The core algorithm isn't too bad once you realize you can just capture every upward slope.
First clarify the problem constraints: whether multiple non-overlapping transactions are allowed, if short selling is permitted, and the expected output format. Then propose an efficient algorithm, such as a greedy approach that captures every upward price movement, and explain how to track transactions to return the structured tuples. Finally, analyze time and space complexity and discuss edge cases.
Pro tip: Emphasize that the optimal strategy is to buy at every local minimum and sell at the next local maximum, which simplifies implementation and yields O(n) time. Mention that this approach is equivalent to summing all positive daily returns, a key insight that impresses interviewers.
Ask whether multiple non-overlapping transactions are allowed, if short selling is permitted, and confirm the exact output format (e.g., list of tuples with buy day, sell day, buy price, sell price, profit).
Explain that you will iterate through the price array, identifying every upward trend: buy at a local minimum and sell at the subsequent local maximum. This captures all profitable opportunities without overlapping.
Describe how to record each transaction: when a price increase is detected, note the buy day and price; when the price starts to decrease, close the transaction by recording the sell day, price, and profit. Append the tuple to the result list.
State that the algorithm runs in O(n) time and O(1) extra space (excluding output). Discuss edge cases: empty array, single day, strictly decreasing prices (no transactions), and strictly increasing prices (one transaction).
Walk through a small example, such as [7,1,5,3,6,4], to demonstrate the algorithm and verify the output tuples. Optionally, mention that the total profit equals the sum of all positive daily differences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the constraints on the input prices list (e.g., non-empty, all positive numbers, within a reasonable range). Then, design a decorator that checks these constraints before calling the wrapped function, raising an appropriate exception if validation fails. Finally, discuss how to apply the decorator to the transaction-finding function and consider edge cases and performance implications.
Pro tip: Mention that decorators should be transparent: use functools.wraps to preserve the original function's metadata, and ensure validation errors are clear and actionable for debugging. Also, consider whether validation should be configurable or reusable across multiple functions.
Ask the interviewer to specify the exact constraints on the prices list, such as non-empty, all positive numbers, sorted order, or maximum length. This ensures your solution meets the requirements.
Outline a decorator that takes a function as input and returns a wrapper. Inside the wrapper, validate the prices argument against the constraints before calling the original function.
Write the validation checks, raising ValueError or TypeError with descriptive messages if any constraint is violated. Consider using a helper function for reusability.
Apply the decorator to the transaction-finding function using @ syntax. Test with valid and invalid inputs to ensure correct behavior and error handling.
Talk about performance overhead of validation, whether to validate only in debug mode, and how to make the decorator configurable for different constraint sets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the input format and expected output, including edge cases like empty arrays or no profitable transactions. Then, outline an algorithm that computes per-transaction profits and formats them into a readable string, ensuring the total profit is correctly summed. Finally, discuss time and space complexity and potential optimizations.
Pro tip: Demonstrate awareness of real-world data issues by mentioning how you'd handle invalid or missing prices, and show that you can write clean, maintainable code with clear separation between computation and formatting.
Ask about the input format (e.g., array of prices, possibly with timestamps) and the desired output format. Identify edge cases such as empty array, single price, or no profitable transactions.
Choose an approach to compute per-transaction profits. For example, if transactions are buy-sell pairs, iterate through the array to calculate profit for each pair. If it's a stock trading problem, decide between single vs. multiple transactions.
Write code that builds a string summarizing each transaction (e.g., 'Buy at X, Sell at Y: Profit Z') and appends the total profit. Use efficient string concatenation or a list of strings.
Walk through examples, including edge cases, to ensure correctness. Verify that the total profit matches the sum of individual profits and that the output is human-readable.
State the time and space complexity (likely O(n) time, O(1) extra space if only total is needed, or O(n) space for the report). Mention potential optimizations or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.