The first step in deploying a new quantitative investment strategy is data acquisition.
For example, a few years ago, there was a limited amount of data due to the lack of proper methods for storing and processing data. Today, advances in technology and declining storage costs have dramatically increased the amount of data stored by organizations across all industries.
This trend has caused new challenges in the form of storing vast amounts of data, which often can not be effectively analyzed by people-operators. This, in turn, is driving the development of a new field of big data.
To support our team in their search for new sources of alpha testing, we use big data, browsing multiple datasets from different areas. They include data such as traditional historical financial time series data, fundamental company data, macroeconomic sector data, and more complex alternative datasets.
Our quantitative developers are constantly creating new technology tools to support the demands of our data scientists. This contributes to efficient extraction and storage of very large amounts of historical data.