Agile quantitative analytics in Financial Services

[tl;dr technology is empowering quantitative analysts to much more on their own. IT organisations will need to think about how to make these capabilities available to their users, and how to incorporate it into IT strategies around big-data, cloud computing, security and data governance.]

The quest for positive (financial) returns in investments is helping drive considerable innovation in the space of quantitative analytics. This, coupled with the ever-decreasing capital investment required to do number-crunching, has created demand for ‘social’ analytics – where algorithms are shared and discussed amongst practitioners rather than kept sealed behind the closed doors of corporate research & trading departments.

I am not a quant, but have in the past built systems that provided an alternate (to Excel) vehicle  for quantitative research analysts to capture and publish their models. While Excel is hard to beat for experimenting with new ideas, from a quantitative analyst perspective, it suffers from many deficiencies, including:

  • Spreadsheets get complex very quickly and are hard to maintain
  • They are not very efficient for back-end (server-side) use
  • They cannot be efficiently incorporated into scalable automated workflows
  • Models cannot be distributed or shared without losing control of the model
  • Integrating spreadsheets with multiple large data sets can be cumbersome and memory inefficient at best, and impossible at worst (constrained by machine memory limits)

QuantCon was created to provide a forum for quantitative analysts to discuss and share tools and techniques for  quantitative research, with a particular focus on the sharing and distribution of models (either outcomes or logic, or both).

Some key themes from QuantCon which I found interesting were:

  • The emergence of social analytics platforms that can execute strategies on your venue of choice (e.g. quantopian.com)
  • The search for uncorrelated returns & innovation in (algorithmic) investment strategies
  • Back-testing as a means of validating algorithms – and the perils of assuming backtests would execute at the same prices in real-life
  • The rise of freely available interactive model distribution tools such as the Jupyter project (similar to Mathematica Notebooks)
  • The evolution of probabilistic programming and machine learning – in particular the PyMC3 extensions to Python
  • The rise in the number of free and commercial data sources (APIs) of data points (signals) that can be included in models

From an architectural perspective, there are some interesting implications. Specifically:

  • There really is no limit to what a single quant with access to multiple data sources (either internal or external) and access to platform- or infrastructure-as-a-service capabilities can do.
  • Big data technologies make it very easy to ingest, transform and process multiple data sources (static or real-time) at very low cost (but raise governance concerns).
  • It has never been cheaper or easier to efficiently and safely distribute, publish or share analytics models (although the tools for this are still evolving).
  • The line between the ‘IT developer’ and the user has never been more blurred.

Over the coming years, we can expect analytics (and business intelligence in general) capabilities to become key functions within every functional domain in an organisation, integrated both into the function itself through feedback loops, as well as for more conventional MIS reporting. Some of the basic building blocks will be the same as they are today, but the key characteristic is that users of these tools will be technologists specifically supporting business needs – i.e., part of ‘the business’ and not part of ‘IT’.

In the past, businesses have been supported by vendors providing expensive but easy-to-use tools to allow non-technical people to work with large datasets. The IT folks were very specifically supporting the core data warehouse & business intelligence infrastructure, or provided technical support for the development of particular reports. In these cases, the clients were typically non-technical, and the platform could only evolve as quickly as the software  vendors evolved.

The emerging (low-cost) tools for quantitative analytics will give rise to a post-Excel world of innovation, scale and distribution that will empower users, give rise to whole new business models, and be itself a big driver into how enterprise IT defines its role in the agile, unbundled, decentralised and as-a-service technology landscape of the near future.

Traditional IT organisations often saw the ‘application development’ functions as business aligned, and were comfortable with the client-oriented nature of providing technical infrastructure services to development teams. However, internal development teams supporting other (business-aligned) development teams is a fairly new concept, and will likely best be done by external specialist providers. This is a good example of where IT’s biggest role (apart from governance) in the future is in sourcing relevant providers, and ensuring business technologists are able to do their job effectively and efficiently in that environment.

In summary, technology is empowering quantitative analysts to much more on their own. IT organisations will need to think about how to make these capabilities available to their users, and how to incorporate it into IT strategies around big-data, cloud computing, security and data governance.

Agile quantitative analytics in Financial Services