Solutions

We have architected and delivered solutions ranging from advanced portfolio management tools to novel systematic investment management approaches.  We have experience with contemporary technologies such as Cassandra and Spark, as well as containerized deployments.

Advisory

We perform research using our toolkits and partner with domain experts to provide targeted research, proofs of concept, and advisory around asset allocation, risk management, and macroeconomic scenario analyses.  We bring a multifaceted and creative approach to exploring various research topics, ranging from factor modeling to stochastic pension ALM.

Design

The output of our analytical tools must be presented in easy-to-intuit, beautiful dashboards.  Investment committees, CIOs, portfolio managers, risk managers must all want to connect to the platform and understand the issue through varying lenses.

Background

The advent of Machine Learning as a realistic set of approaches to unlock new insights to the large amount of data that the world as accumulated brings new opportunities for computational finance.  New data are being sourced every day across the world, not only in the markets, but also from other sources such as the Web and the Internet of Things.  The rise of High Performance Computing (HPC) is transforming our brute-force computational capabilities.  Lower-cost and scalable architectures are enabling us to engage with massive amounts of data.  New statistical methods are helping us to extract information and patterns from previously neglected data sets.  It is our hope that these new elements that have already found wide application in everything from retail marketing to genomic research will help revitalize the field of quantitative financial research, something that has until recently relied on techniques and approaches first developed in the 1950's.  

APPROACH

1. APPLY INSIGHTS FROM NEW DATA SETS AND MACHINE LEARNING

The regime changes we have recently been experiencing and the difficulty in forecasting the world have led to many challenges for asset managers, allocators, and banks.  However, the high availability of new data sources is fundamentally the way insights can be gleaned on markets and the macroeconomy.  Coupled with new techniques in Machine Learning and Artificial Intelligence, we believe that models and solutions can be creatively assembled to answer to some of the most pressing challenges in modern finance related to allocation, risk, and signals extraction.

2. LEVERAGE HIGH PERFORMANCE COMPUTING INFRASTRUCTURE

Mainstream IT infrastructure built for quantitative finance often do not have the requisite performance to drive the most interesting analysts.  We believe the the use of a modern computing stack, made scalable through parallelization and accelerated by GPUs can push computational finance to the next level of sophistication.  Solutions that were only approximated before can now be rendered with higher granularity and precision.   

There is a skills gap between the mainstream IT systems professionals in Finance and the specialists focused around modern computing infrastructure implementation.  New technologies like distributed databases, GPU acceleration, scalable containerized applications on the cloud are all new areas where we seek to bring premium expertise and knowledge.

3. DESIGN HUMAN-MINDED INTERFACES

We believe that one of the failings of many financial software is their inadequacy in human-minded design.  The interface is important to not only provide a user experience comparable to that of the consumer web applications, but also to accurately represent data, communicate insights, and allow decision making.  

All the most powerful analytics in the world with a myriad metrics are useless unless we have the desire to comprehend them.  We believe delivering the analytics via creating dashboards that are not only aesthetic, but that also provide precision and clarity in data visualizations.