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Squarepoint Capital·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Squarepoint Capital data scientist interview with a coding focus. The question was finance-flavored but really just a dynamic programming problem dressed up in quant clothing.

Questions Asked (1)

Q1

Implement a function mdd(pnl) that takes a cumulative PnL series and returns the maximum drawdown as a negative value, along with the start and end indices of that drawdown period.

Algorithms & Data Structures
Author's notes

Looks like a finance question but it's basically the buy-and-sell stock problem with extra steps.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and edge cases, then implement a single-pass algorithm that tracks the running maximum and the maximum drawdown. Return the drawdown as a negative value along with the start and end indices of the drawdown period.

Pro tip: Emphasize the O(n) time and O(1) space complexity, and handle edge cases like empty series or no drawdown gracefully. Also, confirm whether the indices should be inclusive or exclusive.

1. Clarify requirements and edge cases

Ask about input format (e.g., list, pandas Series), expected output type, and how to handle edge cases like empty input or no drawdown.

2. Design the algorithm

Use a single pass to track the running maximum and the maximum drawdown. Update the drawdown when the current value falls below the running maximum.

3. Implement the function

Write clean code with appropriate variable names and comments. Ensure the function returns the drawdown as a negative value and the correct start and end indices.

4. Test with examples

Validate the function with simple cases (e.g., monotonically increasing, decreasing, with a clear drawdown) and edge cases (empty, single element).

5. Analyze complexity and discuss optimizations

State the time and space complexity (O(n) time, O(1) space) and mention any potential optimizations or alternative approaches.

Key Points to Mention

  • Definition of maximum drawdown: the largest peak-to-trough decline before a new peak is attained.
  • Single-pass algorithm: track running maximum and update max drawdown when a new low is reached.
  • Return format: negative value for drawdown, and start/end indices (inclusive or exclusive as specified).
  • Edge cases: empty input, no drawdown (return 0 or None), and handling of equal values.
  • Time and space complexity: O(n) time, O(1) space.
  • Potential pitfalls: off-by-one errors in indices, confusion between drawdown and maximum loss.

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