Unit tests

Thousands of focused tests validate individual functions, data types, models, and calculations. Both Python and Rust have dedicated unit test suites covering parsing, serialization, arithmetic precision, and state transitions.

Integration tests

Test component interactions: data engine to message bus, execution engine to risk engine, cache updates, and adapter lifecycle. Verify that subsystems compose correctly under realistic message flows.

Acceptance tests

End-to-end scenarios exercise full trading workflows: strategy signal generation, order submission, fill processing, position management, and portfolio updates. Deterministic reruns confirm reproducibility across runs.

Property-based tests

Using proptest in Rust to generate randomized inputs and verify invariants hold across thousands of edge cases. Covers order book invariants, backoff logic, value types, timers, throttlers, instruments, retry logic, and network behavior.

Deterministic simulation

The network crate uses turmoil for deterministic simulation of reconnects, partitions, and adverse network conditions. Tests run under controlled fault injection to verify recovery and resilience at the transport layer.

Performance tests

Rust benchmarks target critical hot paths including order matching, message bus throughput, and data serialization. Continuous measurement guards against performance regressions in latency-sensitive code.

Memory safety

A dedicated leak-testing suite uses tracemalloc, RSS monitoring, and memray to detect memory leaks across the Python-Rust boundary. 19 test files run targeted scenarios to verify stable long-running memory behavior.