Hack the market

I came across this article on a Go-playing program and thought it was interesting. Particularly this aspect of it (from wikipedia):

One major alternative to using hand-coded knowledge and searches is the use of Monte-Carlo methods. This is done by generating a list of potential moves, and for each move playing out thousands of games at random on the resulting board. The move which leads to the best set of random games for the current player is chosen as the best move. The advantage of this technique is that it requires very little domain knowledge or expert input, the tradeoff being increased memory and processor requirements.

Our algorithmic trading platform, StratBox, features a unique strategy component model that supports the modular development and re-use of “pieces” - we call them StratParts – of a quantitative trading strategy. StratParts expose metadata which can be manipulated by a human or software agent (e.g., a trader, an optimizer, a regime-switching protocol). A StratPart might be an entire strategy, a risk management component, a graphical or reporting component or really whatever a trader might envision. StratParts can be composed within the StratBox GUI to create a strategy which can be tested, analyzed and executed. Naturally, users can create their own StratParts which integrate seamlessly with the environment. Read more…

Although we’ve been in business since 2005, we’re still something of a start-up and certainly an entrepreneurial entity, so when a VC friend of mine suggested checking-out meetup.com‘s technology and business groups I was open to the idea. This past week I attended my second meeting of the “ny tech” group and was as impressed the second time around as the first. It’s quite a production – from the venue, to the organization, to the ideas, people and products being presented – all for $5! (Though they sadly announced a price increase to $10 starting next meeting.) If you have even a small inclination towards entrepreneurial ventures or emerging technologies, it’s well worth a look-see.

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This week we spent a few hours at the tradetech conference to see what people in the industry are up to and see some demos. Their “certified” logo inevitably reminded me of an old colleague who would from time to time mimic an old saturday night live skit. We saw some interesting things and, most importantly, found that our creative niche remains our own.

The booths we checked out included a few of the big sell-side firms shilling their creatively-named execution quality algos and their white-labeled oms/ems offerings. They had, by far, the best shwag (I should thank Merrill for their nice umbrella and commend citi for the very knowledgeable lady at their booth) but their algo offerings weren’t very interesting to me as they aren’t of the alpha-seeking variety.

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Among the more challenging questions we face when describing the Puppetmaster environment are those like “how do you create new proprietary trading strategies within the environment?” It’s a difficult question because of expectations – people want to hear about some super simple scripting language that any non-technical person can immediately learn and be up and algorithmically trading in no time. A few platforms intended for retail users offer such things – one is even appropriately named easy language. When researching approaches for our system, we spent some time learning easy language and found that it in fact did make easy things easy!

The problem was that it also made sophisticated things impossible.

This led us to pursue another, more powerful, approach for which we are currently seeking a patent.

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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?