It is generally accepted that these quantitative techniques have been successful in attaining their goals, and that banks adopting quantitative methods have made hundreds of millions of dollars through better trades and through selling higher-margin products to their corporate customers. Ultimately, analysts build models in order to predict how a security, a complex trade, or an entire market will perform in the future. A key input in this analysis is the historic price of an asset, and it is not uncommon to utilise 20 or more years worth of pricing data with a model. However, managing time series data has posed a unique problem to financial technologists. Relational models are inefficient containers for time series, and provide neither high performance nor a useful programming interface.
Data sets containing daily data are typically several gigabytes in size.
In a typical implementation, the data sets are stored on a server and delivered to a client, where it is analysed. The task of actually integrating various types of data has been left to individual customers and consulting firms. The newest trend among quantitative analysts is the desire to analyse transactional, or tick data, rather than daily data. For example, an analyst studying equities now wants a data set containing the price and volume for each and every trade for a particular stock over a number of years. A server-centric model is therefore required.