"The Man Who Solved The Market"

I have been live-trading, with my Fidelity IRA account, using the signals generated by the models as introduced in my latest book Forecasting and Timing Markets: A Quantitative Approach, available on Amazon. Started from March 09 this year, I have achieved a net profit of 32.47% as of May 08, 2020, which is impressive given the market volatility induced by the COVID-19. My performance of 32.47% gain actually beats all market indices of Dow Jones (-15.27%), S&P 500 (-9.78%), Nasdaq (0.89%), as well as last year's top 5 mutual funds of FSPHX (0.38%), FOCPX (2.66%), WMICX (9.97%), VINIX (-9.79%), and FSCSX (5.20%), respectively.

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 about what Simons and his team have done, from a spare-time, quantitative trading researcher’s perspective.

(1) Background Information

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

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) Their 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

In 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. Simons's Medallion fund trades about eight thousand stocks.

(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." “Some of the trading signals they identified weren’t especially novel or sophisticated. But many traders had ignored them. Either the phenomena took place barely more than 50 percent of the time, or they didn’t seem to yield enough in profit to offset the trading costs. Investors moved on, searching for juicier opportunities, like fishermen ignoring the guppies in their nets, hoping for bigger catch. By trading frequently, the Medallion team figured it would be worthwhile to hold on to all the guppies they were collecting.

(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.

(14) Recommendation

I strongly encourage every serious quant to read through the entire book for a lot of other secret juices.