The Architecture of Modern FX Platforms

Foreign exchange platforms require a unique combination of speed, accuracy, and regulatory compliance. We examine the architectural decisions that make modern FX systems tick.

The FX Platform Challenge

Building an FX platform means solving multiple hard problems simultaneously: aggregating rates from diverse liquidity providers, executing trades with minimal slippage, managing settlement across different currencies and time zones, and maintaining compliance with regulations in every jurisdiction you operate.

Get any of these wrong, and you're either losing money or losing your license.

Rate Aggregation

The foundation of any FX platform is its rate engine. Modern platforms aggregate rates from multiple liquidity providers—banks, non-bank market makers, ECNs—and present the best available price to customers.

Key considerations for rate aggregation:

• Latency: Rates must be fresh; stale prices lead to rejections or losses
• Reliability: Provider failures must not impact customer experience
• Best execution: Regulations require demonstrable best execution
• Spread management: Business logic for applying margins

The Rate Engine Architecture

We typically implement rate engines as dedicated microservices, separate from the main platform. This allows independent scaling and deployment, and isolates rate-related issues from affecting other platform functions.

┌─────────────────────────────────────────────────────┐
│                    Rate Engine                       │
├─────────────────────────────────────────────────────┤
│  ┌──────────┐  ┌──────────┐  ┌──────────┐          │
│  │Provider 1│  │Provider 2│  │Provider N│          │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘          │
│       │             │             │                 │
│       └─────────────┼─────────────┘                 │
│                     ▼                               │
│           ┌─────────────────┐                       │
│           │  Aggregator     │                       │
│           │  (Best Price)   │                       │
│           └────────┬────────┘                       │
│                    ▼                                │
│           ┌─────────────────┐                       │
│           │  Margin Engine  │                       │
│           └────────┬────────┘                       │
│                    ▼                                │
│           ┌─────────────────┐                       │
│           │  Rate Cache     │                       │
│           │  (Redis)        │                       │
│           └─────────────────┘                       │
└─────────────────────────────────────────────────────┘

Order Execution

When a customer requests a trade, the platform must lock a rate, validate the order, check available balance, execute against the liquidity provider, and update all relevant records—ideally in under 100 milliseconds.

We use optimistic locking for balance checks and idempotent execution to handle retries safely. Each trade generates an execution report that captures the full audit trail for regulatory compliance.

Settlement and Reconciliation

FX settlement is where theoretical architecture meets practical reality. Different currency pairs settle on different timelines (T+0 to T+2), nostro accounts must be managed, and reconciliation must catch discrepancies before they become problems.

Our settlement systems run continuous reconciliation against bank statements, flagging breaks for investigation. Automated matching handles the 95% of transactions that reconcile cleanly; human review addresses the exceptions.

Scaling Considerations

FX volumes can be spiky—news events, market opens, and month-end flows can multiply traffic dramatically. Our platforms are designed for 10x normal capacity, with auto-scaling triggered by queue depth and latency metrics.

Conclusion

Modern FX platforms succeed by treating each component—rate aggregation, execution, settlement—as a distinct domain with clear interfaces. This separation enables independent scaling, easier testing, and faster iteration. The complexity is unavoidable; the goal is to manage it through good architecture.