← Squarepoint Interview Insights
The single-transaction version I got pretty quickly.
Clarify the problem constraints (single transaction, buy before sell, return 0 if no profit) and then walk through a one-pass solution that tracks the minimum price seen so far and the maximum profit. Explain the time and space complexity, and mention edge cases like decreasing prices or empty array.
Pro tip: Emphasize that this is a classic 'buy low, sell high' problem where you only need to remember the lowest price so far, not the entire history. Mentioning that the algorithm runs in O(n) time and O(1) space demonstrates you understand the optimal solution.
Confirm that only one buy and one sell are allowed, the buy must occur before the sell, and that no profit means returning 0. Ask about edge cases like empty array or single element.
Briefly mention that a nested loop checking all pairs would be O(n^2), but it's not optimal. This shows you can start simple and then improve.
Initialize min_price to infinity and max_profit to 0. Iterate through prices, update min_price if current price is lower, else update max_profit if current price minus min_price is greater than max_profit.
State that the algorithm runs in O(n) time and O(1) space. Discuss edge cases: empty array returns 0, decreasing prices return 0, and single element returns 0.
Write clear pseudocode or a concise implementation in a language like Python to demonstrate the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part tripped me up for a moment because I started thinking about DP tables before realizing the greedy insight is almost embarrassingly simple.
Recognize this as the classic 'Best Time to Buy and Sell Stock II' problem where unlimited transactions are allowed. The optimal strategy is to sum all positive daily price differences, which is equivalent to capturing every upward movement. This yields an O(n) time and O(1) space solution.
Pro tip: Emphasize that the greedy approach works because any profitable trade can be decomposed into a series of consecutive daily gains, and holding through a dip would only reduce profit. Also, mention that this assumes no transaction costs; if costs exist, the problem becomes more complex.
Confirm that you can buy and sell multiple times but must sell before buying again, and that you start with no stock. Ask if there are any transaction fees or constraints.
Realize that to maximize profit, you should buy before every price increase and sell before every decrease. This is equivalent to summing all positive differences between consecutive days.
Explain that any optimal solution can be transformed into this greedy one without reducing profit, because each profitable trade can be broken into daily gains, and skipping a positive day would lose profit.
Write a simple loop that iterates through the price array, adding max(0, prices[i] - prices[i-1]) to the total profit. State that time complexity is O(n) and space is O(1).
Mention variations like transaction fees, cooldown periods, or limited transactions, and how they would change the approach (e.g., dynamic programming).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.