Voleon·Software Engineer·Onsite - System Design / Architecture·Senior

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Apr 2026

Summary

Voleon put me through a system design round focused on trading infrastructure, specifically building out order management and strategy logic on top of a market data feed. Pretty deep into the weeds for a single question, they basically wanted a full design with real constraints around participation rate caps and rolling volume windows.

Questions Asked (1)

Design and implement the trading logic for a single client sitting on top of a market data feed, including an OrderManager, a Strategy class, and a rolling-window volume tracker to enforce a participation rate cap.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This question is basically three problems duct-taped together and they want you to see the seams clearly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline the high-level architecture with clear separation of concerns: market data feed, strategy, order manager, and volume tracker. Dive into the design of each component, emphasizing the rolling-window volume tracker and participation rate enforcement, and discuss trade-offs and edge cases.

Pro tip: Demonstrate awareness of real-world trading constraints like latency, partial fills, and market impact, and propose a simple, testable design first before optimizing.

1. Clarify Requirements and Constraints

Ask about the market data feed format, order types, participation rate definition (e.g., percentage of volume over a window), and any latency or throughput requirements.

2. High-Level Architecture

Sketch the main components: a market data handler, a Strategy that consumes data and generates signals, an OrderManager that manages order lifecycle, and a VolumeTracker that maintains rolling volume.

3. Design Rolling-Window Volume Tracker

Choose a data structure (e.g., circular buffer or deque) to efficiently track volume over a sliding time window, and discuss how to handle out-of-order or missing data.

4. Implement Participation Rate Cap

Explain how the Strategy uses the VolumeTracker to compute the allowed order size (e.g., participation rate * window volume - already traded volume) and how the OrderManager enforces it.

5. Discuss Trade-offs and Edge Cases

Address trade-offs like window size vs. responsiveness, handling partial fills, order cancellation, and synchronization between components.

Key Points to Mention

Separation of concerns: Strategy generates signals, OrderManager handles order execution, VolumeTracker maintains volume data.
Rolling window implementation: use a time-based sliding window with efficient data structures (e.g., deque) and consider timestamping.
Participation rate calculation: dynamically compute remaining allowed volume based on current window volume and already executed volume.
Order management: support order types (market, limit), track order state, handle partial fills and cancellations.
Concurrency and latency: ensure thread-safe access to shared data and minimize latency in hot path.
Testing and simulation: backtest with historical data and simulate market impact to validate logic.

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