Wow... what a book! Jim Simons... an idol for every trader. So far, I think this is the only book in the market about the mighty Quant. I read a Chinese version book about Jim Simons before. However, I remain suspicious on the contents of the book. Hence, when this book was introduced by
Michael Covel, I have no hesitation to buy it.
Finally, we have a book that tells the whole story about Jim Simons. Well, I believe this book presented the real Jim Simons compares to those rumors and articles being circulated on the internet. The author did well in presented the whole chronology on how Jim Simons and co started till the latest. Along the way, there are newcomers as well as those who gave up. Hence, we can see how this quant revolution being made. Amazingly, it was an exciting but real tough journey.
You do not have to equipped yourself with great mathematical skill to read this book. The author did so well by presenting simple ideas that every reader shall be able to understand. After all, even if those coding was revealed, we might not understand at all. Basically, finance experts may not understand coding. Same thing applies to coder who may never understand the games of finance. So, again, what Jim Simons achieved, by combining both is really a remarkable stuff.
I believe some readers may be disappointed with this book. After all, this book does not reveal the real secret behind the success of Renaissance. However, like I mentioned above, unless you are a coder, otherwise, there is no point to show you the code. To me, this book is good enough. In fact, I rate it as excellent, 10/10! First of all, it is so hard to get the information on the mighty Jim Simons. Secondly, I am eager to know the history on how Renaissance being established. So, this book revealed what I want. This book is definately well worth reading. It is well written and surely a must read for those who works in financial market like me.
Last but not least... as listed below, I pick out certain points from the book which I found interesting. Thumbs up to the author in writing this book.
Simon quoted: "God gave me a tail to keep off the flies. But I rather have no tail and no flies. That's kind of the way I feel about publicity."
"The lesson was: Do what you like in life, not what you feel you 'should' do," Simon says. "It's something I never forgot."
Lenny Baum developed a saying that became the group's credo: "Bad ideas is good, good idea is terrific, no ideas is terrible."
Markov chains, which are sequences of events in which the probability of what happens next depends only on the current state, not past events.
In a Markov chain, it is impossible to predict future steps with certainty, yet one can observe the chain to make educated guesses about possible outcomes. Baseball can be seen as a Markov game.
A hidden Markov process is one in which the chain of events is governed by unknown, underlying parameters or variables.
"I don't want to have to worry about the market every minute. I want models that will make money while I sleep," Simons said. "A pure system without human interfering."
Stochastic equations, the broader family of equations to which Markov chains belong. Stochastic equations model dynamic processes that evolve over time and can involve a high level of uncertainty.
I strongly believe, for all babies and a significant number of grownups, curiosity is a bigger motivation than money. ~~~ Elwyn Berlekamp
Berlekamp argued that buying and selling infrequently magnifies the consequences of each move. Mess up a couple times, and your portfolio could be doomed. Make a lot of trades, and each individual move is less important, reducing portfolio's overall risk.
Berlekamp and others developed a thesis that locals, or floor traders who buy or sell, like to go home at the end of trading week holding few or no future contracts. Similarly, brokers on the floors of commodity exchanges seemed to trim future positions ahead of the economic reports. Medallion's system would buy when these brokers sold, and sell the investments back to them as they became more comfortable with the risk.
Certain trading bands from Friday morning's action had the uncanny ability to predict bands later that same afternoon, nearer to the close of trading. If market moved higher late in the day, it often paid to buy futures contracts just before the close of trading and dump them at the market's opening the next day.
"I don't know why planets orbit the sun," Simons told a colleague suggesting one needn't spend too much time figuring out why the market's patterns existed. "That doesn't mean I can't predict them."
Simons, too, became nervous when his fund went through rocky times. On the whole, though, Simons maintained faith in his trading models, recalling how difficult it had been for him to invest using his instincts. He made commitment to refrain from overriding the model, hoping to ensure that neither Medallion's returns, nor the emotions of his employees at Renaissance, influenced the fund's moves.
It continued to identify enough winning trades to make serious money, usually by wagering on reversions after stocks got out of whack. Over the years, Renaissance would add twists to this bedrock strategy, but, those would just be second order complements to the firm's core reversion to the mean predictive signals.
Never place too much trust in trading models. Yes, the firm's system seemed to work, but all formulas are fallible. This conclusion reinforced the fund's approach to managing risk. If a strategy wasn't working, or when market volatility surged, Renaissance's system tended to automatically reduce positions and risk.
Simons often emphasized the importance of not overriding their trading system. But, in market crisis, he tended to pull back on the reliance on certain signals, to the chagrin of researchers who didn't believe in ever adjusting their computer programs.
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, rather than profiting by stepping in front of other investors. What Simons and his team were doing wasn't quite investing, but they also weren't flash boys.
Medallion still held thousands of lonf and short positions at any time, and 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. "I am not sure we're the best at all aspects of trading, but we're the best at estimating the cost of a trade, " Simons said.
Medallion still did bond, commodity and currencies trades, and it made money from trending and reversion-predicting signals. The gain 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.
How the firm wagered was at least as important as what it wagered on. If they found a profitable signal, the wouldn't buy when the clock struck nine, potentially signalling to others that a move happened each day at that time. Instead, it spread its buying out throughout the hour in unpredictable ways, to preserve its trading signals.
Instead of the hit-and-miss strategy of trying to find signals using creativity and thought, now you can just throw a class of formulas at machine learning engine and test our millions of different possibilities.
