver a dramatic competitive advantage.

But what does that look like in practice?

Let’s take a look at some of the more specific applications of quant in the financial services software industry.

Uses of quant in financial services software

Trading software

Major trading companies are trading every moment of the day — and watching the value of stocks fluctuate from second to second as the market reacts.

With prices changing so quickly, a nanosecond can mean the difference between a stock exchange that delivers a staggering profit or a devastating loss. Companies have been made and destroyed on the tick of a clock’s second hand.

The result is that trading software companies desperately need fast, reliable software that can support this kind of rapid decision-making. Some of this software centers around “algorithmic trading”, using quantitative analysis to help users buy and sell stocks at the optimum price and time with inhuman accuracy.

Other quants deliver companies — via tools created by quant software devs — rapid recommendations that allow them to optimize split-second decisions on the trading floor. Quant is also vital for model creation (as well as building tools that can be used to create models), swing trading, and risk analysis.

Risk management

“Risk quants” develop tools that monitor and control risks relating to financial stability, reputation, and compliance.

Quants in this field will use their models to evaluate everything from credit risk to market, liquidity, operational, or regulatory risk.

The output of a risk quant’s analysis is often a statistic called the “value at risk” or VaR. This measures the extent of any possible financial losses that a firm or portfolio could incur. To calculate this, quants will assess the amount of potential loss and the probability that the loss will occur within a specific time frame.

Risk quants are constantly working to help financial services companies predict the future, so that they can take action to minimize or completely avoid loss in the event that those risks become reality.

Financial modeling

Financial modeling is a broad field. Otherwise known as front-office quants, quants in this field provide the models needed to automate financial processes, improve data analysis, and enhance risk management. They’ll also support some core banking tasks and some processes like portfolio management or market and trade surveillance.

Front-office quants tend to be in constant contact with traders, funneling them the insights they need to make the best possible decisions. They’re also increasingly important for business development teams who are scouting for new investment opportunities, evaluating risk vs. reward, and recommending the most fruitful course of action.

Getting the work done: The role of C++ in quant

Now that we’ve covered what quants do in financial services software, let’s consider how they do it.

Quants use a wide range of programming languages to get the job done, from R, to MatLab, Stata, and Java. But across the industry, one language reigns supreme: C++ (with Python arguably coming a close second).

Scroll through the dev files of any trading firm or FinServ app developer, and you’ll be greeted with row after row of .cpp file extensions.

But why is C++ so popular?

C++ has a few features that make it perfect for quant:

  • C++ is efficient – C++ is compiled directly into machine code, so its compile time is much faster than many other languages. It also has a much faster execution time. This makes it perfect for time-sensitive quant applications.
  • C++ is portable – C++ is platform independent, so it’s easy to shift programs written in C++ to other platforms.
  • C++ gives devs control over memory management – C++ doesn’t use a Garbage collector, which means devs have complete control over memory management. For some devs, this is a chance to get total control over where and how sensitive financial information is stored.
  • C++ has a huge community behind it – C++ users have spent years developing a huge library of guides, courses, tips, and tricks for fellow devs. If something goes wrong with C++, quant devs can almost always find the advice and support they need to fix the problem. This huge fanbase also means that there are plenty of C++ libraries available to help speed up the dev process.
  • C++ integrates well with legacy systems – As mentioned in the introduction, C++ has been a favorite of financial services companies for years now — which means that most of their systems are probably already built on C++. That makes it much easier to integrate C++ applications.

Fintech moves fast. C++ acceleration helps quants move faster.

Quants play a huge range of roles in financial services software development. But there’s one common denominator: a constant pressure to deliver results fast, without sacrificing accuracy.

That’s why more and more quant devs are seeking out C++ acceleration tools that cut down on build times. Clawing back the extra nanoseconds that can mean success or failure and give companies an extra competitive edge.

Think your quant dev team could benefit from a C++ accelerator? We dive deeper into the challenges and opportunities involved in C++ acceleration for quants in our latest whitepaper. Head here to find out more.

Joseph Sibony
Joseph Sibony reading time: 5 minutes minutes September 7, 2023
September 7, 2023

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