In some ways, automated trading systems already make use of Big Data. You could even argue that they were among the first systems that found a practical use for Big Data. The difference these days is that there is increasingly more data available, and there’s an opportunity for algorithmic trading systems to expand their scope and make use of it as it becomes increasingly available.
Of course, many companies will not want to part with the data they collect themselves because they will regard it as proprietary, or containing trade secrets, or what-have-you, but all companies these days have a web presence and all companies exist in the brick and mortar world. Big Data can be collected from these spheres as well, and there is much that could be learned about a company from purely public sources, with no cooperation needed from the company itself.
The biggest concern with using and processing Big Data is, of course, latency, but here, automated trading systems actually have an advantage. They have been fighting the battle with latency for many years now. Their experience in dealing with and minimizing it means that they can, on the whole make better use of large data sets than companies without that body of experience.
Long Term Impacts If Quality Data With Automated Trading Systems
Big Data is just now coming into its own. Three years ago, nobody was thinking much about it, or had even heard the term. These days, you can’t go a full week without tripping over an article giving it a mention. The best approach would be to begin now incorporating whatever large data sets can be found into your process, and take them and whatever they reveal into account when back-testing you automated trading systems. It may only make a difference at the margins this year or next, but they day will come when information gleaned form very large data sets could dominate the landscape. Such information could, in fact, make or break a buy/sell order.