We leverage the architecture of artificial neural networks in the design and execution of investment programs.
Confidence trough computation
Our primary mission is to advance the current mathematical and statistical methods used in quantitative portfolio management by introducing spatial dimensions to traditional time-series in order to produce improved risk-adjusted returns in investment environments not commonly accessible.