multi-factor

Cross-sectional stock ranking combines multiple factors into a single comparable score. You calculate momentum, value, or quality factors, apply Z-score normalization, and aggregate them into a composite score to identify top opportunities across many instruments.

Cross-sectional standardization applies Z-score normalization to each factor across the entire stock universe. This ensures different factors are comparable before aggregation, allowing you to compute a deterministic composite score for ranking.

Yes, you can select TopN stocks from the composite scoring results and assign deterministic equal weights. This creates a simple long-only portfolio allocation across multiple instruments using pandas and numpy.

The multi-factor engine includes provisions for missing data during factor calculation and standardization. This ensures your cross-sectional composite scoring and TopN selection remain robust without dropping stocks prematurely.

Yes, the factor calculation supports computing momentum, value, quality, or other factors for a broad set of stocks. These factors are standardized and aggregated into a single composite score for cross-sectional ranking.

Equal-weight allocation assigns deterministic weights to TopN stocks without considering factor magnitude or volatility. This simple long-only approach ignores risk optimization, making it suitable for baseline cross-sectional ranking rather than advanced portfolio balancing.