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The next generation of buy-side trading models is driving new requirements for market data. Buy-side firms use market data in a variety of different ways and for different purposes, but how – asks Valerie Bannert-Thurner, Managing Director of Skyler Technology Europe – can they make the most of the market data available to drive their automated trading decisions?

Optimising Market Data for Buy-Side Automated Trading

As automated trading increases in complexity, the need for faster, more comprehensive, and differentiated market data becomes more pressing. By leveraging the growing array of market data sources available today, buy-side firms are faced with a new opportunity to innovate, and ultimately profit.

The traditional way of using market data for automated trading relied mostly on pricing and pre-aggregated information such as Level I tick or quote data. Real-time tick data was used to trigger orders and historical tick data for model generation and backtesting. But with automated, model-driven trading going mainstream, this approach offered little opportunity to generate and leverage a competitive advantage, as everyone had access to the same limited pools of data. It also failed to provide usable information about the overall market structure – something that is becoming increasingly important in today’s fragmented environment. As a result, the automated trading models that leverage this data have tended to be simplistic and homogenous in their approach to using market data, limiting the alpha that they can generate.

However, the market data available today is much more comprehensive and diverse, ranging from full-depth liquidity information, over news information to transaction cost analysis (TCA) reports. This enables the automated trading engines that use it to be much more comprehensive and complex in their approach, and ultimately more profitable. But are they making the most of the data available to them? ...

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