Pythonic return computation

Computing forward-looking returns in pandas

This question tests your fluency with pandas Series manipulation, a core skill in quantitative finance where you routinely prepare training data for predictive models. When building machine-learning systems to forecast returns, you need to align your features (today's price data) with your target (tomorrow's or next-period's return), which requires careful shifting of time-indexed data.

The question probes whether you know the standard pandas method for time-shifting a Series so that future values "appear" in earlier rows—a operation fundamental to avoiding look-ahead bias and constructing properly-aligned supervised-learning datasets. This is a practical, low-friction check that you think clearly about data alignment and can reach for the right tool without hesitation.

• Time-series alignment and resampling in pandas
• Forward-fill vs. backward-fill semantics
• Avoiding lookahead bias in machine-learning pipelines