Like many Americans, last night I dutifully switched on my TV at 9pm to see the State of our Union. Always a spectacle, America’s leadership have upped the surreality ante with the bizarre backdrop of Biden lip-synching amiably in the background whilst Madame Speaker sat with all the calm collection of a fish on a hook and never seemed fully in control of herself or her eyebrows. The spectacle of would’ve-been king McCain sitting there and glowering openly at the lecturn as his confederates sat in stony silence while their ‘opposition’ applauded like drunken high schoolers at a home coming at every mundane utterance proved a bit much and I had turned off the glowing beacon of groupthink by 9:25 and gone to investigate something on my computer. I was surprised and delighted to see that it was still available: dingbatkabuki.com
Dingbat Kabuki and other structural market hacks
When I first started puppetmaster trading, one of my dearest friends, a Yale-educated economist and professor of same, asked me an important question. He asked:
In the markets, there are always ‘insiders’ who have the ability to trade on knowledge that you can’t know or with an advantage that you can’t have. How are you going to compete with these players?
I provided a variety of answers, but at the time my conception of the universe of people with both inside knowledge and the ability to trade on it was limited to cases like that of Mr Rajaratnam. I believed that cases like these were constrained by clear laws that were duly surveilled and prosecuted by the appropriate authorities. The problem seemed like a very real one, but constrained in size and not essential to my enterprise. I still hope that my belief of the time was true, but since then I’ve certainly understood that there’s more than one way to hack the market.
For some, a market hack might consist of some kind of simple (or complex) algorithm(s) applied to some set of markets. But this really isn’t a hack so much as it’s a trading strategy – like many that have long existed – only that it’s now implemented in software where originally it would have been implemented in wetware. While implementing trading strategies in software does open up new vistas in terms of the kinds of strategies that you can look to implement – computers are faster than people by a noteworthy amount in many tasks – but, for the most part, you’re really still just trading and when you take on positions, you are still bearing risk. You might be ‘hacking’ but it’s really not a market hack as I’ve come to appreciate.
One of the nicest things about the holiday season (Happy New Year, btw) is that it provides a lovely opportunity to spend some quality time with a project that’s a bit more exploratory than might be meaningfully undertaken while trading in lively markets.
A number of months ago, I mentioned using HDF5 to manage tick data as RDBMSes just aren’t up to the task and specialized Tick DBs are absurdly expensive. While I’d spent some time exploring this idea through the fall, I never had a discrete chunk of time to really explore the technology beyond determing that its Java interfaces weren’t production-worthy. This meant that we’d have to drop into C to access the functionality we’re interested in and that we’d have to come up with our own bridge out into Java for access by StratBox while StratCloud could access it directly.
Below, I describe what I’ve learned through my holiday geek-spelunking-trek including some timings on various configurable characteristics of HDF5 (e.g., compression and “chunking”).
While Carl Sagan’s famous formulation introduced a generation to the vastness of the cosmos, more recent history suggests that his memorable term might now be more aptly applied to financial extents: our deficits and debts, perhaps, to the economically or politically minded. But for those of us with the markets on our mind, the term has to evoke the enormity of the data we create and must manage every day. We’ve recently been working with the NYSE’s TAQ data in an effort to integrate it into StratBox‘s back-testing and optimization capabilities. And the enormity of the data is really just staggering.
Each day, the NYSE publishes all of the day’s quotes and trades as well as some reference data. Compressed, the data will just about fit onto a DVD. For one day. A DVD. Compressed. It’s really mind-boggling. A year of the stuff, uncompressed, will require over a petabyte of storage. Over 1,125,899,906,842,624 bytes. And that’s just the US Equities markets. You want options data, too? I hope your uncle is named EMC, because just managing the data is going to be a challenge…
I’ve been trading an increasing amount at the open and close of the equity markets using market-on-open (MOO) and market-on-close (MOC) order types and have found that the quality of executions varies enormously between the two types and have spent a bit of time analyzing the differences which I share below.
The quick scoop is that MOC orders almost invariably fill at the exchange’s published closing price, while MOOs vary very substantially from the published open price. Below I quantify my findings in a bit greater depth.