At Causality Group we are passionate about implementing the new technologies and quantitative methods that enable us to better understand the drivers of asset returns. Automation of investment processes simplifies the task faced by quantitative portfolio managers, increases transparency, and enables us to use a wide range of techniques borrowed from machine learning, optimal control, and statistics.
While our primary aim is to benefit our employees, we also care about our surroundings. By continuously working towards more efficient
mathematical modeling techniques, investing in next-generation hardware, and advancing open-source software, we also help information technology users and support our counterparties
who trade with us in global markets. At the same time, we allocate capital to those companies we believe have the talent and commitment to build great businesses.
We have found that some of the most powerful data analysis, deep-learning, and big-data ecosystems are implemented under public and open-source licenses. We also use closed-source applications when they perform exceptionally well; however, we believe that customizable open-source solutions are impossible to beat.
Open-source developers and data analysts tend to follow the most efficient path, without worrying about politics involved when choosing their tools. This results in strong support for productivity-oriented solutions like Python, pandas or R (which we use intensely for data discovery). Unique results call for unique tools. It is often quicker to get from zero to one using commercial applications, but automating the workflow with specialist analysis tools is easier to achieve with the source code.
We combine trading strategies or factors so that the aggregated performance is stronger than any of the individual strategies. A significant part of our job is to discover the next strategy which will further strengthen our live portfolio.
The fundamental law of active management states that increasing the number of strategies has decreasing marginal utility with regard to the performance of the resulting portfolio. The ideal way therefore to increase our trading profits is to improve every detail in our trading process: starting from our philosophy on factor modeling, through unbiased inversion of covariance matrices, to optimal control of trading strategies in the order book.