containing a strategy
My son recently had his first birthday and amazes me daily with his new feats as he runs around increasingly stably exploring the world around him. It occurs to me that the system I use to trade every day, Stratbox, is approaching its fourth “birthday” in the next few months. I hadn’t originally intended to write a system – an algorithmic trading platform – but found that existing products were limited, expensive and didn’t fit my mental model of what they should do.
This isn’t surprising as I wanted the system to support all of the activities associated with our algorithmic trading. It turns out that that’s a lot to ask of a system. It also turns out that you learn as you go and so the system continues to evolve. A few years ago I’d posted about the basics of a strategy container and in this post I’m going to come back to this topic and describe some of the layers of code and thought developed since then.
First, let’s consider the role of a strategy container. Its job is to intermediate between trading strategies and the external environments with which they interact. It must also provide services that strategies can use (e.g., position management) and that it wouldn’t make sense for each strategy to re-implement. In the past I’ve focused on the former responsibility of adapting strategies to external environments. Why is this necessary and interesting? Because it allows us to take the same exact strategy and run it live, or in simulation or in backtest, etc. Interesting and necessary, but not what I want to focus on this time. Instead, I want to look at the services provided to strategies; the ‘ecosystem’ a strategy container provides in the hope that strategies might flourish within it.
We’ll go from the bottom up. At the bottom you have one or more pipes of market data coming in. You might also have news feeds, weather feeds or other such things coming in and we’re just going to conflate them all and say “market data.” Still at the bottom, you also have one or more two-way pipes with your oms or broker or directly with exchanges or with an exchange simulator. Within Stratbox we call this abstraction an “ExecutionPlatform” because it’s conceptually where trades get executed. The lingua franca for execution platforms is FIX, so the baseline object model of a strategy container is likely going to look a lot like messages described by the FIX spec. Here we have orders, executions and the like. Within Stratbox we’ve implemented an exchange simulator, a QuickFIX-based FIX interface and a couple broker-specific APIs and they are all of type ExecutionPlatform. To a strategy, they all look the same. Likewise with market data. Within the strategy container, we provide a publish-subscribe model wherein any kind of market data can be subscribed to regardless of the ultimate source of the data. The intention is always to intermediate between the strategy and its external environment.
the baseline object model
Great. But now I want to write a trading strategy. What’s that going to look like? And this is where things get increasingly interesting as we get to decide what kind of facilities we’re going to provide. We also need to manage concurrency in some fashion as trading is an intrinsically asynchronous activity. For this lowest level of strategy, we’ll say that each market feed is handled by a thread which timestamps and enqueues a “Quote” for later consumption and redistribution to relevant subscribers by a MarketDataService within the container. Likewise, each execution platform will be serviced by a thread who similarly enqueus “Execution” objects from exchanges real and simulated. The threads handling executions should likely have a higher priority than the threads handling market data. So, the strategy is naturally exposed to the markets’ asynchronicity right off the bat. What baseline facilities will our strategy have at its disposal? Well, it needs to be able to manage orders and positions, so it needs some kind of a “blotter” facility. With this in place, a strategy can safely assume that it doesn’t need to listen to each execution coming in just to have a correct picture of its book. It needs access to a MarketDataSvc and it likely also needs access to historical data. But, staying simple, that’s more or less all we have to provide and we’re going to provide all of these things to the strategy through a single handle: a strategy context. By swapping out a strategy’s context, we can move the strategy among environments (eg, from simulation to live execution).
With these facilities in place, basic algorithms can be implemented within a strategy and you should be able to test and trade them.
But you’re dealing with very low-level stuff. You’ll also find that you’re writing the same kind of code over and over. This inspired the introduction of a layer on top of the baseline object model which I’ve described before: StratParts – a strategy component model.
