IT Rise of the Machines
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With the hopes of maximizing their return on investment, stock traders increasingly rely on quantitative trading, a concept that leverages data analysis and algorithms in an attempt to “buy low” and “sell high” as much as possible.

Widely used by financial institutions and hedge funds, quantitative trading is another example – like blockchain – of the impact technology innovations are making on the financial world. What follows is a high-level overview of the technology behind quantitative trading and its implications upon the world of finance. Maybe a new role developing algorithms for quants is in your or your company’s future?

Turning Mathematics into Trading Algorithms

At its heart, quantitative trading involves the analysis of various trading techniques and making a model of them using mathematical functions. Algorithms are then developed to execute these models within a computer program. Just like any other software application, a QA process then follows with a resultant optimization of the underlying code based on the test results.

Eventually, these trading applications are executed in production environments accessing financial markets in real time with actual capital. Needless to say, this is one arena where the importance software testing is paramount. In the past, hundreds of millions of dollars have been lost in less than an hour due to a bug in trading software.

Detailed data analysis of historical market data plays an important role in developing strong mathematical models used in quantitative trading. The ultimate goal is forecasting profitable trades based on certain market conditions. Computing horsepower is vital in doing the heavy lifting required when analyzing massive amounts of trading data.

Dynamic Trading Models are Essential

One of the major advantages of quantitative trading is a computer program’s ability to quickly analyze data – both real-time market information and the results of its previous trades. Mere humans simply can’t keep up. Software is also immune to the emotional reactions to the market that sometimes lead to irrational trading mistakes.

Considering the fickle nature of the world’s financial markets, successful quantitative trading models are dynamic enough to react to these changes. Many models fail after the market shifts noticeably, making it vital to constantly vet their efficacy. When combined with the risk of software errors, these are the main disadvantages of the practice.

Are you looking for an IT job in the Finance Industry?

If developing quantitative trading models and algorithms piques your interest, talk to the knowledgeable team at The Ceres Group. As one of the top technology staffing agencies in the Boston area, we know what financial companies are looking for software development talent. Contact us today to find an IT job that will advance your career.


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