May 20


Jim Simons – The Man Who Solved the Markets

By David Bergstrom

May 20, 2024

Who is Jim Simons?

Jim Simons, born in 1938, is often referred to as “the most successful hedge fund manager of all time” and the “greatest investor on Wall Street”. The Financial Times named him “the world’s smartest billionaire” in 2006. Simons is the founder of Renaissance Technologies, a quantitative trading hedge fund based out of New York, that boasts an incredible 66.1% average annual return since 1988. Simons’ approach to the markets is fully systematic and there is so much to glean from his work as Jim Simons launched the quant revolution. This article will examine Simons’ many accomplishments and a few takeaways that can help us become better system traders.

If you are not interested in his origins and rather skip ahead to the systematic trading parts choose below


Simons began as a mathematician and arguably had a full career before leaving to start Renaissance Technologies. He studied mathematics at Massachusetts Institute of Technology (MIT) and later got his Ph.D. from the University of California, Berkeley.  

Simons put his mathematics skills to the test working with the National Security Agency (NSA) to break codes during the Cold War while simultaneously teaching at MIT and later Harvard University. After his public opposition to the Vietnam War, Simons was forced out but later appointed chairman of the math department at Stony Brook University.  

Simons is known for his studies in pattern recognition and developed the Chern-Simons form with Shing-Shen Chern and is credited with contributions toward the development of String Theory. His theoretical framework combined geometry, topology, and quantum field theory.

Simons has formally received recognition in mathematics, geometry, and topology before shifting his focus to finance in the late 1970s

  • AMS Oswald Veblen Prize in Geometry 1976
  • Elected to National Academy of Sciences 2014

The Greatest Trader on Wall Street

Jim Simons founded Renaissance Technologies in 1982 as a 40-year old retired mathematics professor. However, he left his job/academia to start his first hedge fund Monemetrics in 1978. This fund was moderately successful and employed both fundamental and technical approaches to the market, but Simons felt “gut wrenched” by the emotional swings.

Simons decided to use a purely systematic approach to avoid emotional rollercoasters and avoid common trading biases that trip up most traders. Simons staffed the new fund, Renaissance Technologies, with mathematicians, computer scientists, and physicists to pioneer a new approach to algorithmic trading.

Since 1988, Jim Simons’ Renaissance Technologies flagship fund, the Medallion Fund, has returned an average of 66.1% per year which shatters any other publicly available returns over the same horizon. Later I will dissect what little information is available about Simons’ (and RenTec) strategies, approach and success. Skip ahead

Simons is quoted as saying his best algorithm has always been

“you get smart people together. You give them a lot of freedom. Create an atmosphere where everyone talks to everyone else. They’re not hiding in a corner with their own little thing. They talk to everybody else. And you provide the best infrastructure, the best computers and so on that people can work with. And make everyone partners. So that was the model that we used in Renaissance. So we would bring in smart folks and they didn’t know anything about finance, but they learned.”


Jim Simons has given over $2.7 billion to philanthropic causes ranging from education and health to scientific research. In 1994, Simons and his wife Marilyn Hawrys Simons co-founded the Simons Foundation which later established the Simons Foundation Autism Research Initiative in 2003.

This foundation founded Math for America in 2004 and Simons later doubled the initial $25 million pledge in 2006.

Simons’ alma maters and former academic employers have been benefactors from Simons’ trading successes with large contributions to University of California Berkley, MIT and Stony Brook. His most recent contributions have been aimed to advance computational science and mathematics.

Jim Simons Renaissance Technologies Medallion Fund

Medallion Fund Beginnings

Simons started in the late 1970s and was met with some initial success. However, Simons questions whether the early success was more luck than skill. Primarily focused on commodity futures using fundamental and technical analysis, Simons never achieved the emotional clarity or systematic approach to have full trust.

In 1988, Simons set up the Medallion Fund with a focus on huge amounts of data diversifying across timeframe, asset class, and pattern. Adding the stock market how Jim Simons and team gained additional success. However, the big success did not come until the early 1990s when he brought Bob Mercer and Peter Brown on board from IBM.

Mercer was a large donor in the 2016 US Presidential election (Trump) and eventually left the firm in 2017 after political differences.

Medallion Fund Fees

Most hedge funds charge “2 and 20” which is shorthand for an annual 2% management fee and a 20% rake on performance. Simons knew he had something special and offered investors “5 and 20” in the early days of Medallion but has since moved toward a “5 and 44” structure. This %5 management fee and 44% profit take may be the most aggressive fees in the industry, but Simons and the Medallion Fund have earned them. Due to the large fees, the net returns are more in line with a 39.1% average annual return instead of the earlier stated 66.1%. However, 39.1% is still remarkable with such a large capital base.

Medallion Fund Performance

Gregory Zuckerman’s book The Man Who Solved the Market shares Medallion’s annual returns in Appendix 1.

