Around the 2000s, financial markets such as stocks, futures and options markets, as well as energy markets, began to shift from being physical markets to being almost fully electronic. This shift transformed trading, as it also became possible for computers (algorithms) to trade. Algorithms now account for the majority of trading activity. They act automatically and much faster: sometimes their actions are not even perceivable by the human eye. This also creates new challenges for regulators as data size has increased tremendously and new types of market manipulation have emerged. Where do you start looking when there is so much data?

This is where Project HighLO can help.

Project HighLO uses particle physics methods which has decades of experience in storing, processing and handling big, high-speed data. There are similarities between the billions of particle collisions in CERN's particle accelerator (Large Hadron Collider; LHC) and the high-speed trading in which multiple orders per nanosecond are placed. Most collisions in the LHC show no anomalies, but when they do, this may lead to new ground-breaking insights for physicists. Project HighLO uses similar algorithms and tools CERN uses, to identify anomalies on market data. When there are anomalies in financial and energy markets, market participants, exchanges and authorities want to know what happened, how the market responded and how they should respond.

Applying particle physics tools to high-frequency data opens up a whole new world of analysis and research. It makes us able to see behavior and patterns that we did not see before.