5 Structural Shifts Redefining Market Data and Trading at Quantitative Firms

Extended trading hours and accelerating market data volumes are expanding the scope and complexity of modern trading environments, challenging infrastructure originally designed for more stable, session-defined market conditions. The result is a growing concern among financial firms that existing infrastructure may not be able to keep pace with accelerating data demands, rising operational complexity, and expanding market coverage.

These shifts have led to three recurring infrastructure challenges:

• Shrinking time to opportunity as strategies move faster
• Rising operational drag from market data complexity
• Increasing difficulty for firms in scaling performance and coverage without adding risk or footprint

These challenges arise when volatility spikes, liquidity shifts beyond the core session, and firms try to avoid throwing more hardware at the problem to preserve performance. Liquidity isn’t just shifting across hours but also across venues, raising the bar on market-wide visibility and making it harder to maintain a singular view of the market. 

To understand how firms are responding, Exegy partnered with Acuiti to gather insights from 61 firms operating in trading globally. The following blog post summarizes the findings of the white paper, 2026 State of Trading Infrastructure: How Structural Shifts Are Redefining Market Data and Trading at Quantitative Firms, offering five key insights for modern financial firms navigating unprecedented market shifts.

This post focuses on the infrastructure pressures tied to equities today, but the full white paper also digs into how crypto and cross-asset strategies are reshaping expectations for always-on infrastructure.

Volume Is Only Half the Problem

Average daily volume is no longer a reliable metric for infrastructure planning. Instead, today’s architectures must be designed to handle “burstiness”—sudden spikes in which market data rates can exceed two times typical averages during volatile periods. In other words, as opposed to growing smoothly, data rates now surge sharply during short, intense periods of volatility. The result is the creation of peak loads that far exceed daily averages, which, for many firms, presents significant operational risk.

Beyond higher average volumes, modern trading architectures must be designed to absorb extreme variability and sudden bursts in data rates. In short, peak load—not the mean—has become the defining constraint. Because capacity planning has to anchor on peak load behavior instead of mean, average daily volume is no longer a meaningful proxy. As a result, firms either overprovision or risk drops and latency spikes at the exact moment opportunity is greatest. 

As stated in the report, “Volatile markets are often when pricing dislocations emerge, and trading opportunities are at their greatest. Whether firms can capture those opportunities depends not on average throughput but on the resilience of their market data infrastructure under extreme, unpredictable load.”

Legacy Infrastructure Limits Performance In Modern Markets

Market data infrastructure tends to be a limiting factor in maintaining performance and reliability, and it often represents the primary point of failure during high volatility. Nearly half of the surveyed firms reported that their systems struggled to process market data during periods of high volatility. 

On top of that, market data processing is often the first function to come under strain as message rates rise. When asked which components of their front-office infrastructure would require investment to support rising volumes, nearly 60% of respondents identified market data processing. A whopping 74% experienced performance issues, such as latency spikes, dropped data, and complete outages in peak conditions. These statistics illustrate the effect of strain on market data processing, which causes firms to miss the moments that matter most. 

Outdated Market Data Infrastructure Leads to Missed Opportunities

Infrastructure limitations translate directly into lost revenue; only 3% of quantitative firms reported being able to capture all trading opportunities that volatile periods presented. Infrastructure limitations can directly translate into missed trading opportunities—particularly during periods in which potential returns are highest.

For nearly three-fourths of respondents, periods of elevated volatility and volume led to some level of disruption in their market data infrastructure. So, what’s holding firms back from more robust redundancy? The primary factor reported was cost, underscoring the trade-offs between performance, resilience, and operational complexity that firms face. In fact, 1 in 5 firms lack backup market data, which explains why disruptions persist despite risk awareness. 

Market Structure Changes Are Driving Higher Data Volumes

Additionally, incremental rule changes, such as the United States Securities and Exchange Commission’s (SEC’s) shift to half-penny quoting and dynamic “round lot” definitions, have created a cumulative and permanent increase in the baseline of market data traffic. In other words, as opposed to being driven by a single event, growth in market data volumes from regulatory change now reflects the cumulative impact of incremental rule changes. Beyond volatility, regulatory changes are contributing to higher baseline message rates across markets.

One example of this occurrence is the SEC’s amendment to minimum pricing increments. By allowing more granular price increments, quote churn and the number of updates that systems must ingest, normalise, and distribute in real time increase. Even though individual price movements may appear small, the cumulative effect is a sustained increase in market data traffic.

Secondly, the definition of a “round lot” under Regulation National Market System has been updated. This change requires firms to dynamically reference and apply daily reference data when processing quotes, demonstrating a shift from static assumptions to variable input.

This isn’t temporary volatility—it’s a structural reset to the baseline load firms must plan for. These baseline increases compound the operational drag of market data complexity because more churn means more normalization, distribution, and downstream systems feeling the pressure.

Preserving Competitive Latency Is Increasingly Challenging

Lastly, maintaining a competitive relative latency position is now considered as important as the trading strategy logic itself for the majority of firms (58%). Firms increasingly evaluate latency in relative, competitive terms rather than absolute benchmarks.

Capacity for market data processing is a key variable determining whether firms can maintain competitive latency during volatile conditions. Essentially, the question that firms are asking themselves is no longer “Are we fast?” The objective has shifted to “Are we fast enough compared to everyone else?” As it stands, relative position is more significant than access to speed, and market data handling is one of the biggest determinants of that status. This influence, on top of predictive edge erosion due to more complex and competitive market conditions, poses a serious challenge for systematic traders.

What’s the Bottom Line?

Today’s market data infrastructure is being tested on multiple fronts at once. Rising volumes, increasing burstiness, and shifting liquidity patterns are placing sustained pressure on systems originally designed for more predictable conditions. Meanwhile, latency requirements remain unforgiving, with even brief periods of degradation translating directly into missed opportunities. 

Across all of these shifts, the same message comes through—firms can’t treat market data as a background utility anymore; it’s the bottleneck, the risk surface, and often the deciding factor in whether they can scale coverage without blowing up cost and complexity.
Looking to dive deeper? Download the full 2026 State of Infrastructure report to read the findings in their entirety and help prepare your firm for continued growth, variability, and structural change.