4 Lessons From The Greatest Quant Trader of All Time

Last Updated on September 22, 2023 by Mark Ursell

I recently read the book – The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. It is an entertaining and eye-opening read about the man who founded the original Quant fund.

In this article and video, I discuss 4 lessons from Jim Simons – the greatest quant trader of all time.

If you are unfamiliar with Simons, his fund Renaissance returned 66% annually (before fees) for 30 years until 2019. This makes him probably the most successful trader of all time.

Renaissance Capital returned 66% annually before fees for 30 years until 2019 and 39% including fees

The Man who Solved the Market – Gregory Zuckerman

Watch the Video

YouTube video

The Man Who Solved The Market

Jim Simons - The Man Who Solved The Market

Jim Simons in 2007, Gleuschk, CC BY-SA 3.0, via Wikimedia Commons

Even before he founded his hedge fund, Jim Simons had led a life of staggering over-achievement.

In 1964, he moved from academia into the intelligence world. The NSA noticed his problem-solving brilliance, and he joined the Institute for Defense Analyses. Working as a code-breaker at the Cold War’s height (the Cuban Missile Crisis had just occurred in 1962), Simons was doing important work. At the IDA, Simons saw how models could be used to identify patterns in seemingly random data.

However, his free-thinking attitude clashed with the government’s government attitudes, and he was fired for speaking out against the Vietnam War. Simons was not down for long and soon became one of the youngest chairmen of a Math Department in the US. At Stony Brook University, he honed his legendary recruiting skills to turn an average public university math department into a center stuffed full of world-class talent.

In 1976, the American Mathematical Society’s Prize in Geometry recognised his contribution to mathematics.

At the age of 40, he left Stony Brook to found an investment firm and seek his fortune. He dreamed of a purely computer-driven trading system that removed all emotion from the trading process.

But he didn’t know how to achieve this. In fact, it would take him years of research and recruiting some of the most brilliant people in the world to achieve his goals. By the time he finally achieved his goal, he had changed the world of finance forever.

By the time he did finally achieve his goal, he had changed the world of finance forever.

Here are some of my key lessons from The Man Who Solved The Market by Gregory Zuckerman:

“We never believed our models reflected reality – just some aspects of reality.”

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In 1998, a series of events brought down the biggest and most famous quantitative hedge fund of its time. This fund was Long Term Capital Management (LTCM).

The 1997 Asian Financial Crisis, followed by the Russian Debt Default, caused shockwaves throughout the financial world. LTCM was overextended, and the New York Federal Reserve was so worried they organized a bailout.

At the time, many commentators were quick to predict the end of quantitative trading. In this case, the people who profited at the expense of LTCM had been the funds using traditional economic analysis, trend following, and intuition.

This seemed an obvious conclusion at the time, but as we know now, Renaissance was only just getting started on a multi-decade run of massive returns.

So why didn’t Renaissance get caught out like LTCM?

It might seem strange that the creator of some of the most powerful and profitable financial models of all time should admit that they do not reflect reality. However, former Renaissance employee Nick Patterson’s view was that, unlike LTCM, they were always aware that models were not entirely accurate.

This is one of the most contentious issues for all backtesters. To what degree does the model predict future behaviour?

Many people argue that all models are curve-fitted and have no predictive power. Conversely, many people are convinced that their models are fully predictive.

As this quote shows, the fact that models are not reality does not mean they are helpful. As you will see in the next section, a partially accurate model might be all you need to make incredible returns.

“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”

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By 2010 Renaissance was reaching new levels of complexity. At this time, they would have thousands of long and short positions at any time. Buying and selling throughout every day.

Bob Mercer, co-CEO of Renaissance, said they only needed to be right 50.75 per cent of the time to make billions.

This is a lesson not just for mechanical traders but for every trader. Trading success is an accumulation of wins and losses. Very rarely will one big trade make all your worries go away.

I think the reason that many traders drop out is the realization that it is hard to keep swinging again and again. Taking trades when you are winning and when you are losing is hard for most people.

Mercer knew that the edge was still tiny despite the 60 PhDs in AI, statistics, quantum physics and linguistics. But the cumulative effect of using these edges thousands of times every day.

Renaissance was unrelentingly committed to execution. They took pride in hiding their trading activities from their competitor. Disguising what they were up to.

Retail traders do not have to worry about competitors working out our positions and picking us off. We don’t have to worry about our trading size. But we can be just as focused on keeping costs down and getting great execution.

“The firm’s success is a useful reminder of the predictability of human behavior. Renaissance studies the past because it is reasonably confident investors will make similar decisions in the future.”

Modelling human behavior icon

Past performance does not guarantee future results is a standard disclaimer on financial results.

Jesse Livermore famously said: “There is nothing new on Wall Street.” And he was right because human behavior does not change. We are still subject to the same biases and blind spots that afflicted traders of the 1920s and traders of the 1820s.

“There is nothing new on Wall Street. There can’t be because speculation is as old as the hills. Whatever happens in the stock market to-day has happened before and will happen again.”


Simons and Renaissance made the most money when the markets were panicking. They knew that this is when the humans who make up the financial markets are at their most irrational.

“Simons’ phone call is a stark reminder of how difficult it can be to turn decision-making over to computers.”

Bear market icon

In January 2018, equity markets were serene and rising smoothly upwards. They were so calm that many investors were short volatility. These investors used ETFs and other instruments to back the VIX Volatility Index to stay low or even get lower. In early February, this trade backfired spectacularly as volatility came roaring back.

Worse was to come at the end of December 2018 when the S&P 500 was down 20%. This was the moment that Simons made a call to his financial advisor. The market had been falling for months. Was it time to go short and protect his capital?

What could make the most successful quantitative hedge fund manager lose faith in the algorithms that had made him rich? The man whose fund had made annualized gains of 39% (including fees) for 30 years. A man who was worth approximately $23 billion by this time.

In fact, there are numerous occasions noted in the book when Simon overruled his systems.

What can we learn from this? For me, the simple truth of don’t be too harsh on yourself. I know how difficult it can be to completely remove emotions from trading. It is often hard to forget about trades that go wrong, the bad decisions and especially those that you don’t take. But if billionaire Jim Simons can make that phone call, then there is hope for the rest of us.

Ultimately, his financial advisor persuaded Simons to wait a few days. The stock market recovered, and he never took the short trade.

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