StratParts – a component model for strategies
With StratParts, we introduced metadata and composition (in the “composite pattern” sense) to our Strategy object model. A strategy is itself a stratpart which contains other stratparts. Thus, stratparts introduce a hierarchical structure to a strategy. Each stratpart publishes a metadata descriptor which is aggregated at the strategy level. The descriptor contains all of the modifiable characteristics of the strategy and can be changed while a strategy is executing thus giving a sort of grey box capability wherein a trader (or another strategy etc) can modify the behavior of a strategy as it’s running. Stratparts also create an effective means of providing scoping within your strategy environment. Stratparts can “see” the activity of other stratparts within the same strategy – they’re all peers in this sense. This scoping can also be used for allocation of resources across different elements of a strategy. For example, one stratpart might be allocated 80% of the cash available to the strategy while another stratpart manages the remaining 20%.
Stratparts have proved to be very powerful and we’ve written many of them though we’ll sometimes write strategies as one monolithic stratpart where it makes sense.
Stratparts have a lot of uses but one thing they don’t do is help manage the low-level complexity inherent in trading activities. This was best described by a trader with whom I’d collaborated. He’s a reservist in the IDF and uses military metaphors like I use sports metaphors. He bemoaned the low-level handling of orders required for a very close-to-the-market strategy we were working on and said that:
we need an order like a ‘smart’ missile: fire it and forget it
He was right and that inspired the next level of abstraction/support that we built into the system.
‘smart’ orders and the tradeflow stratpart
The strategy we were developing was meant to look at a universe of futures spreads and generate all of the ‘cycles’ that might result in an arbitrage opportunity. Since these are rare at best, we were really looking for circumstances where it looked like we had an advantage based on depth-of-market and various heuristics we’d apply. Among the functionality that he wanted was an ‘order’ which would act as a limit unless some conditions obtained in which case it might pay the spread or otherwise stop being a fixed limit order. He didn’t want to handle this inside the strategy but instead wanted to fire these things off and forget about them unless they required attention (eg, if they’re rejected by the exchange or his firm’s risk management checks). Although we ended up calling these smart orders, they’re really a sort of very localized execution strategy themselves.
To support them, we utilized the same workflow (= state machine) framework we’d implemented for our ExchangeSimulator as we wanted to ensure that these smart orders had a very well-defined life-cycle with a clear set of states and guarantees about transitions among them. We embedded this workflow engine into a stratpart which supported smart orders operating within this “tradeflow”. Thus, the extra functionality (and weight/expense) of this functionality is only achieved/incurred when actually required or desired. This has proven to be a powerful addition to the system and raises the level of services enjoyed by prospective strategies substantially, particularly given the variety of orders we’ve implemented which stand ready for use by any new strategy that might want to employ them.
At this point, it should be clear that we’re blurring the lines between the strategies themselves and the container. Another way of thinking of it is that one container can have a set of pre-made building blocks that can be applied for families of strategies. Each one provides a particular grammar with which a strategy’s aims can be expressed. For very close-to-the-market strategies where very low-level handling of orders is required, the tradeflow stratpart provides a set of really useful facilities, but if I’m just trading with limits then it might not be so interesting.
portfolio-oriented strategies and beyond
I’ve mentioned before that I only care to think of strategies that operate on a portfolio. As such, we’ve written a good deal of facilities for creating, analyzing and manipulating portfolios within strategies. By using these facilities within a tradeflow stratpart, I can express quite complex strategies relatively simply by using portfolio analytics to determine my current ‘model’ portfolio and then using smart orders to most effectively transition me from my current state to my model state. For the kind of trading I do, this provides a rich ‘vocabulary’ for the development of strategies. All the same, my brain is having a tip-of-the-tongue moment as I feel that there’s an entirely different model possible for portfolio-oriented strategies just at the periphery of my imagination… oh well, there’s always something new to explore.
Different trading styles, perspectives or trading problems are undoubtedly better served by different sorts of facilities. I’m finding that just as language constrains and shapes thought, the strategy container that you employ shapes and constrains the kinds of strategies that you can implement.
If you have any ideas about such facilities for any kind of trading you do, I’d love to hear them.
a note about Stratbox
Although I’m talking about our system in this blog, we’re not marketing the system and happily “just” use it for our own trading activities. We had looked into marketing the system previously but ultimately feel the same about selling stratbox as selling strategies…