Quant hedge funds have significant power over the market—they control nearly a third of the daily liquid volume traded. When stocks make strange moves, such as when O’Reilly Auto Parts shares started to move when Bill O’Reilly was in the headlines, traders often blame the unusual activity on quant trading algorithms.

The O’Reilly example shows that new quantitative approaches to trading are still in their infancy, and that there’s plenty of room for innovation in the quant space. And though there is a veritable gold rush for alternative data such as satellite imagery lately, some are still laser-focused on cracking the code around price analysis.

Tradagon, a fintech company that develops automated trading strategies based on price analysis, is a case in point.

“While knowing the number of cars in a Wal-Mart parking lot may have some bearing on the stock’s or the overall economy’s trajectory, we’re not convinced alternative data has the predictive capacity to really help you pinpoint the starts and ends of major price moves in real time,” said Ramesh Dhingra, Tradagon CTO and cofounder. “The holy grail is still hidden in the price data itself, and how you can surface the confluence points in all of the undercurrents of that data.”

The company has created “Behavioral Calculus,” a methodology that addresses the faulty assumptions it says are prevalent in traditional technical analysis concepts. “The​ ​root​ ​of​ ​the​ ​problem​ ​is​ ​that​ ​there​ ​is​ ​a​ ​vast​ ​analytical​ ​gap​ ​between​ ​’momentum’​ ​as​ ​perceived​ ​in​ ​price​ ​action​ ​-​ ​or in​ ​any​ ​crowd​ ​behavior,​ ​for​ ​that​ ​matter​ ​-​ ​and​ ​“momentum”​ ​as​ ​defined​ ​in​ ​physics.​ ​With​ ​Behavioral​ ​Calculus,​ ​we​ ​have bridged​ ​that​ ​gap,” Tradagon asserts in its new whitepaper.

Essentially, Tradagon says you can think of the market as a big crowd of investors, and you can get closer to understanding it using established knowledge about crowd dynamics.

Tradagon’s whitepaper claims that “confluence triggers crowd behavior” and sets off trends.

“Very​ ​often​ ​a trend​ ​will​ ​persist​ ​indefinitely​ ​until​ ​a​ ​new,​ ​more​ ​compelling​ ​trend​ ​takes​ ​its​ ​place,” the paper said. “​This​ ​is​ ​exactly​ ​what​ ​happens​ ​in​ ​price​ ​action,​ ​and very​ ​often​ ​there​ ​is​ ​no​ ​real​ ​explanation​ ​for​ ​even​ ​major​ ​price​ ​moves​ ​-​ ​even​ ​after​ ​the​ ​fact​ ​-​ ​which​ ​is​ ​a​ ​hallmark​ ​of​ ​crowd behavior.”

The model works by feeding real-time or historical price data (or economic data) into the calculus, which “performs​ ​a​ ​series​ ​of​ ​powerful​ ​decompositions,​ ​normalizations,​ ​and​ ​other​ ​transformations,​ ​and​ ​finally​ ​feature​ ​processing​” to automatically​ ​generate​ ​entry/exit/hold​ ​signals.

“It is virtually the same fully automated strategy that works across equities, currencies, t-bonds and commodities, with the same few parameters, and it works because we’ve tapped into something really fundamental here,” it says.

To learn more about how funds can take advantage of these signals, read the full whitepaper on Tradagon’s site.

Tradagon is an editorial and data partner of Benzinga.

Photo credit: public domain