Now most of the investors and owners in the fund are employees as Medallion takes no outside capital.

Assuming the net returns column over the most recent 20-year span then an investor would have experienced the growth of a $1,000 investment into a whopping $906,933.26.

Medallion Fund vs. S&P500

Assuming the past 20 years S&P 500 returns then the same $1,000 invested in the S&P500 would return an impressive $2,039.12. This is a far cry (about 500x smaller) than the return generated by the Medallion fund. There is beating the market, capturing alpha, and then the universe that Medallion and Simons are from. Here are the side-by-side returns:

Medallion Fund Takeaways

  • Fully systematic
  • Data-driven
  • Diversify across timeframe, asset class
  • Hire smart, collaborative people
  • Win rate is not as important as trading edge
  • Compounding creates wealth

Jim Simons Net Worth and Personal Life

Billionaire Status and Forbes List

Jim Simons is the current richest trader in the world with a $28.1 billion dollar net worth ranking him on the Forbes list. He has the highest net worth of any trader or money manager on the Forbes wealthiest list. For example, Simons has a wealth more than three times that of George Soros.

Archimedes Yacht

Jim Simons owns a $100 million yacht with an annual running cost of $8 to $10 million. The 68-meter (222 ft) yacht is appropriately named Archimedes after the famous mathematician. Archimedes the superyacht sleeps 12 in 6 cabins and has room for 18 crew members on board with a dining hall for 20 guests. The yacht was built at the Dutch yacht builder Royal Van Lent and delivered to Simons in 2008. Let’s face it, these big boy toys are half the allure of trading.

Private Jet

Simons also owns a $70 million Gulfstream G650 private jet. You can charter one of these bad boys for about $10,500 per hour, if interested.


Unfortunately, Simons’ 84-plus years have not been without tragedy. In 1996, his son Paul was struck and killed by a car while riding his bicycle on Long Island. Paul was 34 at the time. In 2003, Simons’ son Nicholas, 24 at the time, drowned while on a trip to Bali, Indonesia.

Bonus Fact

Simons does not like, nor does he wear socks. Not sure if there is any correlation to his trading success but perhaps if you are struggling remove the feet prisons. I don’t know.

Jim Simons Quant Trading Insights

As all traders, I am always reading, learning, and trying to find a new piece that can help improve performance, limit drawdown or push me forward. The aforementioned book about Simons came out in 2019 and I devoured it in a weekend. I originally posted some thoughts on a See It Market blog here: 5 Lessons From One of the Greatest Traders of All Time (Jim Simons) – See It Market but want to elaborate on the insights and their application to system trading, Build Alpha and my own musings.

Edge is Important – Not Why It Exists

Simons does not care to explain the hypothesis or explanation of why a predictor or model works. If a predictor has edge and is statistically significant then why bother with some explanation for why it must work.

If the edge can be explained, then others are probably aware of the edge and others will soon trade it away.

In other words, data mining is ok. I’ve long defended this approach since the launch of Build Alpha and is nice to hear Simons echo similar ideas.

In my opinion, it is possible we cannot comprehend why a pattern or edge exists because it exists in a dimension too complex for our current understanding. Discarding an edge because we cannot explain it is a mistake.

Remove human bias and let the data show you where, when, and how to trade. Let others overlook these “unexplainable” patterns.

Excerpts to drive the point home

“Simons and his researchers didn’t believe in spending much time proposing and testing their own intuitive trade ideas. They let the data point them to the anomalies signaling opportunity. They also didn’t think it made sense to worry about why these phenomena existed. All that mattered was that they happened frequently enough to include in their updated trading system, and that they could be tested to ensure they weren’t statistical flukes”. (pg 109) 

“Simons and his colleagues hadn’t spent too much time wondering why their growing collection of algorithms predicted prices so presciently. They were scientists and mathematicians, not analysts or economists. If certain signals produced results that were statistically significant, that was enough to include them in the trading model” (pg 150)

“I don’t know why the planets orbit the sun. That doesn’t mean I can’t predict them” – Simons (pg 151)

“More than half of the trading signals Simons’s team was discovering were non-intuitive, or those they couldn’t fully understand. Most quant firms ignore signals if they can’t develop a reasonable hypothesis to explain them, but Simons and his colleagues never liked spending too much time searching for the causes of market phenomena. If their signals met various measures of statistical strength, they were comfortable wagering on them.” (pg 204)

“Volume divided by price change three days earlier, yes, we’d include that” – Simons (pg 204)

To read on how to quantify trading edge check this out

Stick to Your Model

You cannot run a trading business that relies on your emotional state or gut-instincts. There are too many days where you may be sick, tired, hungover, dealing with personal issues and what happens when these days line up with the most opportunistic market days?

