Posts Tagged book

Calculated Bets by Steven Skiena

I just finished reading the book Calculated Bets by Steven Skiena.  When I saw the book mentioned on Hacker News I immediately downloaded it to my Kindle and started reading.  I built a pokerbot before, so I figured I’d enjoy the book.

I did enjoy the book, but I’m not sure that I’m the right audience.  I’m also not sure that this should be a book.  The writing style makes me feel it should be a collection of blog posts.

Even though the book is short, it contains a substantial number of detours that should have been edited out.  For example, he spent several pages talking about 1) why programmers dislike Microsoft and, 2) what the Y2K crisis was all about.

Furthermore, I found the author’s professorial tone at times endearing and at times annoying.  I appreciated his descriptions of writing a program to predict football games when he was a teenager, and then convincing his local paper to publish his quotes.  I also enjoyed his recounting of his graduate student years at the University of Illinois.  The book also concludes nicely, with Skiena listing several fun projects that the reader might want to dig into.

On the other hand, for most of the book, the author depicts himself as an outdated Computer Science professor who doesn’t know how to code.  For example, he had to recruit grad students to write programs to fetch and parse simple web pages from the jai alai websites.  The description of that project phase included an awkward description of the Perl programming language.  I found it awkward because he didn’t convey his own love for coding Perl.  Instead, he seemed awe struck that his student could hammer out Perl code that parsed a web page.

Also, the book was ultimately too amateurish on the gambling topics.  Most glaringly, he never touched on risk of ruin.  He deposited $250 with the off track bettor and started making daily bets, as dictated by the program, of around $100 per day.  At one point he nearly went bust, but luckily bounced up.  I would have liked to see him talk about expected variance, including some notion of what his limits were for deciding that a losing streak was more than a bad turn of luck.

Finally, in terms of cold cash, the ultimate results aren’t impressive.  After all of the time spent, both by him and his cheap (free) student labor, I think he made a couple thousand bucks in a year, on an investment of $250.  Then he got spooked about legal issues and also wasn’t able to maintain the code after his students left.  So he shut the whole thing down.  In my opinion, if you’re going to go work so hard to create a winning program, you should have the drive to squeeze some serious money out of it.

Having said all that, I enjoyed the book.  I’m happy that Skiena wrote it, and I appreciate his passion for systematically gambling on jai alai.  Hopefully he’ll write up a summary (without detours) of his efforts on a blog and also provide all of his data and code.  Then maybe some other hackers will dive in and push his system further down the track.

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Review of Programming Pearls by Jon Bentley

I just finished reading Programming Pearls by Jon Bentley.  This is a collection of his “Programming Pearls” columns for the Communications of the ACM magazine.  Bentley shares stories about implementing fundamental data structures and algorithms.

My biggest takeaway was Bentley’s authoritative yet humble tone.  This stands in contrast to the high bravado and chest thumping that defines so much technical communication in the current realm of blogs and tweets.  These days it seems that every writer is a gladiator who’s foremost goal is to demonstrate technical superiority.  It’s king of the cage for nerds.

Bentley, a researcher at Xerox PARC, and a CMU professor who advised Charles Leiserson, is certainly a king of the technical cage.  Even so, he makes you feel like he’s a coworker discussing his current project during lunch.  My mind  was generating imaginary suggestions for implementation techniques instead of simply digesting his word as gospel.

The columns typically highlight personalized implementation stories which often involve now antiquated machines such as the PDP-11.  One personal anecdote that I really appreciated was Ed Reingold’s solution to a toy problem of searching for a missing integer.  Bentley mentions that Reingold uses the question on one of his quizzes.  Prof Reingold was my undergraduate advisor, and these are exactly the type of questions that he asked on his quizzes, which I always thoroughly enjoyed.

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Acknowledgements to Bernie Madoff

Last night I started reading Trading and Exchanges: Market Microstructure for Practitioners, by Larry Harris.  I was surprised to see Bernie Madoff mentioned in the book’s acknowledgments.  Dr. Harris thanks both Bernie and Peter for “their early support” that allowed him to “take time off from my teaching to start this project.”

Curious, I searched Google Books for books published before 2007 that mention the great ponz.  The search yields a number of results — pretty amusing.

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Book Review: Nerds on Wall Street by David Leinweber

This weekend I read David Leinweber’s book Nerds on Wall Street: Math, Machines and Wired Markets.  While sitting at Radiance Tea, I read a book review in the Wall Street Journal.  Since I had previously enjoyed David’s chapter in How I Became a Quant, I paid $24 and instantly downloaded it to my Kindle.

David’s has a clear, concise, and humorous writing style.  Also, since I’m also a (significantly more junior) nerd on Wall Street, I’m in his target audience.

My big takeaway is the sense of pragmaticism and passion that oozes out of his stories. It’s clear that technology makes him tick, and that he’s always right on top of the newest techniques.  And he’s been at it for years, so his stories all carry true gravitas.

Data mining while developing quantitative strategies is one principal theme that’s addressed in most of the book’s chapters.  Personally, I wish he had addressed this in greater detail.  It’s easy to dismiss eggregious data mining that links butter production to S&P returns.  It’s also easy to say “withhold data from your training data.” I also felt that when he discussed a specific example of using genetic programming, he essentially admitted that the strategy was datamined, yet restrained by common sense.

I recommend this book.  It’s a quick and enjoyable read about an interesting topic.

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