On Trading Analytics. Interview with Cat Turley
“ Trades are driven by real-time market conditions where billions of dollars move every second, generating enormous amounts of data. The biggest challenge is minimizing the latency associated with analyzing these chaotic data streams and turning it into something that’s actionable for traders.”
Q1. What is your role at ExeQution Analytics?
Cat Turley: I’m the CEO and founder of ExeQution Analytics. We’re a boutique consultancy focused on helping financial organizations, particularly trading firms, take greater advantage of their data infrastructure. The story of ExeQution Analytics began 20 years ago, when I was working at an international broker. I challenged the head of trading to “do more” as I believed that we could write more interesting analytics and achieve better understanding of the markets and our trading patterns. I truly believed we were only skimming the surface of what kdb+ could achieve. He returned the challenge and invited me to build a green-field analytics platform capable of understanding market microstructure and providing real-time and historical signals to electronic trading strategies.
Over the past two decades, I’ve continued to refine this approach to analytics. Four years ago, we officially launched ExeQution Analytics as demand had grown, and we identified a gap in the market. There were plenty of resources focusing on the acquisition and storage of data, but less focus on what the data was used for. We developed a structured and flexible analytics framework to solve the problem that everyone was seeking to solve: how to make analytics more efficient and accessible across all aspects of the trading lifecycle. Now my role requires that I work closely with those we have partnered with, from financial organisations on both sides of the street, to technology leaders such as KX.
Q2. How do you help organizations maximize the value of their technology investments and improve data-driven innovation?
Cat Turley: What makes ExeQution Analytics unique is that we’re positioned right at the intersection of the three pillars of trading: the traders themselves, quants and technology leaders. We speak all three languages and provide a framework that helps everyone achieve their common goal of delivering better trading outcomes. Our standardized framework efficiently analyzes large volumes of market data at speed and scale. From there we create customized analytic platforms that enable firms to gain enhanced and actionable insights tailored to their unique trading workflows.
Trading teams cannot accelerate innovation if they’re stuck spending all of their time preparing data. We’re giving them the tools necessary to remove the onus of data preparation and instead focus on extracting signals, identifying patterns, and understanding market activity. When armed with these insights, firms can test more ideas, move faster, ask better questions about their data, and ultimately generate strategies that improve trading outcomes.
Q3. Let’s talk about Quant. What do they do and how have they evolved?
Cat Turley: Quant teams build and refine models that power trading strategies, everything from price prediction to portfolio optimization. This has always been a data-driven process, but over the years, thanks to the advancement and accessibility of computational tools, it has increased in complexity and sophistication. Now, quants can efficiently and quickly analyze years of historical market data to glean unique insights that optimize market prediction and trade execution.
Most financial organisations have been using advanced machine learning capabilities for the last decade or so, enabling more sophisticated predictions. There is potential for even further evolution as advances in AI become more integrated into the quant trading process through the use of large language models, vector databases and techniques such as time series similarity search.
The second significant avenue of evolution is the integration of real time market data into the quant lifecycle, enabling better understanding of how models react to the ever-evolving market conditions. As data volumes grow, it has never been so important to remain agile in volatile market conditions.
Q4. If we consider Intra Trade Monitoring: What are the challenges?
Cat Turley: Trades are driven by real-time market conditions where billions of dollars move every second, generating enormous amounts of data. The biggest challenge is minimizing the latency associated with analyzing these chaotic data streams and turning it into something that’s actionable for traders. Trading once relied heavily on human intuition and experience with many decisions based on “gut feeling”, but with the advances in markets and technology, this instinct can now be augmented with data-driven understanding. The challenge is getting the right analytics in front of the right person at the right time, so they can make the best decision to improve trading outcomes. These days, traders are typically monitoring thousands of individual orders at any one time, as algorithms control execution. They need access to tools that can distill all this noise into actionable insight.
Q5. How is Trading Analytics related to Quant and Intra Trade Monitoring? What do you see as major challenges here?
Cat Turley: Intra trade monitoring supports real-time observation and analysis of trade execution, feeding live data into analytics systems. Both traders and quant analysts depend on these analytics to measure performance feedback for refining computational models that drive pricing, forecasting, and trade decision-making. Essentially, these three components support pre- and post-trade analysis.
The challenge many firms are facing is how to use trading analytics to transform TCA from a tick-the-box exercise to a more comprehensive framework to properly understand the nuances and intricacies of trading execution and opportunities for optimisation. Historically, pre-trade and post-trade were often considered separate processes. To truly optimise execution, they should consider two aspects of the same process, where one informs the other.
