multi-factor

To build a cross-sectional stock ranking model, you combine normalized technical and fundamental factors like momentum and volatility into a composite score. This process uses Z-score standardization per factor and sums them to produce unified portfolio signals for TopN selection.

Z-score standardization normalizes diverse factors like momentum, reversal, and volume ratio onto a comparable scale. It prevents high-magnitude factors from dominating the composite score, ensuring equal contribution when summing them into a unified cross-sectional ranking signal.

You calculate momentum and reversal signals in pandas by computing historical returns over lookback windows. These technical inputs are then standardized via Z-score and combined with volatility and volume ratios to rank stocks across the cross-sectional universe.

Yes, you can apply equal-weight TopN selection and manage rebalance frequency in Python. The approach assigns equal long weights to the highest-scoring securities and reuses previous signals between rebalance dates, such as a 20-day cycle, to prevent portfolio churn.

Yes, this multi-factor ranking approach works for China A-share equities. You can score cross-sectional holdings using momentum, reversal, volatility, and volume inputs, optionally adding fundamental factors like 1/PE or ROE when available for the universe.

Equal-weight TopN selection limits portfolio signals by ignoring factor magnitude differences, assigning identical weights to all selected stocks. It relies on fixed rebalance intervals like 20 days to maintain stable exposures, which may lag during sudden market reversals or volatility spikes.