Simons is 100% systematic and preaches the importance of treating trading like a business that can be backtested, modelled and followed. Here’s a quick minute long video where he explains why:

You Need a Great Team

Simons is no doubt successful on his own right, but Medallion’s performance really skyrocketed when Simons started building his team. Jim doubled salaries to hire people away from prestigious positions in tech, science, and academia.

Being around other smart, successful, and innovative people will only push you farther. The old saying “if you want to go fast go alone but if you want to go far go together” applies here.

Seek out other like-minded individuals and be open to sharing ideas. This is one of the greatest reasons I keep Build Alpha open to other traders. The ideas, inputs, and feedback help me create better software which allows all of us to create better portfolios. So, thank you for all the contributions, ideas, sound boards, etc.

Edge Does Not Have to be Big

Renaissance searched for “overlooked” edges and joked about a 50.75%-win rate while utilizing the law of large numbers to win in the long-run. Seeking the perfect entry or exit or the one strategy is often a failed approach. Ren Tech generated astronomical returns with a nearly 50%-win rate. Much more can be gained combining unique smaller edges together than wasting time hunting for the holy grail. 

Some of the trading signals they identified weren’t especially novel or sophisticated. But many traders had ignored them. (Page 112)

“We’re right 50.75 percent of the time… but we’re 100 percent right 50.75 percent of the time. You can make billions that way” (pg 272)

The Man Who Solved the Market – Gregory Zuckerman

Most of the quotes in this article are from this tremendous book. The book released in November 2019 and does such a great job covering Simons. Simons and Renaissance are very secretive about their strategies but there are a few insights (if you read between the lines) in the book. 

Jim Simons Interviews and Videos

James Simons Full Length Numberphile Interview

The Mathematician who cracked Wall Street

James Simons – Mathematics, Common Sense and Good Luck

Famous Jim Simons Quotes

These quotes come from Zuckerman’s book along with page number. You can read into the lines and see why Simons is such a staunch supporter of the systematic trading approach.

Early on, he traded like others, relying on intuition and instinct, but the ups and downs left Simons sick to his stomach. (Page 2) 

Simons and his colleagues used mathematics to determine the set of states best fitting the observed pricing data; their model then made its bets accordingly. The why’s didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states. (Page 29)

“I don’t want to have to worry about the market every minute. I want models that will make money while I sleep”, Simons said. “A pure system without humans interfering.” (Page 56)

If a currency went down three days in a row, what were the odds of it going down a fourth day? Do gold prices lead silver prices? Might wheat prices predict gold and other commodity prices? Simons even explored whether natural phenomena affected prices. (Page 57)

Their goal remained the same: scrutinize historic price information to discover sequences that might repeat, under the assumption that investors will exhibit similar behavior in the future. Simon’s team viewed the approach as sharing some similarities with technical trading. The Wall Street establishment generally viewed this type of trading as something of a dark art, but Berlekamp and his colleagues were convinced it could work, if done in a sophisticated and scientific manner – but only if their trading focused on short-term shifts rather than longer-term trends.  (Page 108)

Berlekamp also argued that buying and selling infrequently magnifies the consequences of each move. Mess up a couple of times, and your portfolio could be doomed. Make a lot of trades, however, and each individual move is less important, reducing a portfolio’s overall risk. (Page 108) 

Humans are most predictable in times of high stress – they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past….we learned to take advantage.” (Page 153)

“Any time you hear financial experts talking about how the market went up because of such and such – remember it’s all nonsense”, Brown later would say.  (Page 199)

By 1997, though, more than half of the trading signals Simon’s team was discovering were nonintuitive, or those they couldn’t fully understand. (Page 203)

“If there were signals that made a lot of sense that were very strong, they would have long-ago been traded out”, Brown explained. “There are signals that you can’t understand, but they’re there, and they can be relatively strong.” (Page 204)

The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. (Page 272)

his larger point was that Renaissance enjoyed a slight advantage in it collection of thousands of simultaneous trades, one that was large and consistent enough to make an enormous fortune. (Page 272)

The inefficiencies are so complex they are, in a sense, hidden in the markets in code,” a staffer says. “RenTec decrypts them. we find them across time, across risk factors, across sectors and industries.” (Page 273)

For all the unique data, computer firepower, special talent, and trading and risk-management expertise Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market (Page 317)


Thanks for reading,


>> By Dave Bergstrom from blog buildalpha

David Bergstrom

About the author

My name is David Bergstrom, and I am the guy behind the Build Alpha software. I have spent many years researching, building, testing, and implementing market making and trading strategies for a high frequency trading firm, a handful of CTAs, individual clients, registered money managers, and even aspiring retail traders. I am a self taught programmer who uses C++, C#, Python, Perl, Java and even TradeStation’s EasyLanguage. Most of my experience has led me to a series of repeatable processes to find, create, test, and implement trading ideas. Build Alpha is the culmination of this process from start to finish.

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