3 min read

Builder Spotlight: Market beating AI + Software platform for Investment Managers

Platform enabling a team of 2 investment professionals to outperform teams of 200+

Build time: 18 months build time by 7  finance & machine learning practitioners.

The Problem

Can a machine learning enabled investment strategy beat a passive fund? Can a Machine + Human beat a traditional analysis? 🤔

This was the task that Rezco Asset Management encountered around 2015.  What initially started around a discussion on how to improve their investment idea screening tools, turned into a multi-year journey in which one had to master the intersection of machine learning, software engineering, finance and human behaviour.  Read more on our journey to scale in this memo.

We learned through experience that the engineering challenge of developing and running cutting edge machine learning models at scale was only half the problem. And in many ways it is the easier half to solve leveraging well developed machine learning cloud based IaaS solutions offered by the likes of Google Cloud, AWS and Azure.

The hardest part of the problem was learning how to frictionlessly incorporate AI research cycles and predictive signals into the workflow of Portfolio Managers (PMs). The hardest part of the problem was not figuring out how to create value through machine learning, but figuring out how to help the consumers of this technology realise the value being created in the areas most important to them.

The Solution

Meet the stakeholders where they are.

The core guiding philosophy that helped us solve this problem is that we needed to meet the stakeholders where they are. Context switching destroys focus and value realisation.  Here are a few simplified examples:

  • Sending a set of stock predictions in a spreadsheet to the investment team and then asking for feedback does not work.  This is not meeting the investment team where they are, instead its essentially asking the PM to 'step away from their current context'.  PMs need to be able to make decisions in a distraction free environment which enables and maintains focus.
  • Creating a "Swivel-Chair-Interface" by requiring a PM to log into some portal or pull up some email to access stock predictions.  There are already a large number of investment tools, adding another 'terminal' just to access predictions does not make sense.
Swivel chair is a slang term for a common interface work-around that involves manually entering data into one system and then entering the same data into another system.

Instead, what if the machine learning team is able to deliver predictions and well crafted model properties (features, model hypothesis, benchmark etc.) directly to into the Portfolio Manager's context?

Alis.Build enabled this through a set of consistent, well organised, well defined and highly performant APIs.  Every interaction between the stakeholders are performed through this single API layer. Practically, these 7 individuals implemented more than 200 micro services, for example services that:

  • manage the integration with Morningstar Direct to generate marketing material and analytics
  • building a highly scalable "Alpha Console" integrating machine learning driven predictive signals with traditional analyst research.
  • manage predictions (GetPrediction, ListPredictions, UpdatePrediction, etc.)
  • manage the data pipelines from Bloomberg
  • capture any investment research memos and notes and propagate these up at a stock level next to prediction insights.
  • etc. (more than 200 of these 😎)

The Result

Rezco Asset Management is generating outperformance on a pure Machine Learning investment strategy, after all trading costs.

This tight integration between the machine learning team and PMs creates a closed feedback loop between the machine learning and finance domains.  Better models -> more engaged PMs -> better human in the loop feedback -> Better models

In the following video, Simon Sylvester (CEO) shares how machine learning conceptually fits into their investment management world:

Watch Part 2, where you can see a practical example of how ML delivers real value to their clients

How Alis.Build made it possible

Best-in-class, resource-driven API development and cloud infrastructure management made easy for non-professional software developers with:

  1. Out of the box libraries, Excel add-ins and documentation;
  2. Support from the Alis Build team on thorny technical issues; and
  3. Access to learning and development opportunities for the team.