This is where KX can offer an advantage: one of its unique attributes is that it’s a high-performance analytics database optimized for both real-time and historical data. Building a custom trading analytics platform using KX technology allows organisations to evolve towards more proactive analytics, enabling the identification of both optimisation opportunities as well as execution risks and alpha generation opportunities. Integrating real time data into the TCA/execution analysis or trading research process enables a better understanding of where a different trading decision would have resulted in an improved outcome, and back-testing with historical data can inform where this intuition offers statistically significant performance improvement.
Q6. You have been using KX for over 20 years now. How did the KX ecosystem evolve over time?
Cat Turley: It’s changed dramatically. When I started over 20 years ago, obtaining a kdb+ license required a much more substantial investment in development resources, often turning into a one-to-two-year project before it was put into production. Now, thanks to KX-driven platform releases and updates as well as tools like Data Intellect’s TorQ, the development of kdb+ infrastructure has been streamlined. Today, teams can take advantage of previous iterations and go to market much faster. It’s gone from a highly technical, custom-built process to something far more streamlined, accessible, and scalable. That means firms can focus on the nuance of their individual trading requirements, how to turn data into value, rather than spending as long on the building blocks of data ingest, storage and availability.
Q7. You use kdb+ for trading. What are the main benefits you see in using such a database?
Cat Turley: ExeQution Analytics framework is developed in q and designed for integration with kdb+ platforms. So, you could say that I’m a big advocate for q and kdb+, and that’s not just because of its unprecedented speed—but also the incredible flexibility that the q query language offers – it truly is the standout benefit. kdb+ is the fastest time-series database available, and q enables us to move beyond a data-warehouse to deliver genuine analytic platforms with a reduced time to market. That speed allows quants to pursue excellence – they can fail fast, learn fast, and keep improving their models.
As I briefly mentioned earlier, kdb+ is unique because it can handle both real-time and historical data without compromising speed and performance. In the trading world, this combined benefit is what allows for better trading outcomes, rather than a series of missed opportunities.
Q8. Specifically, how do you handle large volumes of real-time and historical data with low latency? Why does kdb+ being columnar matter?
Cat Turley: Our approach combines fast in-memory processing for live data with efficient on-disk, columnar storage for historical data, enabling seamless and high-speed time-series analytics across both. And kdb+’s columnar absolutely makes a difference in optimizing low-latency performance because it only pulls the fields needed.
Storing data in columns allows for faster reads, better compression, efficient CPU caching and parallel processing, all of which are ideal for fast-moving, analytical trading workloads.
Q9. kdb+ offers Q, a SQL-like language. How easy is it to use, and how do you encourage adoption over SQL?
Cat Turley: I am a huge advocate for the q language – it is elegant, expressive and enables incredibly fast time-to-market from a development perspective. An analytic written in q versus SQL or Python is typically 10 times more concise. This means it takes 10% of the time to write, your development team can be 10% of the size, and you incur 10% of the errors. It may have a reputation for being harder to learn but it is well worth getting over the initial learning curve, as it offers huge benefits once mastered, especially when dealing with time-series operations, high-frequency data and real-time decision making.
The other benefit is that the q community is unlike any other; you can really lean on these folks for support and learning materials. SQL gives you access to data and is well-suited for general purpose tasks and projects, but q is purpose-built for speed and analytics at scale which are critical benefits for those working with high-speed or high-volume data.
Q10. kdb+ is used across hedge funds, investment banks, and trading firms. What are the similarities and differences among them when dealing with quantitative trading operations?
Cat Turley: Their trading operations are similar in the sense that they are all working with high data volumes and require low latency. Of course, they have varying levels of latency tolerance and data flow, but overall, I would say the premise is the same: operations need to optimize data-intensive workloads and minimize time-to-decision.
What I think differs is their objectives. Hedge funds prioritize strategy simulation and alpha generation, banks emphasize client service and pricing, and trading firms are laser focused on speed and execution edge. Regardless of the objective, all rely on the ability to process massive volumes of real-time and historical data with precision and speed.
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Cat Turley, CEO/Founder, ExeQution Analytics
With 20 years’ experience working with leading global investment banks and some of the world’s largest asset managers, Cat has an extensive understanding of market microstructures, execution analysis and how the right choice of technology can empower organisations to achieve more with less. Cat is passionate about improving efficiency and understanding across all areas of trading and founded ExeQution Analytics to contribute towards this goal.
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