I have been live-trading, with my Fidelity IRA account, using the signals generated by the models as introduced in Forecasting and Timing Markets: A Quantitative Approach (ASIN:B0875JBWBQ ). Started from March 09 this year, I have achieved a net profit of 24.54% as of April 24, which is impressive given the market volatility induced by the COVID-19. Coincidentally, I learned from an online post that Simons’s Medallion Fund also achieved an over 24% return during this same period of time. I was motivated to find out more about his Medallion Fund and thus bought this book.
I eagerly read through the entire book so that I could assess how different his quantitative approach is against the AlphaCovaria System I have been relying on as mentioned above. I am so grateful for Mr. Zuckerman who dug out so many details about how Simons’s models have been built. Here is a summary of what I have learned from a quantitative trader’s perspective:
(1) First, a little background. While at IDA during his earlier career, Simons and his colleagues wrote a research paper that determined that markets existed in various hidden states that could be identified with mathematical models. At IDA, they built computer models to spot "signals" hidden in the noise of the communications of the United States' enemies. This was the precursor to Simons’s later persistent pursuit to testing the approach in real life.
(2) Performance-wise, Simons has been the most successful one in trading, given the performance comparisons of this list: Jim Simons (Medallion) 39.1%, George Soros (Quantum Fund) 32%, Steven Cohen (SAC) 30%, Peter Lynch (Magellan Fund)29%, Warren Buffett (Berkshire Hathaway) 20.5%, and Ray Dalio (Pure Alpha) 12%. One of the factors that Simons could succeed so much is that he is a strongly principled person with a strong belief in "Work with the smartest people you can, hopefully, smarter than you... be persistent, don't give up easily." So he is not only a great mathematician but also a great visionary and business manager.
(3) Their model dev process: By 1997, Medallion's staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals: (1) Identify anomalous patterns in historic pricing data, (2) make sure the anomalies were statistically significant, consistent over time, and nonrandom , and (3) see if the identified pricing behavior could be explained in a reasonable way.
(4) Trading frequency: Medallion made between 150,000 and 300,000 trades a day, but much of that activity entailed buying or selling in small chunks to avoid impacting the market prices.
(5) Data granularity: They use five-minute bars as the ideal way to carve things up. Their data hunter Laufer's five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects.
(6) Holding period: Medallion still held thousands of long and short positions at any time. Its holding period ranged from one or two days to one or two weeks. The fund did even faster trades, described by some as high-frequency, but many of those were for hedging purposes or to gradually build its positions. Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques.
(7) Their performance as measured by Sharpe ratio. 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion's trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year. No one had achieved what Simons and his team had-a portfolio as big as $5 billion delivering this kind of astonishing performance. In 2004, Medallion's Sharpe ratio even hit 7.5, a jaw-dropping figure. Medallion had recorded a Sharpe ratio of 2.5 in its most recent five-year period, suggesting that the fund's gains came with low volatility and risk.
(8) Their portfolio composition. They started with commodity, bond, and currency, but later expanded into equities, which became the major source of profits after many years of efforts.
(9) Does Simons strictly stick to their models? In general, yes, but he made calls when he saw models were malfunctioning due to extreme market conditions.
(10) How have their models worked under various market conditions? Their models are mostly neutral, which was made possible by making quick trades only to eliminate unforeseeable events. They claimed that they could make models that would work with long-term investments, but it seems that they have not done so.
(11) What is the most secret juice with their models? Medallion found itself making its largest profits during times of extreme turbulence in financial markets. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, booms, and busts. "We make money from reactions people have to price moves." They look for smaller, short-term opportunities-get in and get out. 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. "We are right 50.75 percent of the time... but we're 100 percent right 50.75 percent of the time," Mercer told a friend. "You can make billions that way."
(12) How long was their learning curve? Simons spent 12 full years searching for a successful investing formula, without much success until he and Berlekamp built a computer model capable of digesting torrents of data and selecting ideal trades, a scientific and systematic approach partly aimed at removing emotion from the investment process.
(13) Size of their computing infrastructure. On page 248, it says their computer room was the size of a couple of tennis courts. I arrived at a guestimate that they might have about ~13,000 servers, computed like this: 2x78x27 (two tennis courts) x 0.6 (total area occupied by racks) / (2x4 (rack area)) x 40 (servers per rack) = 12,636. This should not be too far away from what they have.
I strongly encourage every serious quant to read through the entire book for a lot of other secret juices.
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Reviews with images
Inspiring book for those writing algorithms to trade the markets
I have to admit my review is probably a little bias. I have been a huge fan of Simons ever since I started programming algorithms to trade the currency markets in 2008. What I appreciated most about this book is the fact that Simons and his team went through the same trials and tribulations that I have encountered. Sometimes its a rogue piece of code that destroys your entire model. Other times you may have just over curve fit a strategy. Whatever the case may be it feels good to know one of the world's best investors faced the same issues. It would be interesting to know if he hadn't capped his fund's investment whether or not he would have been able to sustain his 66.1% returns since inception.This book does an excellent job telling the story of not only Jim Simons but the historical context of other hedge funds during his reign. If anything while you're reading this book it will help generate and spark some of your next trading hypothesis. Jim Simons applied AI to the markets before AI was a common household term. His use of monolithic models and bayes' theorom to develop trading systems is no easy feat. This book does however make me wonder what Jim Simons would have accomplished for the world had he not set his focus on financial markets.I would recommend this book to anyone who quantitatively trades the markets.
Top reviews from other countries
Rav London (UK)
Great account of a very secretive and successful business
Reviewed in the United Kingdom on March 6, 2024Tomás Antunes
Homem impressionante com uma vida equiparável aos rendimentos e inteligência do mesmo
Reviewed in Spain on December 15, 2022Michael L.
Excellent writing - path to "success" is mired with traps
Reviewed in Canada on October 29, 2020