Today we return to our series on regime switching and the topic of managing portfolios of strategies. In particular, we build on the examples illustrated in sensitivity testing and steppin’ out, in which we showed historical and then real-time ‘forward-walking’ of strategies. The next step we’d described was to evolve the techniques illustrated to support the real-time management of a portfolio of strategies.
In the example below, we look at another ‘meta’ strategy named StrategyPortfolio which maintains a dynamic portfolio – P – of strategies which it will select from a set of strategies – S – running concurrently in simulation. The constituents of P as well as their cash allocations and parameterizations will be rebalanced/adjusted regularly after an initial ‘out-of-sample’ period during which only the S strategies are run.
Apart education, the intention of this strategy, as I’d originally suggested here, is to ‘back-into’ a regime-switching strategy without attempting to directly quantify the regimes explicitly.
This has proved to be even more interesting than I’d expected, not so much because it performs particularly well (though it’s promising), but because of all of the things it has taught us. In particular, the transitions are a killer and there are properties of strategies which (dis-)qualify them from being effective in such a scheme…
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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…
One of the challenges of algorithmic trading is that although there’s plenty of interest in the space, practitioners aren’t generally forthcoming about their observations. Academics, instead, focus on things that are frequently not very immediately practicable, or when they might be, always seem to set-up a little hedge-fund on the side while publishing colorful chum about how markets are ‘behavioural’ or somesuch.
Even if it’s hard to find good stuff, one must still look as there’s always more information that can help you than you can effectively process or retain. A few weeks ago I was trying to formalize the expected profit function of an algorithm I’m developing and wanted to see what people had written about the topic. I entered ‘define profit function for trading algo’ into google and was pleasantly surprised to see a paper entitled ‘Multi-strategy trading utilizing market regimes’ by Mlnarik, Ramamoorthy and Savani. It doesn’t directly cover the topic I was looking for, but instead addresses a number of related topics I’ve been interested in for some time:
- the treatment of a strategy as an instrument in its own right
- composing portfolios comprised of strategies
- using regime switching techniques to manage portfolios of strategies
In this post, I’ll briefly review their paper, illustrate how one can easily model strategies in relevant ways using the strategy ‘object model’ I’ve described previously through an example, and conclude with some thoughts on how these kinds of strategies might be implemented and further explored.
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