Order Matching Engines

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SUMMARY

An order matching engine is the core technology component of a trading venue that pairs buy and sell orders according to predetermined rules. It maintains the order book, processes incoming orders, and executes trades when matching conditions are met, while ensuring price-time priority and other market rules are followed.

How order matching engines work

At their core, matching engines implement a set of rules that determine how orders interact and execute. The engine maintains a limit order book that organizes resting orders by price and time priority. When new orders arrive, the matching engine follows these key steps:

  1. Validates the incoming order
  2. Checks for matching opportunities against existing orders
  3. Either executes trades or adds the order to the book
  4. Disseminates market data updates

The matching process must be deterministic, fair, and incredibly fast - modern matching engines operate at microsecond or even nanosecond latencies.

Next generation time-series database

QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.

Key components and features

Price-time priority

Most matching engines implement price-time priority (also called FIFO - First In, First Out). This means:

  • Better-priced orders execute first
  • At the same price, earlier orders get priority
  • Priority is preserved until an order is modified

Order types support

Matching engines must handle various order types:

  • Market orders
  • Limit orders
  • Stop orders
  • Pegged orders
  • Complex order types

Each order type requires specific processing logic while maintaining consistent behavior.

Performance considerations

Latency management

Tick-to-trade latency is critical for matching engines. Key factors include:

  • Hardware optimization
  • Memory management
  • Network stack efficiency
  • Process scheduling

Deterministic behavior

Matching engines must produce consistent results regardless of:

  • Market conditions
  • Order flow rates
  • System load

Market integrity features

Self-match prevention

Self-match prevention stops trades between orders from the same participant, preventing wash trading and other manipulative practices.

Price protection

Matching engines implement various price protections:

Market data generation

Matching engines are the authoritative source for market data:

  • Order book updates
  • Trade reports
  • Statistics
  • Reference data

This data must be generated and distributed with minimal latency through market data feed handlers.

Integration points

Modern matching engines interface with multiple systems:

Regulatory considerations

Matching engines must comply with various regulations:

  • Regulation NMS in U.S. markets
  • MiFID II in European markets
  • Exchange-specific rules
  • Audit trail requirements

This ensures fair and orderly markets while maintaining regulatory compliance.

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