Download new 12-page white paper from the London Stock Exchange and A-Team Group

The adoption of algorithmic trading by the mainstream has created a requirement for high-quality historical data for development, testing and maintenance of trading strategies.

Until recently the exclusive remit of Tier 1 investment banks, algorithmic trading is becoming democratized as smaller brokerages and boutiques implement increasingly affordable high-performance trading platforms. This gives them the opportunity to differentiate their offerings to buy-side clients.

Key to success here is the quality of data. Nowhere is the adage ‘bad data in, bad data out’ more true than in the area of algorithmic and quantitative trading, where the use of highly granular tick and order book data is crucial to producing trading strategies that perform.

Furthermore, increased regulatory scrutiny means firms need to recreate market conditions current during their trading activities, so as to demonstrate due process in meeting their best execution obligations. This all points to the need for a considered approach to sourcing and managing historical data in support of high-performance trading activities.

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Whether they accept it or not, sell-side institutions are finding themselves in the unfamiliar role of information technology vendor. The adoption of algorithmic trading models by buy-side firms of all shapes and sizes is shifting trading strategies, and the technology infrastructure to supply and support them, from the realm of nice-to-have appendage to must-have service offering.

With more sell sides than ever offering both standard benchmarks and their own takes on old favourites, competition between algorithmic trading strategies is heating up. And it’s not all about alpha. The buy side doesn’t like surprises. What many fund managers are seeking from their sell-side suppliers is certainty of execution, low market impact and some degree of accuracy on hitting stated targets.

As a result, brokers and their technology suppliers are all working furiously to help differentiate algorithmic offerings, launching custom and so-called ‘adaptive’ algorithms, and taking great lengths to prove that their models perform as stated on the tin.

In essence, the world of algorithmic trading is entering a new phase as models’ acceptance by the wider marketplace is increasing pressure on firms to perform and, to some extent, productize their offerings. Expect more innovation, more customization and more choice.

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