multi-factor

You standardize multiple financial factors for cross-sectional stock ranking by applying Z-score normalization to ensure factors with different scales are comparable, then combining them into a single composite score to rank assets.

You build a long-short portfolio using composite alpha signals by calculating and combining multiple factors with custom or equal weighting, then automatically selecting the TopN and BottomN assets based on the resulting standardized cross-sectional ranking.

Yes, you can process wide-panel data for systematic portfolio construction using pandas and numpy, as the multi-factor framework relies on these dependencies to handle robust signal generation and cross-sectional asset ranking.

The best way to combine momentum and volatility factors for a long-only trading strategy is to apply cross-sectional standardization to each factor, merge them into a composite score, and select the TopN performing assets for your portfolio.

Yes, cross-sectional Z-score normalization works for factors with completely different scales because it transforms the distributions to a common scale, making disparate alpha signals directly comparable for composite scoring.