Retool·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Retool SWE interview with a coding problem that looks like a standard stock trading question until you read it more carefully. The forced-trade constraint is what makes it interesting, since you can't just skip bad days.

Questions Asked (1)

Given an array of daily stock prices, implement two functions: one that finds the maximum profit from exactly one buy-sell transaction (including the least-loss scenario if prices only fall), and another that does the same for exactly two non-overlapping transactions. You must always perform the required number of trades, even if that means taking a loss.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The 'exactly one transaction' part I got pretty fast.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, especially the requirement to always perform the exact number of trades even at a loss. Then, for the single transaction, use a one-pass approach tracking the minimum price and maximum profit (or least loss). For two transactions, use dynamic programming with states representing the number of transactions completed and whether holding a stock, or use a divide-and-conquer approach with prefix and suffix profit arrays.

Pro tip: Mention that the two-transaction problem can be solved in O(n) time and O(1) space using state machine DP, which is optimal and elegant. Also, explicitly handle the case where prices are strictly decreasing by initializing the profit to negative infinity and updating with the least loss.

1. Clarify requirements and edge cases

Confirm that exactly one and exactly two transactions are required, even if it results in a loss. Discuss edge cases: empty array, single price, strictly increasing/decreasing prices.

2. Design single transaction solution

Use a one-pass algorithm: track the minimum price seen so far and compute the profit if selling today. Initialize max profit to negative infinity to handle the least-loss scenario.

3. Design two transaction solution

Use dynamic programming with states: after first buy, after first sell, after second buy, after second sell. Update these states in one pass, ensuring non-overlapping transactions.

4. Analyze complexity and trade-offs

Explain that both solutions run in O(n) time and O(1) space. Compare with alternative approaches like prefix/suffix arrays (O(n) space) and discuss why the DP approach is more space-efficient.

5. Test with examples

Walk through examples: increasing prices, decreasing prices, and mixed. Verify that the functions return the correct profit or least loss for exactly the required number of trades.

Key Points to Mention

Handling the least-loss scenario by initializing profit to negative infinity and updating with the maximum (least negative) value.
The importance of non-overlapping transactions and how the DP states enforce this.
Time and space complexity: O(n) time and O(1) space for both functions.
Edge cases: empty array, single element, strictly decreasing prices, and the requirement to always perform the exact number of trades.
Alternative approaches: prefix/suffix arrays for two transactions, and why they use O(n) space.
State machine DP formulation for the two-transaction problem, with states representing holding or not holding a stock after each transaction.

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