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Robinhood·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Robinhood coding screen, one question about processing a list of trades to extract buy/sell pairs and compute total profit. Pretty focused problem, nothing too wild on the surface but the pairing logic takes a minute to think through cleanly.

Questions Asked (1)

Q1

Given a list of trades, write a function that iterates through them to identify buy/sell pairs and calculate the overall profit.

Algorithms & Data Structures
Author's notes

Took me a beat to figure out how to track open positions while scanning through.

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AI HintsAI Generated

Suggested Approach

Clarify the problem first: define trade structure, whether short selling is allowed, and if multiple buys/sells can occur. Then propose an efficient algorithm, likely using a hash map to track open positions and compute profit on matching sells, and discuss time/space complexity.

Pro tip: Mention edge cases like unmatched trades, multiple buys before a sell, and negative profit scenarios. Also, relate the solution to real-world trading systems where order matching and profit calculation are critical.

1. Clarify Requirements

Ask about trade representation (e.g., fields like symbol, quantity, price, type), whether trades are per symbol or mixed, and if short selling is allowed. Confirm if profit is realized only when a sell matches a buy.

2. Choose Data Structures

Use a hash map to track open buy positions per symbol, storing quantity and price. For sells, match against buys using FIFO or average cost, depending on requirements.

3. Design Algorithm

Iterate through trades: for buys, add to open positions; for sells, reduce positions and calculate profit as (sell price - buy price) * matched quantity. Sum profits.

4. Handle Edge Cases

Consider sells without matching buys (short selling), partial fills, and multiple symbols. Decide how to handle unmatched trades (e.g., ignore or track separately).

5. Analyze Complexity

Time complexity O(n) for n trades, space O(m) for m open positions. Discuss potential optimizations if needed.

Key Points to Mention

  • Trade data structure: fields like symbol, quantity, price, type (buy/sell), timestamp.
  • Matching logic: FIFO vs. average cost method for calculating profit.
  • Handling multiple symbols: use a map keyed by symbol to track positions.
  • Edge cases: unmatched buys/sells, short selling, zero quantity, negative profit.
  • Time and space complexity: O(n) time, O(m) space where m is number of open positions.
  • Real-world relevance: order matching engines, portfolio accounting.

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