We’ve been looking at what we’ve been calling “meta-strategies” – strategies that act upon other strategies – with the goal of implementing something like we’d described in the recent regime-switching post. (Please note that since then I’ve added a category to capture this thread.)
Last time we saw an example of historical forward-walking of a portfolio-oriented day-trading strategy which utilized daily data. This time we do something a bit more interesting and correspondingly complex. Today we’ll look at a real-time forward-walk of a moderate-frequency strategy (trades perhaps a few hundred times in a day) which looks at the top-of-the-book but doesn’t use market-depth. The strategy is a simple mean-reverter that we’ve described before though we’ve had to make some small changes to get it to behave in the context we’re looking at now…
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'optimization' or 'search'?
We’ve been looking at how a strategy container might view and implement a variety of modes for strategies it will launch and contain. Last time I documented a uniform initialization process for many of them, including a posited walk-forward parameter optimization mode. I’ve implemented an initial version of this that I’ll illustrate through a screencast (first ever – be gentle) below, but before continuing want to raise a couple of cautionary notes about the slope we’re traversing here.
From the very first post on this blog I’ve tried to underline the danger that over ‘optimization’ poses in view of the simple unalterable fact that if you look at enough random junk, you are bound to see things that look impossibly good. Doesn’t mean they’re actually good. In the context of trading strategy development, this is a particular danger as strategy parameter optimizers are easy to come by and can be very misleading if employed naively. I think this is in part due to the term ‘optimization’ which is really a stretch for what these tools do. They’re better described as search tools as they are really searching through a tuple-space of possible parameter combinations that you’ve specified, and then ranking them by some criteria you specify.
They’re still useful, but less as ‘optimizers’ and more as tools for judging the sensitivity of the strategy to different parameterizations. If the strategy demonstrates good performance and stability over a variety of market conditions and parameterizations, you may just have found yourself a winner…
Anyway, I felt that had to be said…
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poor Jorge wasn't ready...
In this post I’m going to revisit some of the topics discussed in the recent ‘containing a strategy‘ and ‘multi-strategy trading with regimes‘ posts, focusing on the process of assembling a strategy and its context in preparation for its launch into any of a variety of modes.
I recently realized that – from the perspective of a strategy container – the process of walk-forward testing is remarkably similar to the regime-switching model we’d discussed previously. Up until now, I’ve employed walk-forward testing in an ad-hoc manner by taking an existing strategy and then writing a little driver very much like a unit-test scaffolding which would walk the strategy forward, permuting parameters based on previous performance. Not a general solution, but straight-forward as I employ the strategy parameter optimizer from stratbox in this kind of a toolkit use-case.
I sat down to write one of these walk-forward scaffolds yesterday and started to think about how I could generalize the solution and roll it into stratbox’s GUI and it occurred to me that I could likely kill two birds with one stone…
or: a computational complexity model for derivatives fraud
lemon-law arbitrage?
Derivatives pricing has always been a notoriously complex, computationally expensive and potentially breathtakingly remunerative undertaking. This is true enough for relatively vanilla, exchange-traded options, but once one goes off-market and starts applying creative financial engineering, it can get much more complicated. Products like CDOs, CDSs, CDO^2s and their ilk have exploded in recent years creating opaque markets of trillions of notional dollars and accounting complexities we’re still only beginning to understand.
A recent paper, Computational Complexity and Information Asymmetry in Financial Products, by Arora, Barak, Brunnermeier and Ge take things a step or two further as they illustrate using information theory that it may be far worse than imagined as totally undetectable fraud can be engineered into these products. They show that fraud with these products can be undetectable in the sense that the pricing process is a formally intractable problem when the informational asymmetry inherent in the development of these products is taken into consideration. In this context, “informational asymmetry” is a polite way of saying “fraud.”
The authors, from the Department of Computer Science and Center for Computational Intractability at Princeton (man, I want one of their business cards!), demonstrate that if the designer of, say, a CDO wants to cherry-pick amongst bundled assets to maximize their own return, they can do so in a way such that it would be impossible for a buyer of the derivative to know they were being stiffed. The problem can be so hard that if you got the NSA’s mythic clusters humming on a pricing model, they might chug away until the sun falls from the sky before they accurately price it… Co-author Rong Ge provides a FAQ to the paper here and I must hat-tip Andrew Appel for his informative post on the paper.
The “perfect crime” is a puzzle that has occupied the (criminal and otherwise) mind of many a bright and motivated soul from time immemorial. While some may indulge towards the vulgar or base through violence or vice and others might ponder the perfect crime of passion, the cerebral Queen of Crime is surely some form of regulatory arbitrage: committing the crime for which the law has yet to be written or creatively engineering a legal loophole for a crime one has perpetrated or is about to perpetrate. The developers of CDOs are to be lauded as it appears they have materially upped the state-of-the-art of the perfect crime.
hmmm… Is there a Nobel for that?
