This took me embarrassingly long to understand what was even being asked.
Start by clarifying requirements and constraints, then propose a modular architecture with components for market data, order management, and trade execution. Walk through the algorithm for calculating participation rate and generating trade prints, and discuss trade-offs around latency, accuracy, and scalability.
Pro tip: Emphasize the importance of using the most recent trade price and previous minute's volume, and discuss how to handle edge cases like missing data or market close. Show awareness of real-time constraints and potential bottlenecks.
Ask about expected order volume, latency requirements, data sources, and whether the system is for backtesting or live trading. Confirm that participation rate is based on previous minute's volume and that trades are printed at the most recent trade price.
Outline main components: a market data handler that ingests trades and computes per-minute volume, an order manager that tracks orders and their participation rates, and an execution engine that generates trade prints. Consider using a message queue for scalability.
Detail the algorithm: for each order, at each minute, calculate the target volume as participation rate times previous minute's volume. Then, as trades occur, fill the order up to the target, printing trades at the latest price. Discuss how to handle partial fills and order completion.
Choose appropriate data structures: a time-series store for volume per minute, a priority queue or list for orders, and thread-safe structures for concurrent access. Discuss locking or lock-free approaches to handle high throughput.
Discuss trade-offs: latency vs. accuracy, simplicity vs. scalability. Cover edge cases: missing volume data, market halts, order cancellation, and ensuring fairness across orders. Mention monitoring and logging for debugging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.