business.consulting:
eigen brings to bear a unique combination of quantitative finance and high performance computing expertise,enriched by extensive real-world experience in applying a wealth of skills to a diverse set of problems; for example:
algo.containersfor HFT and other systematic trading, we provide both toolsets and systems integration consulting in the use of our advanced machine learning, pattern recognition and statistical methods containers.
a key component of our approach relies on using both observed and implied measures to derive signal generation, trade sizing and entry/exit levels; this is reflected in the design of our APIs and models. in using machine learning models, it is important to us that both our clients and us understand model behavior both intuitively and quantitatively.
decision.supportcombining machine learning with innovative data visualization yields valuable insight in a number of ways. as showcased in our eigenomics pages, statistical methods are a great way to highlight anomalies, recognize features in data and bring attention to important information that is otherwise buried in large data.
risk.analyticsour quantitative finance expertise has been deployed by a number of clients in diverse areas. We have satisfactorily served our clients in areas such as multi-factor models, richness & cheapness analysis using both statistical and principal components approaches, model validation and development of advanced inflation analytics.
high.performance.computingthe eigen team boasts an in-depth knowledge of both parallel computing (Intel Thread Building Blocks, CUDA & Thrust, OpenCL), and with reconfigurable devices (FPGA IP cores). It is our hope to make parallel and reconfigurable computing accessible to as large an audience as possible, and to that end we offer standard and bespoke training programs in the application of these technologies to quantitative finance.