Alphalens Reloaded

Alphalens begins with a factor value for each date and asset, plus pricing data. Its get_clean_factor_and_forward_returns function aligns those inputs and calculates later returns. Pass the prepared table to create_full_tear_sheet to examine the factor through several diagnostics.

Choose the return horizons and either quantile buckets or value-based bins. Sector mappings allow group comparisons; by_group displays separate reports, while group_neutral changes the analysis to account for groups. The information coefficient measures the rank relationship between factor values and subsequent returns. Turnover reports show how frequently the selected groups change.

Timing deserves careful attention. Prices must reflect when the signal could actually be acted on and extend beyond the factor period far enough to calculate future returns. The API documentation also warns that its optional forward-return outlier filter introduces look-ahead bias; filter_zscore=None disables that filter.

Install alphalens-reloaded in a compatible local Python environment and start with the example notebooks. No hosted account is required. Reloaded retains the established factor-analysis workflow; the maintainers place new feature work in ML4T Diagnostic. Check the selected package release's Python and pandas requirements before upgrading an existing environment.