Two Sigma·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Two Sigma coding round, one meaty simulation problem about IPO order matching. The core idea isn't that complex but the tie-breaking rule inside same-price groups is where they really want to see if you're paying attention.

Questions Asked (1)

Given a list of buy orders (each with a price, timestamp, and quantity) and a fixed share supply, simulate IPO order matching: fill higher bids first, and within the same price tier, distribute shares round-robin across orders sorted by timestamp. Output the final allocation per order.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the high-level structure pretty fast.

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AI HintsAI Generated

Suggested Approach

Clarify the matching rules and edge cases, then outline an efficient algorithm using sorting and a round-robin pointer. Walk through a small example to validate the logic, and discuss time/space complexity and potential optimizations.

Pro tip: Explicitly state your assumptions about tie-breaking and rounding, and mention how you would handle large inputs or streaming data to show production-level thinking.

1. Clarify requirements and edge cases

Ask about tie-breaking rules, whether partial fills are allowed, and how to handle insufficient supply. Confirm output format and any constraints.

2. Design the algorithm

Sort orders by price descending and timestamp ascending. Group by price tier, then allocate shares round-robin within each tier until supply is exhausted.

3. Implement and test with an example

Code the solution, using a queue or circular list for round-robin. Trace through a small example to verify correctness and edge cases.

4. Analyze complexity and trade-offs

Discuss time complexity (O(n log n) due to sorting) and space complexity. Mention alternative approaches like priority queues or bucket sort for price tiers.

5. Optimize and extend

Suggest optimizations for large datasets, such as streaming allocation or parallel processing, and discuss how to handle dynamic updates.

Key Points to Mention

Sorting orders by price descending and timestamp ascending
Round-robin allocation within same price tier
Handling partial fills and remaining supply
Time and space complexity analysis
Edge cases: empty list, zero supply, all orders filled
Potential optimizations for scalability

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.