Book Review: Christopher Steiner’s “Automate This”

As part of my independent research on using ML-generated embeddings to map patterns in market behavior, I have been reading various books which can provide insight into the field of quantitative finance. These books cover topics ranging from statistical modelling, mathematics, programming, or even general macroeconomics. So far, I’ve read Boomerang by Michael Lewis and The Black Swan by Nassim Taleb.
Last week, I finished reading Christopher Steiner’s bestseller Automate This. I originally planned on reading Marcos Lopez de Prado’s Advances in Financial Machine Learning, but with the influx of work during finals week, I wasn’t in an ideal place to study a dense mathematics textbook. So, I’ve postponed this as my next endeavor, and I will write a corresponding report later on.
In his book, Steiner covers various modern implementations of machine learning algorithms in different fields, spanning from traditional quantitative finance to music recognition to matching personality types for dating. Steiner seems to firmly believe in a future dominated by algorithms and machine learning, as he concludes with this powerful outro: “There’s going to be a lot of work in the future for those who can write code. If you can also conceive and compose intricate algorithms, all the better — you may just be able to take over the world. That is, if a bot doesn’t do it first” (220).

Wall Street’s Obsession With Speed

The book begins with the story of Thomas Peterffy, the Hungarian-born billionaire who famously took over Wall Street in the 1980s. This excerpt in and of itself is a fascinating tale, as Steiner claims Peterffy was the first person to ‘hack’ financial markets. It was Peterffy’s work which started the race to automatize trading, and is a primary reason why nowadays, more than 60% of all trading is done through digital algorithms. Peterrfy began by writing simple computer code which received real-time market data as an input, and through a series of calculations, decided whether or not an option was over/under valuated. From this information, Peterffy could purchase options before other traders (who were all working manually at the time) discovered this inefficiency. Those working without computers were always too late, and could never beat Peterffy to profit. Peterffy continued refining his algorithm, to the point where he integrated the newly-discovered Black-Scholes model for pricing options into his algorithm.
This was all happening 30+ years ago, but the basic principles behind Peterffy’s strategy remain relevant. Traders realized that speed was the new gateway to profit, and no one can work faster than computers. This incited a ‘digital revolution’ on Wall Street, where all the best trading firms began hiring engineers, mathematicians, and computer scientists in the hopes that they could create new, faster algorithms which outpaced the competition. Steiner elaborated on this concept by introducing the story of Daniel Spivey, the man who decided to build a brand-new dark fiber cable line to connect the Chicago Mercantile Exchange with Wall Street. Spivey correctly believed that having faster exchange of information between these two exchanges would allow for more time to spot inefficiencies in prices, and more time to profit from arbitrage. This reminds me of Tom Connerly, who I interviewed earlier this year, and his spiel on how HFT companies are now investing in shortwave trading lines in hopes that these can transfer information faster than fiber cables. I’ve seen this in the works of Alexandre Laumonier as well, on his blog Sniperinmahwah.
Image result for shortwave radio station
Steiner then goes on to explain the origins of algorithms, which he traces back to Carl Friedrich Gauss and Gottfried Leibniz. I found it interesting that “The mathematician [Leibniz] stipulated that cognitive thought and logic could be reduced to a series of binary expressions. The more complicated the thought, the more so-called simple concepts are necessary to describe it” (58). This is nothing new, as binary code in computer hardware has existed for more than half a century, but I think the concept of taking complicated tasks and partitioning them into hundreds of simple tasks is very powerful. This goes far beyond programming and investing, as this rule can be applied for tackling issues in one’s life or visualizing daunting problems, one step at a time.
Steiner also interweaves a handful of additional narratives about algorithms and their applications, but it would be more interesting to experience these on your own rather than have me repackage them poorly. One which stood out to me was the story of David Cope, who wrote a series of algorithms which could compose original classical music indistinguishable to the works of Bach and Beethoven.

