Many machine learning techniques have been applied in finance, including neural nets, genetic algorithms, reinforcement methods and rule induction. We are developing a new approach that is inspired by ideas about how the human immune system functions. Like the immune system, our software can not only discover effective responses to new conditions (in our case, potential trading opportunities), it also adapts to remember past successes in order to be able to re-activate them quickly when conditions change.

In biological systems, recognition happens by molecular binding. In our software, recognition is based on elaborate mathematical expressions that describe features of the behaviour of stocks. The system is designed to be efficient; it can look at many thousands of elaborate expressions per second.