IPO share allocation

Implementing a fairness-aware IPO share allocation system in Python

This hard coding problem tests your ability to implement a multi-tiered allocation algorithm where priority rules interact with fairness constraints. It is representative of the kind of system-design and data-structure challenges that arise in trading infrastructure and market microstructure.

The core difficulty lies in correctly orchestrating three distinct allocation modes: single-bidder fills (straightforward), price-group round-robin (requires careful state management), and price tier sequencing (requires prioritisation). Strong solutions use appropriate data structures—such as heaps or sorted containers—to track price groups and iterate through bidders efficiently. You must also handle partial fills, bidder removal mid-cycle, and the termination conditions carefully to avoid off-by-one errors and logic bugs.

• Priority queue and heap operations for price-ordered processing
• Round-robin and fair-allocation patterns
• State mutation and bidder tracking across multiple passes
• Edge cases: empty bids, exhausted shares, zero-share recipients