The real difference between East and West Coast

Toward the end of his book, Steiner details the shifting dynamic between East and West coast United States. As is still the case, investors on Wall Street develop their quantitative strategies by recruiting the best engineering minds directly out of college, mainly students with mathematics, physics, and computer science degrees. What I didn’t know is that because of the vast surplus of wealth in financial industries, especially from 1995-2008, Wall Street firms would happily offer up to $200K starting salaries just to claim the most promising college students. This meant that from 2000 to 2008, firms in other industries, such as biotech, medical, and technological research institutions, had a shortage of new engineering employees. After the crash of 2008, when Wall Street lost the prestige and esteem it had reserved for the past decade and half, these quants went directly to Silicon Valley to work at startups. The rise of tech startups like Facebook, Amazon, LinkedIn is correlated to this influx of engineers moving from New York City and Connecticut to San Francisco and Palo Alto.
This was fascinating to me, as this is yet another pattern embedded in human behavior which can be used to make predictions. Right now, for instance, Wall Street is back on the rise, with record returns and a continued push for improved trading algorithms. At the same time, tech firms like Facebook, Google, and Tesla have all been receiving negative coverage in the media for a multitude of reasons. Specifically, I think of Facebook’s recent scandals in breaching user privacy, where some users have theorized that Facebook is secretly tapping into phone microphones to pick up on keywords which can be used to generate more specific ads. This is clearly a time when Silicon Valley’s most talented engineers might decide to make the move to the East coast.
With this knowledge, soon after the the next financial crash, it would be advisable to start investing in companies tech and biomedical companies, as they pick up all the employees who will inevitably ditch Wall Street and Wacker Drive.

Anecdotal Evidence for NLP in Quant Finance

Of all the anecdotes and stories in Steiner’s book which stood out to me, there’s one that I will surely remember for many years to come.
On page 179, Steiner references the work of Robert Mercer and Peter Brown, two computer scientists who worked as researchers at IBM in the 1990s. In an effort to create software which could accurately translate text from English to French without being hampered by the many obscure rules and counter-intuitive idioms in each language, “The men created machine-learning algorithms to look for patterns in twin-texts. Where others had tried to solve the problem with elegant code that attempted to reproduce the grammatical structures of different languages, Brown and Mercer employed ‘dumb’ software and brute force.

“Brown and Mercer then built a set of algorithms that tried to anticipate which words would come next based on what preceded it.”

Brown and Mercer’s breakthrough didn’t go unnoticed on Wall Street. They left IBM in 1993 for Renaissance Technologies, the hedge fund. Their work developing language algorithms could also be used to predict short-term trends in the financial markets, and versions of their algorithms became the core of Renaissance’s best funds. During a run powered by Brown and Mercer’s work, Renaissance went from $200 million in assets in 1993 to $4 billion in assets in 2001.
Renaissance Technologies is widely heralded as the best hedge fund in the world, in terms of performance, and I’ve been fascinated by this company for a while. Shrouded in secrecy (most hedge funds are, but this one is really secretive, as you can infer from their website), the only thing that’s well-known about their investing strategy is their adherence to mathematical and statistical models. This is the first time I’ve read that RenTec saw the potential of NLP in finance over 25 years ago.
Now, I’m not saying that just because I’m working on NLP embeddings in finance I will create the next Medallion Fund of the world. I’m referencing this because it’s nice to see affirmation for the work I am doing. It’s especially nice when this affirmation comes from the most successful hedge fund in the country (you could argue that colleges are more successful as hedge funds than anyone on Wall Street will ever be, but save this argument for another day). I truly believe the power of context-learning A.I, whether it be similar to NLP models or not, has untapped potential in finding patterns in all of the world’s many markets.

The True Future for Trading Algorithms?

This idea of defeating competition through pure speed remains true today. Yet, it appears as if we are reaching a ceiling. As Steiner himself affirms, Wall Street has been saturated with the best engineering minds on the planet who have cycled through thousands of mathematical and statistical strategies for the past 30 (going on 40) straight years. This means that today, there is little room for profiting off trading algorithms which rely solely on rigorous math-based models. This means two things:

  1. We need new, creative approaches which find patterns within the markets that cannot be reached through traditional mathematical/statistical modeling. This is part of why I am so curious in using NLP embeddings for mapping patterns in the price movements of stocks/ETFs. I believe that, much like how Word2Vec has surprised us in unearthing new patterns within familiar text, these embeddings can discover new similarities between sequences of price-shifts in the markets.
  2. The next step in speed and efficiency needs to be a leap, not a step. This is why I, as well as many others, believe that quantum computing will be at the core of trading algorithms within the next 10-20 years. This is a relatively new revelation for me, and I definitely won’t be using quantum computers to run my embedding-based trading algorithm, but I will definitely continue researching this topic.


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