Hack the market

While the war over the latest+greatest video cards for the current generation of graphics intensive games seems always to ebb and flow between nVidia and its arch-rival ATI, I’ve long preferred nVidia for their better support of Linux.  Thus, all of my machines have some sort of nVidia Graphics Processing Unit (GPU) in them.

For those who spend their workdays in the markets and their weekends pondering derivatives pricing, latency, oceans of market data, portfolio optimization, and how to make every last damn thing faster, a preference for nVidia cards could prove to yield an unexpected benefit.

nVidia has recently unveiled a product line dubbed “TESLA” which leverages their absurdly fast GPUs to provide a supercomputer-like High Performance Computing (HPC) platform at a previously unimaginable price point.  TESLA computers are regular machines that have a set of slightly modified GPUs in them; modified such that they have no video out, but instead become additive processing clusters which the machine can use for compute intensive tasks.  For about $10K you can buy a 1U machine with some 4 teraflops of capacity.  By way of comparison, this is over 20 times faster than the funky Helmer project I’d been drooling over a few months ago in a production-worthy package ready for the server room today.

So, TESLA refers to the machines built with these specialized GPUs.  Making all this power usable is what CUDA is about…

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monte-carlo methods, options pricing, portfolio management, technology

One of these days I’m going to give an overview of all the excellent open-source software I use on a daily basis. Until that day comes, I’ll observe that finance remains one of the big areas where open-source software has made relatively limited inroads.

Two production-quality packages fight that unhappy state: QuantLib - a comprehensive framework for quantitative finance - and QuickFix - a full-featured FIX engine. Both are C++ libraries and both provide very nice interfaces to facilitate integration with other languages, including Java. QuantLib is a big and complicated library and integrating it with Java is not totally obvious. Below, I’ll describe how to build and use QuantLib from Java.

These instructions are based on a unix installation. I’m not really a windows developer and don’t have all the shiny tools that windows developers use, so it’s not an area of focus for me. That said, I have managed to build QuantLib under windows by using MinGW+MSYS but it wasn’t terribly easy and I don’t currently have a working installation, so I won’t cover that here. If this is your aim, don’t be dismayed as it is possible and it had all the functionality I enjoy under linux.

Using QuantLib from Java (on linux)

• Build QuantLib
  • Requires a working version of Boost. This may prove to be the hardest step of all and you’ll need to use the ample documentation provided by the Boost team.
  • Once you have a working copy of Boost, building QuantLib should require little more than

  sh autogen.sh
./configure

  make
sudo make install

• Build QuantLib-SWIG
  • Requires a working copy of SWIG. Again, look to the SWIG instructions, but it should be easy.
  • Once SWIG is available, building the QuantLib/SWIG interfaces should only require:

  sh autogen.sh
./configure \

  –with-jdk-include=${JAVA_HOME}/include \
  –with-jdk-system-include=${JAVA_HOME}/include/linux
  make -C Java
sudo make install

• Now you’ll have a Jar file with all of the SWIG/JNI stubs in it available in /usr/local/lib/QuantLib.jar. Add this to your classpath.
• Programs which call QuantLib functionality will need to have the LD_LIBRARY_PATH set. This can be done by invoking the vm with something like:

-Djava.library.path=/usr/local/lib

• Programs which call QuantLib functionality will also need to explicitly load the QuantLib libraries. This can be done with something like the following static block appearing before your main method:

static { // Load QuantLib
try { System.loadLibrary("QuantLibJNI"); }
catch (RuntimeException e) { e.printStackTrace(); }
}

• That’s it. Now test your configuration by running the examples in Quantlib-SWIG/Java/examples.

It’s worth understanding how Quantlib is being used from java. SWIG is creating a JNI interface into those methods within Quantlib which have been exposed through their declaration in the swig *.i files. These files are found in Quantlib-SWIG/SWIG and they determine what functionality from Quantlib will be available to you. You’ll likely need to get familiar with a subset of those files that you care about. If you find that some functionality you care about isn’t exposed in those files, you may need to expose it yourself.

