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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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