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Point72 Asset Management·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate
Apr 2026

Summary

Point72 gave me a 30-minute coding task for a Data Engineer role, basically a portfolio trading optimizer built around a classic buy/sell stock problem but with extra structure they wanted around it. More Python-specific than I expected for a data engineering screen.

Questions Asked (3)

Q1

Implement a Python decorator called 'validate_prices' that checks a stock prices array against defined input constraints before processing.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I don't write decorators from scratch that often so I had to think for a second about the wrapper function structure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input constraints (e.g., non-empty list, numeric values, positive prices) and the desired behavior on violation (raise exception vs. return error). Then implement a decorator that wraps the target function, validates the prices argument before calling it, and preserves metadata using functools.wraps. Finally, discuss trade-offs like performance overhead and flexibility for different constraint sets.

Pro tip: Mention that validation should be configurable (e.g., via decorator arguments) to avoid hardcoding constraints, and highlight the importance of clear error messages for debugging in production trading systems.

1. Clarify Requirements

Ask about the expected input format (list, numpy array, etc.), specific constraints (e.g., all positive, no NaN, length > 0), and how violations should be handled (raise ValueError, log warning, etc.).

2. Design Decorator Signature

Decide if the decorator takes arguments (e.g., @validate_prices(min_price=0)) or not. Plan to use functools.wraps to preserve the wrapped function's metadata.

3. Implement Validation Logic

Inside the wrapper, extract the prices argument (by position or keyword), check each constraint, and raise an appropriate exception with a descriptive message if any fail.

4. Handle Edge Cases

Consider empty lists, non-numeric types, negative values, and ensure the decorator works with functions that have different signatures (e.g., prices as first arg or keyword).

5. Discuss Trade-offs

Talk about performance overhead of validation, especially for large arrays, and suggest alternatives like validating once at data ingestion or using type hints with runtime checks.

Key Points to Mention

  • Use of functools.wraps to preserve function metadata
  • Configurable constraints via decorator arguments for reusability
  • Clear and specific error messages for debugging
  • Handling of edge cases: empty list, non-numeric, NaN, negative prices
  • Performance considerations: validation overhead vs. safety
  • Integration with existing codebase and testing strategy

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

Q2

Write a function 'find_transactions' that takes a stock prices array and returns a list of tuples containing buy day, buy price, sell day, sell price, and profit for each transaction to maximize total profit.

Algorithms & Data Structures
Author's notes

This is the core of the problem and it's basically the greedy approach from the classic multi-transaction stock problem.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., single vs. multiple transactions, whether overlapping transactions are allowed) and then propose a greedy algorithm that captures every upward price movement. Implement the solution by iterating through the array, buying at local minima and selling at subsequent local maxima, recording each transaction's details.

Pro tip: Explicitly state your assumptions about the problem (e.g., unlimited transactions, no shorting) and discuss how the solution would change if constraints were different—this shows you think about edge cases and business context.

1. Clarify Requirements

Ask whether multiple transactions are allowed, if they can overlap, and if there are any constraints like transaction fees or a maximum number of transactions.

2. Choose Algorithm

Select a greedy approach that buys at every valley and sells at the next peak to maximize profit, or dynamic programming if constraints require it.

3. Implement Logic

Iterate through the price array, tracking buy day/price and sell day/price, and append a tuple for each completed transaction.

4. Handle Edge Cases

Consider empty arrays, decreasing prices, and flat prices; ensure the function returns an empty list or appropriate transactions.

5. Test and Validate

Walk through a few examples (e.g., [7,1,5,3,6,4]) to verify the output and total profit, and discuss time/space complexity.

Key Points to Mention

  • Greedy algorithm for maximizing profit with unlimited transactions
  • Time complexity O(n) and space complexity O(1) for the greedy approach
  • Handling edge cases such as empty input or monotonically decreasing prices
  • Comparison with dynamic programming for constrained scenarios (e.g., limited transactions)
  • Recording transactions as tuples (buy day, buy price, sell day, sell price, profit)
  • Potential follow-up: how to adapt if overlapping transactions are not allowed

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

Q3

Build a 'generate_report' function that prints a formatted summary of all transactions and the total profit.

Algorithms & Data Structures
Author's notes

Easiest part.

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AI HintsAI Generated

Suggested Approach

First clarify the input format and expected output, then design a clean function that iterates through transactions, accumulates profit, and formats the report. Emphasize modularity, edge case handling, and testability.

Pro tip: Mention that you would separate the report generation logic from the data processing to make it reusable and testable, and discuss how you would handle large datasets or streaming data if needed.

1. Clarify Requirements

Ask about the transaction data structure, expected output format, and any constraints (e.g., currency, rounding, sorting).

2. Design Function Signature

Define the function parameters and return type, considering whether to return a string or print directly, and how to handle empty input.

3. Implement Core Logic

Iterate through transactions, compute total profit, and build the formatted summary string with appropriate headers and alignment.

4. Handle Edge Cases

Address empty transaction lists, negative profits, floating-point precision, and large numbers.

5. Test and Validate

Write unit tests for typical and edge cases, and verify the output format matches expectations.

Key Points to Mention

  • Input validation and handling of missing or malformed data
  • Efficient computation of total profit (e.g., using sum with a generator)
  • String formatting techniques for alignment and readability (e.g., f-strings, format specifiers)
  • Separation of concerns: data processing vs. presentation
  • Consideration of floating-point precision for financial calculations
  • Extensibility: allowing different report formats or additional metrics

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