There’s a learning curve, but it’s worth traversing so you can get at all the rich functionality so many smart people have put together.

FIX Protocol, monte-carlo methods, open-source software, options pricing, technology

Professor DeLong points out that Emanuel Derman has begun posting lecture notes to classes he’s teaching as part of the Columbia Master’s of Financial Engineering. If you’re even remotely interested in financial engineering or algorithmic trading, then you should read Dr. Derman’s engrossing book “My Life as a Quant” as it gives a unique and personal perspective on the explosion of engineering as a discipline within finance. I haven’t studied his notes carefully yet but a cursory examination suggests they look very worthwhile.

I recently found in my inbox an invitation to study for a Certificate in Quantitative Finance which is, I’m sure, a great program. But it’s pretty pricey and any quant should be aware of costs! Laughing in the dark might be a reasonable alternative to shelling out for a more structured offering…

books, dereferenced, options pricing

There are many conceptions of our world and its underlying nature, each seemingly more creative than the last. One of my favorites - adopted by all serious fans of Star Trek as well as some physicists who’ve run rather far afoul of friar Occam’s good rule - posits the existence of a multiverse. There are various such stories, but the basic idea is that at every quanta in time the universe partitions itself off into an infinitude of parallel instances of itself, each representing a different branch of realized possibilities. In this one, I decided to become a surfer while in that one I went into finance and in that one I led the US to its first-ever world cup victory. While we only experience the particular universe we happen to be situated in, the multiverse represents the simultaneous execution of all possible universes. Under this view, we really do live in the best of all possible worlds.

This idea has surprising utility. While its cosmological implications might represent the extreme psychedelic fringe of the continuum, at the other ends lies actuarial science, the foundations of risk management and, yes, a variety of option pricing techniques.

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EMS Internals, back-testing, options pricing

The best way to reason about a trading strategy’s performance, that is valuing it, is as an option.

Or perhaps as a collection or portfolio of them.

I have to assume that people reading this have a working idea of what an option is, so I’m not going to provide definitions that can be readily found elsewhere. I will note that my favorite book on the trading of options is by Allen Jan Baird.

Let’s consider the three illustrative trading strategies we’ve looked at up until now. The trend-following strategy suffered many little losses and then enjoyed a big win. Sounds like buying options. The mean-reverting strategy made lots of little profits and then risked getting clobbered with a big loss. Sounds like someone who’s writing options. And the first strategy we looked at, the morning range breakout, had a payoff which looked like a long straddle or strangle where the break-evens were near the observed high and lows for the session (where we set our entry stops).

Now, there’s obvious differences between the trading strategies’ payoff structures as compared to the similar options strategies. There’s no premium, for instance, and that’s clearly significant. The morning range breakout seems to exhibit a sort of knockout effect when a position has been entered but then the market reverses and you’re “knocked-out” of your position. You just take a loss and do not collect even if the market turns back in your direction. With a straddle you don’t have this behavior. There are differences and they are worth keeping in mind. But the reasons for viewing trading strategies as options portfolios are many and compelling.

The superficial reason, as I mentioned, is that the basic payout structures are potentially similar. The deep reason is that ultimately the problems are the same - how to value complex instruments with engineered payouts. And the pragmatic reason is that many many very smart people have applied their considerable brains and diverse skill-sets to advancing options pricing techniques. There’s also a great deal of high quality software available out there which can be used to adapt these time-proven techniques to your own algorithmic trading strategy valuations.

The techniques which we’ve seen up until now, back-testing and parameter optimization, are sort of weak cousins of a family of techniques long used for options pricing: Monte-Carlo (MC) methods. MC simulation can clearly be used to assess a trading strategy’s performance.

In subsequent posts, we’ll talk about some of the details of each of these techniques and about some of their respective trade-offs. That should keep my pump primed for a bit, but in the meanwhile I leave you with a parting inquiry: what other options pricing techniques might we apply to our algorithmic trading practices?

back-testing, books, monte-carlo methods, open-source software, options pricing, performance analysis, strategy development, technology