This post was originally published on Pure Storage
Over the past decade, quantitative trading (quant trading) strategies have been subject to increasing scrutiny regarding their ability to seek, discover, and retain alpha, the measure of an investment’s performance relative to a benchmark. As financial markets become increasingly complex and global, the competition to generate alpha becomes more intense. This makes access to diverse, accurate and timely data crucial.
Quant Trading Data Demands Are Evolving
In the past, quantitative trading researchers and data scientists focused on the size of their data sets and the speed at which they could access them. However, as the market environment becomes more fast-paced, the key differentiators have evolved to:
Efficiency of models to adapt quickly to changing market conditions An infrastructure ecosystem that empowers the agility required for success
The more data a firm can access and process, the better positioned it is to make strategically timed, profitable trades. Analysts must ensure the data they use is accurate, comprehensive, and up-to-date while building, testing, and deploying advanced algorithms that can process and assess massive data sets quickly and efficiently.
Mitigate Risk by Diversifying Data Sources
Two critical factors that can make or break a strategy are the concentration and diversification of
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