Recently, I had a sudden idea to create a new series introducing the best traders in the U.S. stock market. The reason for this is that the cryptocurrency market is currently linked to the U.S. stock market, so studying the excellent traders in the U.S. stock market and their investment strategies can effectively guide our investments in the cryptocurrency market, as the current cryptocurrency market is similar to the U.S. stock market 5-10 years ago (the volatility in the cryptocurrency market is currently larger, and its market value is lower).
So I asked chatgpt to help me search for the best traders in the U.S. stock market over the past 10 years (after 2015), including both institutional and individual investors, and their trading strategies. (Chatgpt recently pushed a deep research feature that will conduct extensive searches and compilations.)
1. Jim Simons (Institutional Background, Quantitative Trading Strategy) - Founder of Renaissance Technologies, renowned as the 'King of Quant.' The Medallion Fund led by Simons has astonishing long-term returns: a net increase of +76% in 2020, with a historical average annual net return of nearly 40%. The company uses vast amounts of data and mathematical models to predict the market, with team members mostly experts in mathematics, physics, and other fields. Simons advocates for the 'scientist-led investment' philosophy, and his story is featured in bestselling books such as 'Decoding Genius Traders.'
Unfortunately, Jim Simons passed away on May 10, 2024, at the age of 86. At that time, his net worth was estimated at $31.4 billion, ranking him 51st among the world's wealthiest individuals.
Simons is a mathematician, skilled in geometry and topology. He earned a Ph.D. in mathematics from the University of California, Berkeley at the age of 23, then taught at MIT and Harvard University, and joined the Institute for Defense Analyses (IDA) to research wartime code breaking; due to his public opposition to the Vietnam War, Simons was dismissed from IDA and subsequently joined Stony Brook University in New York, serving as the chair of the mathematics department from 1968 to 1978.
In 1974, Simons and Shiing-Shen Chow jointly proposed the Chern-Simons form theorem, a theoretical framework that combines geometry, topology, and quantum field theory, having a profound impact on theoretical physics, particularly quantum field theory and string theory. Simons received the highest award in the American geometry community—the American Mathematical Society's Veblen Prize in Geometry—in 1976.
At the peak of academic honors, Simons chose to abandon his research career and apply his mathematical genius to a seemingly mundane research area: how to make more money in the shortest possible time.
In 1978, Jim Simons founded a small investment company. Unlike other financial institutions that employed Wall Street elites as their core personnel, Simons took a different approach by gathering a group of scientists: mathematicians, physicists, and statisticians. By constructing complex mathematical models, they attempted to use computers to interpret data and predict market trends, initially focusing on currency and commodity price fluctuations.
Four years later, the company was renamed 'Renaissance Technologies' and expanded its investment focus to the stock market. Simons' team began to collect a vast array of global information resources—from political news in Africa, bank data from small Asian countries, to price fluctuations of potatoes in Peru, all of which were included in the calculation models. The core idea of this approach is to find the hidden 'non-random behaviors' in the market, which are price patterns that can be captured by mathematical models.
Ultimately, Simons and his team of 'scientist traders' demonstrated that many areas of the financial market—including stocks, bonds, foreign exchange, and commodities—actually exhibit predictable patterns that can be modeled, much like differential equations in mathematics. His quantitative trading ideas have fundamentally changed the operational methods of the hedge fund industry.
Over the next thirty years, Renaissance Technologies’ core product, the 'Medallion Fund,' recorded astonishing performance: an average annual total return rate of up to 66.1%, with a net return exceeding 39% after deducting high fees. This achievement far surpassed legends of the investment world like Warren Buffett and George Soros, making Simons one of the most successful fund managers in modern financial history.
2. Peter Muller (Institutional Background, Quantitative Trading Strategy) - Founder of PDT Partners. In the first 11 months of 2015, the PDT fund had a net return of +21.5%. Muller focuses on algorithmic trading and strict risk control: he aims to 'extract funds from the market with as little risk as possible,' achieving excess returns multiple times. PDT is highly sought after by institutional investors due to its outstanding performance and innovative strategies.
Peter Muller graduated from Princeton University with a degree in mathematics (1985). In 1992, Muller joined Morgan Stanley and formed the 'Process Driven Trading (PDT)' team.
In 2013, Muller officially spun off the PDT team from Morgan Stanley to establish the independent hedge fund company PDT Partners.
According to reports from Bloomberg and Institutional Investor in 2023, PDT has recently managed assets exceeding $10 billion, maintaining exceptionally high alpha capability.
Peter Muller and the PDT trading system emphasize the following points:
Characteristics:
Quantitative driven: All trades are based on statistical and mathematical models, eliminating emotional influences.
Multi-strategy portfolio: including statistical arbitrage, machine learning, short-term forecasting, market neutral, event-driven, etc.
High Frequency + Mid Frequency: A strategy that combines high frequency (seconds to minutes) and mid frequency (days to weeks).
Team collaboration: Recruited a large number of doctoral researchers with backgrounds in mathematics, computer science, and physics, different from traditional financial talents.
Very strong risk control: Extremely strict control over downside risk and capital volatility is key to its stable returns.
Let's look at two individuals today, both institutional types and both using quantitative strategies, but they are team-oriented, using mathematicians and multidisciplinary talents to build their strategy algorithms. Such strategies are certainly not publicly disclosed, making it difficult for ordinary people to replicate. However, it can be observed that those that are mature and can achieve stable profits are certainly quantitative, as the detachment from emotional manipulation is a more scientific approach!
