Engineering for an Always-On Market

Financial markets are entering a new era where downtime is no longer tolerated. As exchanges stretch their operating hours toward nearly full-day cycles — with some aiming for 24/7 trading — the overnight maintenance windows that once allowed teams to deploy updates and perform critical maintenance have all but vanished. Every change, every failover, and every monitoring decision must be carried out on a live system, with billions of dollars moving across networks in real time. In such an environment, the tolerance for error is measured not in seconds but in potential financial and reputational losses.

Reliability

In financial markets, gradual and automated deployment isn’t just a best practice — it’s a safeguard against costly mistakes. Traditional deployment approaches, which involve manual steps and all-or-nothing rollouts, are inherently risky. In these systems, millions of trades and billions of dollars move every second. Even a short disruption can cause failed transactions, trigger regulatory intervention, and destabilize the market. Blue-green deployments, canary releases, and fully automated pipelines introduce changes progressively, reducing the risk of customer impact and allowing quick rollback if issues arise.

Reliability also depends on robust failover and disaster recovery strategies. If an entire availability zone fails — whether due to a power outage, a natural disaster, or an upstream infrastructure issue — trading must continue without interruption. Active-active multi-region architectures allow live traffic to shift instantly to healthy regions, keeping order books and transaction processing available worldwide. But failover alone isn’t enough. Disaster recovery planning must account for worst-case scenarios, such as the complete loss of a primary data center. This means maintaining offsite backups, regularly testing restoration processes, and ensuring that data can be rebuilt quickly and accurately from secondary locations before disaster strikes.

Observability

In an always-on financial market, observability is the nervous system of the infrastructure. Metrics, logs, and distributed traces must be tightly integrated, allowing engineers to detect, understand, and address issues in real time. This goes far beyond the traditional focus on uptime percentages. Traders and investors care about transaction completion times, order book update latency, and the accuracy of market data feeds. A system might technically be “up” but still be unusable if trades are delayed or prices are stale.

Specialized observability measures are needed to meet these demands. Latency metrics should be segmented by transaction type and trading instrument to pinpoint issues affecting specific market segments. Real-time alerting must be tuned to catch anomalies early while avoiding false positives. Visualization dashboards should serve both technical and business audiences — engineers need to see infrastructure health, while market operators need to see trading flow health. In a market where milliseconds matter, precision observability is the foundation of reliability.

Scalability

Even in traditional hours, trading activity can spike dramatically. Earnings announcements, central bank decisions, and geopolitical developments can all send trading volumes surging within seconds. The release of U.S. CPI inflation data, for example, has repeatedly caused immediate spikes in activity across equities and futures.

Crypto exchanges face this volatility constantly and have adapted with API-driven architectures designed for high concurrency. These systems can handle thousands of automated trading bots reacting to market events at the same time. They process orders in parallel, scale horizontally under load, and apply rate limiting to protect core services. For traditional exchanges, adopting similar API-first strategies would not only improve scalability, but also enable faster recovery from sudden demand spikes and reduce the risk of cascading failures during high-impact events.

AI in the Always-On Market

The complexity of extended-hour markets means manual incident response will no longer scale. AI is becoming essential for both infrastructure management and market oversight.

On the infrastructure side, predictive models can anticipate demand spikes and adjust scaling rules ahead of time. AI can dynamically re-route traffic, tune rate limits, and allocate resources in real time to maintain stability while keeping costs under control.

On the surveillance side, advanced anomaly detection algorithms can identify unusual trading patterns that may indicate market manipulation, insider trading, or coordinated bot activity. AI can cross-reference historical data, market news, and real-time trades to detect risks invisible to manual review, enabling exchanges to act before problems escalate.

Culture

Technology alone cannot sustain an always-on market. The operational culture must evolve alongside it. One critical shift is adopting a follow-the-sun support model, with engineering and operations teams distributed across time zones. This ensures that qualified personnel are available at all hours, reducing response times and preventing fatigue-related errors.

Runbooks are another cornerstone. Procedures for deployments, incident response, and failover need to be clearly documented, automated where possible, and tested regularly in realistic simulations. This ensures that when a real incident occurs, every step is clear, practiced, and reliable under pressure.

Most importantly, reliability must be treated as a shared responsibility across the organization. Developers, SREs, and product teams need to collaborate from the earliest stages of feature design, balancing innovation with stability. In the financial sector, where trust is the currency of the business, even minor outages can have long-term consequences. Those who make reliability a cultural priority today will not just keep up with 24/7 markets — they will shape how those markets operate for decades to come.