How did RenTech do it? – Book Notes of “The Man Who Solved the Market”

Jim Simons’ shop Renaissance Technology, or as insiders call it “RenTech”, is the hallmark of quant fund managers. I’ve been longing to learn about how they achieved it and can’t wait to read this Greg Zukerman’s new book on Jim Simons.


As usual, I tried to put some lessons/thoughts useful for myself, hopefully for my readers as well.

[Bonus] I also dug up some RenTech job descriptions that no one ever (at least I didn’t see) found regarding what type of talents RenTech has been hiring (thus what type of new techniques they may be using) more recently. Go straight to the last point if that is your only interest.

  • First thing first, this book will fall short if you are after RenTech’s strategy (it only talks vaguely and generally about strategies & data sets). Well, it’s totally unsurprising retrospectively, because if otherwise, Simons would do whatever it takes to get these things out of this book given his protectiveness of his secret sauce. Been said, it’s still quite insightful to learn that RenTech started as a managed futures/CTA shop & had its challenge when trying to move to equity side.

This book is more about the history & people of RenTech. E.g. half of the book is dedicated to various lead PMs over the history of RenTech (e.g. Lenny Baum, Jim Ax, Elwyn Berlekamp, Henry Laufer, Peter Brown & Robert Mercer). By the way, most of them are renown mathematicians, who have their own Wikipedia pages on their academic achievements.

  • Jim Simons seemed to never be hands on building the strategies himself (maybe except the early years). He is more of a team organizer, cheerleader & sales person for the better part of the three decades, which I suppose is a natural extension of his experiences as a university Math department chair. I guess these skills are hard to learn and even may be born with.

One pattern consistently showed up on how he successfully attracted such a swarm of talents is he always asks for small incremental favor. E.g. For getting Lenny Baum to quit IDA job for his one man trading shop, Simons “asked if he could spend a day at his office helping set up trading system”; To attract Elwyn Berlekamp, Simons “invited him to fly to Huntington beach a couple times a month to learn to trade for himself and see if his expertise in statistical information theory might be useful to Axcom [a Predecessor of RenTech].”

  • Now to the key question – How did RenTech achieve it? Based on my understanding, two main advantages:
    • Better bullets – Early adoption of frontier techniques/datasets: e.g. they started to record (by themselves) and use intra-day tick data in 80s, then started to adopt some early forms of machine learning in 90s, then started using natural language processing (after hiring Peter Brown & Robert Mercer from IBM) in the 2000s. Data and techniques are the building blocks of trading models, and all of them are well ahead of the market by a decade or so. Also, being early has a cascading advantage, that is once you identify some strong signals and start trading it “to capacity” (p.274. which means you, as the arbitrager, move the market so much that others won’t be able see this inefficiency anymore). In this way, you as the first comer, can continue exploiting the full inefficiency without worrying about it to be competed away.
    • Better guns – Modeling the reality better by incorporating all real world constraints, like trading cost, borrowing cost, etc. Simons once said (p. 271.)

      I’m not sure we’re the best at all aspects of trading, but we’re the best at estimating the cost of a trade.

      This allows them to be able to focus on only real money-making strategies and also implement them much better. Don’t quote me on this, but from my past experience, many institutional quant managers nowadays are still using very rudimentary trading cost models in backtesting & portfolio optimization (e.g. a simple % term, or linear [see this relative new CFA FAJ article on transaction cost of Factor Investing [Link], which used a simple linear model] & square root function at most).

  • So we know what frontier data/techniques RenTech had been using, that’s not helpful if you are managers trying find new edge. I wonder what type of new techniques/data sets are they currently using. Obviously that won’t be an easy task given how secretive they have been. But I know there is one place where they have to disclose at least something –Department of Labor PERM filings for sponsoring green cards [link]. For someone who’s not familiar with US employment based green card process, the PERM is an employer’s proof of no US citizen could be found to fill the position of a current foreign-born employee who is apply the green card. Thus, in these hypothetical hiring process, the employer would be very specific in job description (sometimes almost detailed description of that specific employee, so that only that employee would fit). It turned out there are indeed some detailed secret sauces. Here are a few interesting position descriptions. E.g. the 2018 case candidate is using fluid dynamics in modeling. One 2011 case candidate already started using Tensor technique. But let’s be fair, I don’t even understand many of these disciplines, so no way we can compete away RenTech’s edge.
  • Case 1: 2018 – Mathematical Researcher:
    • JD: “Develop and enhance proprietary statistical models relating to risk management, cost, trade generation and price prediction in order to improve alpha generating potential of quantitative investment strategies. Develop complex mathematical models of small signals embedded in a large amount of noise as well as test these models against large data sets. Analyze models for their predictability of future price movements in the financial markets utilizing C++ and Perl programming in a Linux computing environment.“ “Related experience must include implementing large-scale numerical computations in fluid dynamics and the solution of partial differential equations utilizing C++. Will accept any suitable combination of education, training or experience.”
    • Citizenship: GERMANY
    • 2013 UNIVERSITY OF CAMBRIDGE PhD in Physics


  • Case 2: 2017 – Mathematical Researcher:
    • JD: “Maintain and enhance equities trading strategies utilizing mathematical and statistical algorithms for generating price move predictions, risk and cost estimates. Analyze, design and implement mathematical and statistical algorithms to predict future stocks price movements utilizing advanced linear algebra, calculus of variation, and complex mathematical analysis. Analyze historical financial data, searching for new predictive patterns of equities price movements.“;
    • Citizenship: AUSTRALIA



  • Case 4: 2011 – Operations Research Analyst :
    • JD: “Formulate and apply mathematical modeling and other optimizing methods to develop and interpret data to assist management with decision making and policy formulation. Conduct mathematical analysis of complex algorithms, real-time portfolio valuation and risk models in order to monitor internal behavior of risk profiles of portfolios containing a variety of financial instruments. Conduct mathematical and statistical analysis of modeling techniques to detect anomalies and deconstruct models to validate computations. Utilize Eigen-vectors, Eigen-values, Advanced Tensor techniques, C++ and Bloomberg.“
    • Citizenship: CHINA
    • 2006 MIT PhD in PHYSICS


  • Case 5: 2010 – Mathematician:
    • JD: “Utilize mathematical modeling, mathematical transformations and advanced algorithms to improve computer-driven trading systems programs and support accounting systems, including equities, futures and options, research, accounting and various reporting programs and utilities utilizing C++. Develop quantitative models to detect data errors and ensure quality of mathematical data utilized by trading system for both research and production.“
    • Citizenship: CHINA


  • Case 6: 2010 – Senior Computer Scientist:
    • JD: “Conduct fundamental algorithm-based research in order to design, develop and enhance a highly complex and sophisticated code base utilized for long-term market simulations and proprietary trading algorithms. Refine theoretical models, simulations and trading algorithms in order to determine new patterns in futures, options and equity markets. Incorporate software solutions into larger modeling and trading methodology. Utilize Stochastic Modeling, Combinatory Logic, Distributed Computing, Algorithmic Theory and C++.“
    • Citizenship: NETHERLANDS


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