Deep Nexus has built proprietary machine learning systems for analyzing time-series data and generating predictive analytics. By capitalizing on recent advancements in both hardware and software technology, patterns and anomalies can be detected in global data streams on an unprecedented scale. Coupled with algorithmic trading, Deep Nexus predictive analytics offer a uniquely profitable approach to financial markets.
Kevin has over 20 years of experience in financial markets while holding Series 7, 63, and 66 securities licenses. His trading experience includes stocks, bonds, options, futures, and currencies. In the 1990s, he began researching how to apply machine learning to financial markets. At Deep Nexus, he has been responsible for the development and coding of proprietary deep learning algorithmic trading models. Kevin studied economics and international relations and received his BA from Columbia University.
Deep Nexus is a multi-disciplinary collaboration among physicists, mathematicians, electrical engineers, computer scientists, and experts in algorithmic trading. The team employs the scientific method in the development and use of emerging technologies for the quantitative analysis of time-series data.