Barclays Publishes Algo-Wheel Guidance
Grasp the wheel and take a spin.
Algorithm wheels are gaining significant adoption, with investment firms rapidly implementing new technologies to improve performance and support best execution efforts. In parallel, the Markets in Financial Instruments Directive II’s best execution requirement has prompted investment firms to justify broker selection more transparently. As a result, technology vendors rushed in to sell broker-selection wheels and transaction cost analysis to help address this requirement. As technology products helped to lower the cost of implementing an automated trading wheel, buy-side use of algo wheels skyrocketed and suddenly the term “algo wheel” became ubiquitous.
To help assess best-in-class algo wheel configurations, the Barclays Electronic Equities team has put out a thought paper – an evaluation framework – that outlines steps for consideration, based on its observations, educated opinions, and experience, having assisted clients during the vendor selection and implementation processes.
Daniel Nehren, Head of Statistical Modelling, Equities, spoke with Traders Magazine’s editor John D’Antona Jr. about the firm’s research and process in which the paper was developed. Nehren said that the goal of the paper was to better educate clients on the ways in which automation and broker-ranking can improve workflow and performance, and how incorrect implementation could backfire. Barclays, not having an algo wheel of its own, said that providing a neutral, independent analysis was the best way to serve the marketplace.
“The key takeaway in general is that building an algo wheel is hard,” Nehren began. “The algo wheel is a really generic term for a set of automation tools that go from pure workflow automation to going as far as being a critical component in the optimization of transaction costs, broker selection, and experimentation. It’s really a broad subject and I think there is a lot to think about in the moment you want to implement something like this. And so we tried to set up a conceptual framework around what essentially we would think a best in class wheel.”
Nehren continued by emphasizing that Barclays wants to be their clients’ sounding board, a support system that can help the buy-side make better educated decisions as they leverage a “wheel approach” when it comes to order flow.
“We go from the step of segmenting the flow, how you decide what to automate, and what should be manually driven, down to how do you choose the trading objective (or algo strategy),” Nehren explained.
So, how do you limit and formalize the trading objectives? “The issue with algo wheel is that essentially, every strategy (or trading approach) is like an algo wheel on its own. It’s a complete standalone thing, because regardless of what you measure and how you measure, there has to be some uniformity in the flow that you compare in order to actually have a statistical significance. And, that basically means that, depending on how much flow you have, you have to limit the number of objectives (algo strategies) that you apply.”
The next steps are to decide how many brokers to use. Also, Nehren said that clients have to examine how one chooses brokers. “What are the things that are important to the client?” he said. “Performance is one thing, but sustainable performance is driven by how these algorithms are working. If you want to apply these wheels successfully, you need to allow the broker to experiment and explore and customize the approach to your flow. And that means that clearly you should rely on brokers that have those capabilities: the ability to analyze flow, the flexibilities of building out customization, and in particular brokers that will apply resources to achieve the best outcome.”
To this end, Nehren told Traders Magazine that Barclays itself was redesigning not only its algorithms but also the way it plans to interact with clients. Think next-generation electronic trading platform. The bank plans to look at not only its electronic smart order routing and algo scheduling capabilities, but also its analytics platform.
“I want to drive the new platform to a much deeper level of interaction with a more comprehensive feedback loop,” he said. “I’m looking at how do I quickly deliver content to our clients that is very specific and very bespoke to their problems?”
Exec discusses the fintech behind algos and 'best ex'.
IBM dips its toe into algorithmic trading.
Clients can lower funding requirement via initial margin optimization.
The trio of strategies will soon expand and include a fourth.
The tool helps robo-advisors analyze unfilled and filled order routes.