Renaissance Technologies, the enigmatic hedge fund founded by Jim Simons, delivered unheard-of returns for 30 years.

The firm employs a quantitative and systematic approach to investing that looks to exploit different market patterns, sequences, relationships, and anomalies.

The firm charges a sky-high 5% management fee and a 44% performance fee.

Click here for more BI Prime stories.

As an investor, the idea of a fund generating 66% annualized returns (before fees) over a 30-year period seems nothing short of preposterous.

After all, conventional wisdom says that markets are efficient, competition is steep, and opportunities for outsize gains present themselves only once in a blue moon.

But that's exactly what Renaissance Technologies — the quantitative hedge fund founded by the math whiz Jim Simons — has accomplished. What's arguably even more impressive is that Simons and his subordinates knew almost nothing about business when they got into it. It simply didn't matter.

In Gregory Zuckerman's new book, "The Man Who Solved the Market," readers are given a peek into the inner workings of one of the most secretive and successful hedge funds the world has ever seen — one that started on a whim.

Simons' story began long before he revolutionized the investment world, when he spent his days cracking codes for the National Security Agency and teaching math at Stony Brook University.

"I was immersed in mathematics, but I never felt quite like a member of the mathematics community," Simons said. "I always had a foot [outside that world]."

After growing tired of academia, he decided he'd try his hand at investing. New challenges never seemed to faze him, and Simons wanted to apply his mathematical aptitude to the world of finance.

Simons' background — steeped in pattern recognition and code cracking — wound up transferring into an investment approach that was different from anything Wall Street had seen before.

Instead of analyzing balance sheets and business metrics, he spent his time scouring pricing data, looking for any inkling of a pattern, relationship, sequence, or anomaly buried in the numbers.

Simons' search was unrelenting. Historical data was sequestered into tiny fragments, vetted tirelessly for potential opportunities, back-tested, and then applied to emerging trends in order to verify usability. The firm referred to these finds as "nonrandom trading effects" and hoped to take full advantage of these relationships in their model.

Peculiarities that the firm uncovered included:

prices falling for certain investments before economic reports, and rising directly after

Monday's pricing action following that of Friday's, while Tuesday's pricing action would revert to earlier trends

the "24-hour effect," in which pricing action in the previous day would predict the action in the next.

It's important to note that trades based on the scenarios above worked only some of the time. However, as long as the strategy was more successful than not, profits would keep growing.

Returns were nothing short of ridiculous.

Amid the tech bubble — the period between 2000 and 2002 when markets were melting down — Simons' bellwether Medallion fund garnered returns of 128.1% in 2000, 56.6% in 2001, and 51.1% in 2002, before fees.

"We make money from the reactions people have to price moves," an employee of Renaissance Technologies said.

Splitting trading into 5-minute 'bars'

In time, Simons and his subordinates would carve the trading day into five-minute "bars" as part of their constant search for new ideas.

In his book, Zuckerman demonstrates the level of granularity present in the Renaissance Technologies' vetting process.

"Did the 188th five-minute bar in the cocoa-futures market regularly fall on days investors got nervous, while bar 199 usually rebounded?" Zuckerman wrote. "Perhaps bar 50 in the gold market saw strong buying on days investors worried about inflation but bar 63 often showed weakness?"

Once a thesis was developed to better understand this pricing action, an algorithm was built to predict where prices would move in the future. Though this practice may seem easy to employ in theory, it incorporated large swaths of data points, complex mathematical models, and tons of computational force.

Simons' differentiated approach to investing removed all potential for emotion and human error. Buys and sells were executed automatically. Various algorithms and lines of code parsed through millions of variables to apply trades best suited to take advantage of the prevailing environment.

But even with all of that under consideration, it's important to note that Simons didn't find success overnight — nor did he develop his firm's proprietary models alone. He tapped into a vast network of pioneering mathematical minds in order to keep one step ahead of the competition.

Ultimately, his ability to harness the intellectual prowess of his peers was the driving force behind Renaissance's jaw-dropping returns.