We use ensemble meta-algorithms for primarily reducing bias and variance in supervised learning, and to convert a family of moderately-strong learners to a strong and robust one.
Our choice of method for each incoming dataset is dependent upon the structure of the raw data and proceeds through a system of checks and balances to ensure accuracy. We then employ deep-learning algorithms to layer generative models into feedback loops which are able to learn and adapt to changing conditions.
Our system was developed in collaboration with experience portfolio managers and traders from the worldʼs top firms. We understand risk and we guide our AI to produce the best risk-adjusted outputs.