In what ways does your technology support modularity — allowing quants to build, plug in, or customize components rather than adopt an all-or-nothing stack?

CWIQ has a multi-layered approach to give the quant researchers all the flexibility they need in handling large amounts of datasets, and allowing them the freedom to build models in Python, R, C++ as they want. The interface is designed to give the quant the experience like they are working on their local machines while giving them access to a powerful elastic compute engine that runs with the major cloud providers. The quants are given budgets, they choose CPU or GPU, how many nodes and clusters they need to perform their analysis. Team members don’t hold each other up running analytics over 100s of machines and petabytes of data.

The bedrock is working with clean data. This is achieved by mapping every dataset, a seamless ingestion process, and importantly having a single point-in-time identifier to allow researchers to easily connect data together. The data has a granular set of entitlements that can hide data from people or restrict access to date ranges, this facilitates bringing on new researchers with exposing all your data on day one.

Backtesting is a framework meaning that there is full flexibility and customization offered to the quants. This flexibility allows researchers to try out their scenarios and quickly compare outcomes. Once complete, the switch from backtesting to production is seamless as the models run on the same structures in backtesting as production. No code rewriting.

The tech stack of CWIQ is comprehensive and modular. You can buy into the full stack to eliminate friction such as having to deal with integration and multiple vendors or you can pick the modules you need for your environment.

What aspects of your product or approach do you think are most valuable to quant teams — whether that’s speed, transparency, or the ability to handle complex data?

Efficiency is the starting point at CWIQ. Speed, transparency and the ability to handle complex data are moot points if they come at a high cost.

Beginning with handling complex data through the drive for efficiency we took the approach of ensuring that the quants spend their time doing research. We know that with complex data, there are issues with normalising, mapping and cleaning data, and that often it is the quants that do this in order to get the data in a state that they can analyse it. We also know that data engineers are integral to some teams to organise the data for the quants, and that comes with its own cost. The team at CWIQ maps hundreds of datasets so you don’t have to. Data is assigned a unique, point-in-time identifier to make navigation through the labyrinth of security identifiers a thing for others. The ingestion processes are automated meaning that the data is loaded in a timely manner using the provided mapping.

CWIQ has a granular entitlements system that not only manages what data a quant can use, but can hide data, and has the ability to restrict date ranges.

In the context of clean, well managed, data, the final aspect of CWIQ is to ensure that running analytics is as efficient and fast as possible. This is achieved using a patented File System that eliminates duplication, allows the data to be visible across different cloud providers, and has been built for speed of retrieval. Combined with File System is a set of tools that manages elastic compute on the cloud while maintaining cost control, jobs are routed to the most cost effective providers, instances close when finished meaning there are no surprises when the bills land.

The drive for efficiency ensures that the quants are productive and the costs contained meaning that there is less drag on the profits.

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Over the next 12–18 months, what new capabilities or product areas are you most excited about launching — and why do they matter to quants?

In the next 12-18 months CWIQ is looking to improve the data store as it takes time to evaluate datasets, this represents a cost that can be eliminated by making it more efficient.

This will be achieved by:

• AI automatically generates trading signals from any dataset
• Full evaluation pipeline in hours, not weeks
• Try before you buy - rapid ROI assessment

The benefits are:

• For Data Vendors: Faster proof of value means faster sales cycles
• For Quants: Lower risk to evaluate new datasets

The evaluation also entails understanding the documentation. CWIQ is being enhanced to provide tools to enable Quants to chat with the data documentation using AI, Auto-generated data quality testing scripts (Python) giving instant answers about fields, coverage, and usage.

Our focus on efficiency continues with these planned upgrades to the datastore.

Code Willing is an official exhibitor at Quant Strats Europe 2025 and will be joining us in London on 14–15 October. Secure your ticket below to meet the team and explore their cutting-edge quant research platform, CWIQ.

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