The Akida NSoC has the ability to be trained in an unsupervised manner, a very important attribute when attempting to analyze large sets of unlabeled data such as financial information. Users can select what financial parameters are most important to analyze and then feed that data to the Akida NSoC, which accelerates an spiking neural network crafted to perform a clustering algorithm. These clusters may then be analyzed to determine if these repeating patterns signify successful trading patterns or are predictive of market shifts.