Market Data and Connectivity in Trading Infrastructure
Market data and connectivity sit at the bottom of the trading infrastructure stack. Most of what determines whether a strategy runs cleanly in production is decided here. Most of the questions about it are not asked.
Bad market data stays quiet. It produces marginally noisier signals, occasionally wrong fills, and reconciliation gaps that surface days or weeks after the trade. By the time anyone notices, the cause is hard to isolate. Connectivity issues behave the same way. A failover that did not trigger when expected. A latency spike on one route that nobody noticed. A missed callback that left a position misreported until end-of-day.
For investors evaluating a manager or platform, this is one of the easier infrastructure layers to verify and one of the most often skipped. What follows covers what to ask about both, in plain language, without requiring an engineering background.
What market data quality actually means
Market data is not a single thing. It is a chain of decisions made by the manager about which feeds to use, how to timestamp them, how to normalize across venues, how to handle gaps, and how to detect when something has gone wrong. Each decision is small. The cumulative effect is large.
Three properties matter most. Source accuracy is the first. Direct feeds from exchanges and consolidated feeds from data vendors carry different latency profiles, different completeness guarantees, and different failure modes. A manager who cannot name their feed sources by venue and asset class is using market data the way a sommelier uses tap water.
Timestamping is the second. The most common quiet error in market data handling is timestamp inconsistency across venues. A trade timestamped by the exchange and a quote timestamped by the consolidated feed do not align cleanly, and systems that assume they do produce results that look right and are not.
Missing-data handling is the third. Real feeds drop messages. Real connections lag. The question is what the system does in those moments, with specific timeframes and explicit triggers rather than a general appeal to robustness.
What connectivity quality actually means
Connectivity is the route from the manager’s system to the venue or broker, including everything that can go wrong along that route. The properties worth asking about are similar in spirit to market data and distinct in the specifics.
Direct exchange connectivity differs from broker-mediated access. Direct access exposes the system to venue-specific quirks and reduces dependencies. Broker-mediated access is simpler operationally and introduces a third party between the strategy and the market. Neither is automatically better. The right choice depends on what the strategy requires.
Failover topology is the second property. A well-built connectivity setup documents primary and secondary paths for each venue, with explicit trigger conditions for switching between them and a stated timeframe for detection. Setups that describe redundancy in general terms without those specifics are usually weaker than the description suggests.
Latency budgeting per route is the third. Latency is not a single number. It varies by venue, time of day, and message type. A connectivity setup that claims a single latency figure across all routes is using marketing language rather than operational reality.
What investors should verify before treating market data and connectivity as evidence
Three questions tend to surface what matters quickly.
The first is who the feed sources are, by venue and asset class, and whether the manager can name them without checking. The answer should be specific. Direct exchange feeds for these venues. Consolidated feeds from these vendors for those. A documented fallback path between them. Vague answers here are diagnostic. Real operations can answer this question in two sentences.
The second is how the system detects and handles a stale feed. Stale data is one of the most common quiet failure modes in production systems, and the correct response is detection within a stated number of seconds, with a defined action and a specific person who gets alerted. A manager whose answer is “we have monitoring” without pinning down the timeframe, the action, or the alert is describing intention rather than capability.
The third is failover. Specifically, when failover last triggered, what caused it, and what was changed afterward. Real systems have failover events. A manager whose answer is that failover has never been needed is either operating a system younger than its first significant incident, or not monitoring closely enough to know.
- “We use premium data providers” with no specific sources by venue or asset class
- “We have robust monitoring” with no stated detection timeframe or alert path
- “We have full redundancy” with no spelled-out failover triggers
- “We have never had an incident” with the system in production for more than two years
None of these questions require a technical background to ask. All of them produce different answers from different operations. The pattern of vague answers across all three is itself the diagnostic signal.