Jim Simons will undoubtedly be remembered as a trailblazer, introducing mathematics (including algorithms, big data, and machine learning) to the markets long before the advent of quantitative trading. A mathematical prodigy, he earned his math degree and PhD before the age of 24. Unlike Warren Buffett, who began investing at the tender age of 11, Simons entered the stock market in his late thirties. Despite his successful career leading SUNY Stony Brook’s math department, Simons found life lacking in excitement. He didn’t see himself heading the Math department for the rest of his life and he still had a fire in him.

His foray into trading began with wedding gifts that he invested in stocks and then commodities, initially seeing gains only to watch them evaporate. This spurred him to seek a better approach to investing. He found that no matter what analysis he did on a stock it seemed that the stock had a life of its own. His experience aiding the NSA in cracking Soviet codes taught him a crucial lesson: identifying patterns in seemingly random data. Initially, like many of us, he invested for the long term, but lackluster results led him to change his strategy at the suggestion of his business partner, Berlekamp. Berlekamp advocated for the fund to adopt a casino-like approach using scientific methods — making numerous trades with the aim of winning more than half of them to turn a profit. They didn’t need to analyze individual companies; instead, they gathered vast amounts of data to identify patterns and refine their model accordingly.

The mantra of the law of large numbers guided their approach. At one point, they were responsible for 5% of trades on the NYSE. Their strategy relied on leveraging vast amounts of data — up to 40 terabytes per day — and continually refining their mathematical models through backtesting. To achieve this, Simons recruited the best mathematicians and computer scientists, valuing their expertise over traditional investing knowledge.

Ultimately, their model sought to discern signals from noise, with individual trades being part of a larger whole. They didn’t need to make profits on every single trade. With just 52% of trades profitable on a consistent basis returns would be outstanding. This shift improved the fund’s risk- return profile, attracting new investors who were seeking alternative investments. Returns were further boosted by expanding into international equities and utilizing basket options, resulting in a Sharpe ratio of 6 and leverage increasing to 20 times. Returns were also aided by the fact that intraday trades could be disguised as long-term trades thus lowering taxes by more than $6 billion. The IRS sued and after a lengthy trial Simons and Renaissance settled paying over $7 billion. But in the end Renaissance’s model was unstoppable becoming a money machine, printing money on up and down days.

The book imparts valuable investing and life lessons gleaned from Simons’ fascinating journey. A maverick thinker, Simons learned from his father the importance of forging one’s path rather than conforming to expectations. Always enthusiastic and adventurous, he remained open-minded and willing to entertain diverse perspectives, even conceding when proven wrong. Despite his remarkable success, he remained humble, attributing some of his achievements to luck and displaying moments of self-doubt. What struck me the most was the Simons is not only an outstanding investor but also a very skillful manager. He maintained an openness to new ideas, recognizing their potential irrespective of their source and kept his doors open to every employee reaching out.

Simons’ philanthropy is also noteworthy; he has donated over $700 million to SUNY Stony Brook, the institution where he led a department, and established a nonprofit granting funds to math teachers in New York while supporting scientific research programs, even collaborating with the son of a former partner. In the end, you come to the conclusion that Simons went to all this to have a disproportionate amount of influence on matters he considers important: health, education and science.

I appreciated how the book skillfully connects various experiences in Simons’ life, illustrating their relevance in unexpected ways, such as their impact on the 2016 elections or how his management style honed in the math department. Zuckerman deserves credit for elucidating complex mathematical concepts in a straightforward manner, providing insights into Simons’ partners, and presenting well- researched anecdotes derived from extensive investigation. Of particular interest is the revelation that Simons manages his personal funds based on judgment rather than mathematical models — an irony worth noting.

In conclusion, I highly recommend this book to anyone interested in delving deeper into investing. It’s both entertaining and enlightening, offering valuable insights from a man who has outperformed some of the most renowned investors in history.