What Happens If Your Market Data Feed Fails?
What Happens If Your Market Data Feed Fails?
Direct answer
A failure in your market data feed can produce stale prices, incorrect risk calculations, and failed or misrouted executions. NxCore provides a normalized multi-asset stream delivered over UDP/TCP, with historical data available separately to help teams implement failover strategies and post-incident analysis.
Why this matters
Feed failures translate directly into financial and regulatory risk. Stale or missing ticks can cause automated strategies to trade on outdated information, risk engines to miscalculate exposures, and compliance systems to miss reportable events. Preparing for failures reduces PnL volatility and shortens incident recovery time.
How failures typically manifest
| Failure Mode | Description | Detection Method |
| Silent stall | Updates stop but systems continue using last known prices | Update cadence monitoring |
| Partial degradation | Some symbols or venues drop while others continue | Per-symbol freshness checks |
| Sequencing errors | Timestamps or ordering issues cause inconsistent state | Sequence number validation |
| Throughput collapse | Spikes overwhelm ingestion, causing backpressure | Message rate monitoring |
Design detection and mitigation for each mode rather than assuming a single failure pattern.
Failure Mitigation Patterns: Practical Breakdown
| Pattern | Implementation |
| Redundant feeds | Subscribe to ≥1 independent feed as warm failover |
| Automated probes | Synthetic subscriptions verifying update cadence and latency |
| Circuit breakers | Stop automated execution when freshness or sequencing thresholds breached |
| Graceful degradation | Switch to coarser signals or conservative risk limits when microstructure data unavailable |
| Replay & forensics | Reconstruct state and validate fills after recovery using historical data (supplied separately) |
| Runbooks & escalation | Documented steps, contact lists, automated paging |
Real‑world example
A derivatives desk experienced a partial feed outage affecting a primary venue. Automated probes detected a drop in update cadence and triggered a circuit breaker that paused automated quoting. The system routed pricing to a consolidated feed for surveillance and to a secondary normalized feed for execution. Engineers used historical data (supplied separately) to reconstruct missing events, reconcile fills, and adjust the post-trade ledger. The incident caused no regulatory breach and limited PnL impact because failover thresholds and runbooks were in place.
Common mistakes
- Silently falling back to stale caches instead of failing safe
- Relying on a single vendor without an independent failover path
- Not testing failover under realistic load and market conditions
- Ignoring sequencing and timestamp checks when validating failover data sources
- Failing to include entitlements and licensing in failover planning
Frequently asked questions
Q: What is the simplest effective failover design?
A: A secondary feed from a different vendor (or a consolidated SIP feed) used as a warm standby with automated probes and routing rules. Test it regularly.
Q: How do I detect a subtle degradation before it becomes a full outage?
A: Monitor update cadence, message size distribution, and timestamp sequencing percentiles, not just uptime pings.
Q: Should I stop trading immediately on any data anomaly?
A: Not always. Use tiered responses: reduce aggressiveness first, then widen risk limits, then pause automated strategies if anomalies persist.
Q: How often should we test failover?
A: Regularly. Run quarterly drills. Execute at least one annual full failover test under representative load.
Q: Can historical data help after a failure?
A: Yes. Historical data (supplied separately) can help reconstruct market state, validate fills, and perform root cause analysis.
Who This Is For / Who This Is NOT For
For: SREs, trading operations, risk teams, and infrastructure engineers responsible for production trading systems.
NOT for: Retail traders, dashboard-only users, or teams that do not execute live orders.
What to do next
- Implement probes that measure update cadence, sequencing, and latency
- Define failover rules: warm standby feeds, circuit breaker thresholds, graceful degradation modes
- Run a failover drill using historical data to validate reconciliation and post-trade workflows
- Request vendor artifacts: stress test reports, latency information, and sample data