Introduction

In this high-level article, we present the core architecture and components of a limit order book (LOB) for a high frequency trading (HFT) framework written in Rust. We cover conceptual design, data flow, and key performance considerations. This overview prepares you for detailed implementation topics in later articles.

Overview of a limit order book

A limit order book maintains two opposing queues—bids (buy orders) and asks (sell orders)—sorted by price and time priority. Incoming orders match automatically against the best available resting orders. Any unmatched quantity remains in the book until executed or canceled. This continuous double auction model underpins most electronic markets.

Core components

• Order management: handle creation, cancellation, and modification of orders
• Matching engine: apply price and time priority rules to execute incoming orders
• Data structures: use cache-friendly collections to store and traverse price levels and order queues
• Event bus: publish events (order accepted, trade executed, order canceled) to downstream modules such as risk, analytics, and market data

Architectural outline

1. Ingress layer: receive market data and client orders via network protocols (for example FIX or UDP multicast)
2. Order book core: maintain in memory the LOB and process messages in a single thread for determinism
3. Persistence and logging: append events to a durable log for recovery and audit
4. Distribution layer: send aggregated book updates and trade notifications to clients and internal services

Data flow

1. Receive order: ingress parses, validates, and converts incoming message into an order model
2. Match or queue: matching engine attempts to fill against the opposite side; any remainder is queued in the book
3. Emit events: generate events for each trade and final order state
4. Distribute updates: publish book snapshots or incremental deltas to subscribers

Design considerations

• Performance: prefer contiguous memory layouts (for example arrays or intrusive lists) and minimize dynamic allocations
• Latency: use lock free or single threaded designs and write branch predictable code in hot paths
• Consistency: guarantee deterministic processing to support reliable replay and testing
• Scalability: support multiple instruments via sharded books or partitioned threads

Summary and next steps

We have outlined a high level vision of a Rust based limit order book suitable for professional HFT systems. In the next article we will implement the core data structures, matching algorithms, and Rust specific optimizations needed to achieve low latency and high throughput.