Compounding Returns in Python

What this Python cumulative-returns question tests

This is an easy statistics and data-analysis question that probes whether you can correctly implement cumulative compounding in Python. It tests your understanding of how daily returns compound over time and which libraries and functions are most reliable for tracking strategy performance.

The question focuses on a core workflow in quantitative trading: given a time series of daily returns, building a cumulative growth curve that feeds into drawdown analysis and other performance metrics. Many candidates confuse additive aggregation with multiplicative compounding, or select library functions without understanding what they actually compute. Interviewers use this to filter for candidates who can confidently move from mathematical formula to working code.

• Arithmetic vs. geometric growth and the role of compounding
• NumPy and Pandas built-in functions for cumulative operations
• Relationship between returns, cumulative returns, and drawdown calculations