matchcore 0.1.2

A high-performance order book and price-time matching engine implemented as a single-threaded, deterministic, in-memory state machine
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Matchcore

Matchcore is a high-performance order book and price-time matching engine implemented as a single-threaded, deterministic, in-memory state machine.

It is designed for building low-latency trading systems, exchange simulators, and market-microstructure research tools.

The architecture follows principles popularized by the LMAX Architecture, prioritizing deterministic execution, minimal synchronization, and predictable performance.

Features

  • Price-time priority matching engine
  • Deterministic state machine execution
  • Single-threaded design for minimal latency
  • Efficient in-memory order book
  • Support for advanced order types and flags (e.g., iceberg, pegged, time-in-force)
  • Designed for integration with event-driven trading systems
  • Clear command → outcome model for reproducible execution

Architecture

The design is heavily inspired by the LMAX architecture, a model widely used in low-latency trading systems.

Core principles include:

  • Single-threaded state machine
  • Event-driven command processing
  • Deterministic execution
  • In-memory data structures

These design choices eliminate synchronization overhead while guaranteeing reproducible behavior.

Single-threaded

For an order book of a single instrument, events must be processed strictly sequentially.

Each event mutates the state of the book and the result of one event directly affects the next. Parallelizing matching for the same instrument therefore provides no performance benefit while introducing locking, contention, and complexity.

Running the matching engine on a single thread provides several advantages:

  • No locks, contention, or synchronization overhead
  • Predictable latency
  • Simpler correctness guarantees

This does not mean the entire application must be single-threaded.

A typical architecture may look like:

Command Reader/Decoder → Ring Buffer → Matchcore Engine → Ring Buffer → Execution Outcome Encoder/Writer

Systems can scale horizontally by sharding instruments across multiple engine threads.

For example:

Thread 1 → BTC-USD order book
Thread 2 → ETH-USD order book
Thread 3 → SOL-USD order book

Deterministic

Matchcore operates as a pure deterministic state machine.

Given:

  • The same initial state
  • The same sequence of commands

the engine will always produce exactly the same results.

This property enables:

  • Deterministic replay
  • Offline backtesting
  • Simulation environments
  • Auditability
  • Event-sourced architectures

Deterministic execution is particularly valuable for trading systems where correctness and reproducibility are critical.

In-memory

All state is maintained entirely in memory.

The order book, price levels, and internal queues are optimized for fast access and minimal allocations.

This design provides:

  • Extremely low latency
  • Predictable performance
  • Efficient memory access patterns

Persistence and replication are expected to be handled outside the engine, typically through event logs and snapshots.

Core Concepts

Matchcore processes commands and produces outcomes.

Command → Matchcore Engine → Outcome

Commands represent user intent:

  • Submit order
  • Amend order
  • Cancel order

Outcomes describe the result of execution:

  • Applied successfully
  • Rejected because the command is invalid or cannot be executed in the current state of the order book

Successfully applied commands may also produce:

  • Trades
  • Order state changes
  • Triggered orders

Example

use matchcore::*;

let mut book = OrderBook::new("ETH/USD");

let outcome = book.execute(&Command {
    meta: CommandMeta {
        sequence_number: SequenceNumber(0),
        timestamp: Timestamp(1000),
    },
    kind: CommandKind::Submit(SubmitCmd {
        order: NewOrder::Limit(LimitOrder::new(
            Price(100),
            QuantityPolicy::Standard {
                quantity: Quantity(10),
            },
            OrderFlags::new(Side::Buy, false, TimeInForce::Gtc),
        )),
    }),
});

println!("{}", outcome);

More examples can be found in the examples directory.

Supported Order Features

Matchcore supports the following order types and execution options.

Types

  • Market Order: executes immediately against the best available liquidity; optionally supports market-to-limit behavior if not fully filled
  • Limit Order: executes at the specified price or better
  • Pegged Order: dynamically reprices based on a reference price (e.g., best bid/ask)

Flags

  • Post-Only: ensures the order adds liquidity only
  • Time-in-Force: defines order lifetime (e.g., GTC, IOC, FOK, GTD)

Quantity Policies

  • Standard: fully visible quantity
  • Iceberg: partially visible quantity with hidden reserve that replenishes

Peg References

  • Primary: pegs to the same-side best price (e.g., best bid for buy)
  • Market: pegs to the opposite-side best price (e.g., best ask for buy)
  • Mid-Price: pegs to the midpoint between best bid and best ask

Performance

Benchmarks are run with Criterion.

Matchcore is designed for low-latency, single-threaded, deterministic execution.

Representative benchmark results measured on an Apple M4 using Rust stable are shown below.

To run the benchmarks in your environment, run make bench.

Submit

Benchmark Time
Single standard order into a fresh book ~123 ns
Single iceberg order into a fresh book ~120 ns
Single post-only order into a fresh book ~119 ns
Single good-till-date order into a fresh book ~143 ns
Single pegged order into a fresh book ~90 ns
10k standard orders into a fresh book ~352 µs
10k iceberg orders into a fresh book ~355 µs
10k post-only orders into a fresh book ~354 µs
10k good-till-date orders into a fresh book ~371 µs
10k pegged orders into a fresh book ~284 µs

Amend

Benchmark Time
Single order quantity decrease ~811 ns
Single order quantity increase ~886 ns
Single order price update ~874 ns
10k orders quantity decrease ~190 µs
10k orders quantity increase ~511 µs
10k orders price update ~559 µs

Cancel

Benchmark Time
Single order cancel ~905 ns
10k orders cancel ~243 µs

Matching

Single-level standard book

Match volume Time
1 ~488 ns
10 ~497 ns
100 ~1.13 µs
1000 ~5.02 µs
10000 ~26.30 µs

Multi-level standard book

Match volume Time
1 ~726 ns
10 ~735 ns
100 ~1.39 µs
1000 ~5.38 µs
10000 ~26.57 µs

Mixed workload

Benchmark Time
Submit + amend + match + cancel ~14.4 µs

Notes

  • Benchmark results depend on CPU, compiler version, benchmark configuration, and system load.
  • These figures illustrate the general performance profile of the engine rather than serve as universal guarantees.
  • Full Criterion output includes confidence intervals and regression comparisons.

Next Steps

Additional Order Features

  • Stop orders
  • Last-trade peg reference

Potential Performance Improvements

Currently, the order book stores price levels using BTreeMap<Price, PriceLevel>. This design provides:

  • O(log N) best-price lookup
  • O(log N) submit / amend / cancel operations to locate the corresponding price level

where N is the number of price levels.

Several alternative designs may improve performance.

1. Slab-backed price levels

Use Slab<PriceLevel> and BTreeMap<Price, LevelIdx>, and each order holds its LevelIdx, allowing direct lookup of its price level. This would reduce the time complexity of the amend/cancel order operations to O(1), except when cancelling the order removes the price level entirely.

2. Sorted vector of price levels

Store price levels in Vec<PriceLevel>, sorted by price from worst → best.

Trade-offs:

  • O(1) best-price lookup
  • O(N) insertion / deletion when creating or removing price levels

However, in real-world trading scenarios, most activity occurs near the best price, meaning the effective search distance is often small. This can make a linear scan competitive with tree-based structures for typical workloads.

Makefile

The project uses a Makefile to simplify the development process.

See the Makefile for more details, or run make to see the available commands.

License

Licensed under either of

at your option.

Contribution

Contributions are welcome! If you would like to contribute, please follow these steps:

  1. Fork the repository
  2. Create a new branch for your changes
  3. Make your changes
  4. Run all the checks (make check)
  5. Submit a pull request

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in matchcore by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.