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How is the latest technology accelerating the development and implementation of algorithms and automated trading systems? AT asks leading solutions providers to share their views.

Speed to Market

With:

Dave Bloom, vice president, product management, MarketPrizm
Stephen Engdahl, director, product management, Charles River Development
Steffen Gemuenden, co CEO, RTS Realtime Systems Group
Ali Pichvai, managing director, Quod Financial
Philip Slavin, head of European product strategy, Fidessa

What are the main hurdles to increasing the speed of algorithm and automated trading model development?

Bloom: The sheer change rate of virtually every aspect of the algo trading space is the essence of the problem. Main hurdles centre on the need for: flexible, ultra-low latency, multi-source, information-handling technology; ultra-low latency complex event processing that does not suffer higher latency with increased complexity; a workbench-like approach to algorithm development that combines event processing with advanced calculations and statistics; robust ‘live’ simulation environments that facilitate full testing; and a more advanced real-time risk management paradigm with associated tools. In short, the big barrier is that that the speed of trading is now driving the cost of algo trading beyond the infrastructural and technical capabilities of even the largest organisations. What is needed is a new generation of development and information management technology.

Gemuenden: From the first spark of a trading idea you’re in a race against time; design and delivery need to take place extremely quickly after discovery. One of the key issues is the ability deal with massive volumes of data. Your platform must be able to focus only on user-selectable relevant aspects of market data. Data selection before the routine needs to ‘work’ it is a crucial element of being able to see through the forest that data has become: does the application need to see the whole book?; is the traded volume relevant?; is every trade update relevant? etc. Moreover, the ever-increasing speed at which market data is returned to the trader’s application means that not only does the system need to process this, but that the logic of the trading strategy needs to be interpretive as well as responsive. A great example of this is that order delivery is so fast that the system responds to the market data that the system itself has submitted. The system needs to be aware of its own orders to avoid this, but only the best systems today have the required high performance interpretive skills.

Dave Bloom, Cicada

“…the speed of trading is now driving the cost of algo trading beyond the infrastructural and technical capabilities of even the largest organizations.”

Slavin: The main hurdles can be divided into four main areas. First, the number of lines of code required impacts not only on coding time, but also on the length of time required to test and retest. Second, as the number of parameters increase, so do the number of possible routes through the code that have to be tested and retested. Third, the number of simultaneous order slices can impact development time because each slice has to be monitored, and adjusted if required, and may even interact with each other. The final issue is user-interface customisation. By enabling automated deployment user interfaces, the provision of ‘dialogs on demand’ can greatly reduce the time to market. Developing against an API that has been specifically designed to support algorithmic trading clearly reduces these hurdles.

What evidence do you see of client pressure for faster time to market for algorithms / automated trading models?

Bloom: The type and scale of the inquiries we and our partners receive has significantly evolved over the past few years, with the emphasis moving from milliseconds to microseconds for processing and from months to days (and, we soon expect, hours) for development cycles on new algo trading strategies.

Pichvai: Algorithmic trading is still a comparatively immature segment. We consider the market to be in its third generation, with the most prevalent algorithms today being those in the scheduling category (e.g. VWAP). However, market forces are introducing complex requirements for best execution and eliminating centralised exchanges, thus removing the centralised execution and market data streams that VWAP algorithms require. Meanwhile, leading banks and vendors are developing the next generation of algorithms, which take a liquidity-seeking, adaptive approach. As adoption rates accelerate for these new algorithms on both the buy- and sell-sides, innovation will inevitably take hold and reduce time to market.

Slavin: More clients want to implement an algorithmic trading solution, but they’re wary of complex system integration issues. Clients are increasingly asking for customised algorithms. Markets can change rapidly and clients need to meet the requirements of prevailing market conditions with appropriate new models and be in a position to deploy these as quickly as possible. For MiFID, clients need to ensure that any new algorithms they use continue to meet their best execution policy requirements. Another key consideration is user confidence – both in terms of ownership of the order and in terms of confidence in the model itself. ...

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