Start by clarifying requirements and constraints (asset class, latency targets, throughput, regulatory environment) to scope the design. Then walk through the system end-to-end, highlighting key components, data flow, and critical design decisions at each stage. Emphasize trade-offs between latency, reliability, and complexity, and how you would validate and monitor the system.
Pro tip: Show awareness that in HFT, microseconds matter but correctness and risk controls are non-negotiable; mention specific techniques like kernel bypass, FPGA, and lock-free data structures, and how you'd measure and optimize tail latency.
Ask about asset classes, expected message rates, latency targets (e.g., sub-microsecond), regulatory requirements (MiFID II, Reg NMS), and existing infrastructure. This ensures the design is appropriately scoped and demonstrates thoroughness.
Outline the major components: market data ingestion, strategy engine, order management, risk checks, execution, and post-trade settlement. Describe the data flow from feed handlers to order gateways, emphasizing the critical path.
Detail techniques for minimizing latency: kernel bypass (DPDK, Solarflare), FPGA for feed parsing, lock-free queues, busy polling, and colocation. Discuss trade-offs between latency and flexibility.
Explain pre-trade risk checks (position limits, credit checks, fat-finger checks) and post-trade compliance (audit trails, reporting). Emphasize that risk controls must be inline and not compromise latency.
Describe how to ensure high availability (redundancy, failover), monitor system health (latency percentiles, throughput), and test (simulation, backtesting, chaos engineering). Mention the importance of deterministic behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: which exchanges, data formats, latency constraints, and consistency needs. Then outline a pipeline that ingests raw feeds, normalizes them into a canonical schema, and reconstructs the order book with careful handling of out-of-order and missing data. Emphasize trade-offs between latency, accuracy, and complexity, and mention how you would validate correctness.
Pro tip: Demonstrate awareness of real-world exchange quirks like sequence gaps, snapshot vs incremental updates, and clock synchronization issues—showing you understand that perfect reconstruction is often impossible, so you design for graceful degradation and reconciliation.
Ask about the number of exchanges, expected message rates, latency requirements, and consistency guarantees. This shapes the architecture and trade-offs.
Define a canonical order book schema (e.g., price levels, order IDs, timestamps) and map each exchange's format to it. Handle differences in field names, data types, and update semantics.
Use exchange-provided sequence numbers and timestamps to order updates. Implement buffering and reordering for out-of-order messages, and detect gaps to trigger snapshot recovery.
Apply updates to the book in sequence, handling add/modify/delete operations. Periodically reconcile with snapshots to correct drift and handle missed messages.
Implement checks like crossed books, negative spreads, and checksum comparisons. Monitor latency and error rates, and have fallback mechanisms for data quality issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining latency and throughput and explaining how they trade off in a trading system, then describe how to identify the hot path and isolate the control plane. Use a concrete example like order matching to illustrate design decisions and metrics.
Pro tip: Quantify trade-offs with numbers (e.g., microseconds vs. millions of messages per second) and mention that the control plane should never block the hot path—use asynchronous communication and separate resources.
Clarify that latency is the time to process a single request, while throughput is the number of requests processed per unit time. Explain that optimizing one often degrades the other due to queuing, batching, and resource contention.
Describe the hot path as the critical sequence of operations executed for every request, such as order validation, matching, and execution. Emphasize that it must be highly optimized, often using lock-free data structures and avoiding dynamic memory allocation.
Explain that the control plane handles configuration, monitoring, and orchestration, which are not latency-sensitive. It should be decoupled from the hot path via asynchronous messaging or shared memory with careful synchronization to avoid interference.
Discuss specific trade-offs: batching increases throughput but adds latency; caching reduces latency but may stale data; replication improves throughput but adds coordination overhead. Relate these to the hot path and control plane separation.
Suggest a concrete design: e.g., hot path uses a ring buffer and busy-spin, control plane runs on separate cores with message passing. Define metrics like p99 latency and messages per second to validate the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting part of the conversation for me.
Start by emphasizing defense-in-depth: multiple independent layers that prevent live execution, such as separate environments, network isolation, and mandatory dry-run modes. Then describe a concrete architecture with sandboxed execution, simulated market data, and strict access controls. Finally, discuss how to validate safety through chaos testing and audit trails.
Pro tip: Mention that you would use a 'dead man's switch' or circuit breaker that halts all trading if the environment is misconfigured, and that you would regularly test the safety mechanisms themselves with red-team exercises.
Use physically or logically separate environments (e.g., separate VPCs, accounts, or clusters) for testing and production, with no direct network routes between them. Ensure test environments have no credentials or API keys that can access live markets.
Provide realistic but synthetic market data feeds, or replay historical data, to test strategies without connecting to live exchanges. Use a mock exchange that mimics order placement and fills but never sends real orders.
Implement a mandatory dry-run mode that logs intended orders instead of executing them. Add kill switches at multiple levels (strategy, account, firm-wide) that can be triggered manually or automatically on anomaly detection.
Restrict who can deploy to test environments and require multi-party approval for any changes that could affect live trading. Log all actions and maintain immutable audit trails for compliance and debugging.
Regularly test the isolation and safety controls through penetration testing and chaos engineering. Ensure that even if a strategy is accidentally deployed to production, it cannot execute live trades without explicit approval.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.