multi-factor

name	multi-factor
description	Multi-factor cross-sectional stock ranking. Combines factor standardization, equal-weight or IC-weighted scoring, and TopN portfolio construction. Suitable for multi-instrument portfolio strategies.
category	strategy

Multi-Factor Cross-Sectional Stock Ranking

Purpose

On the same time cross-section, compute multiple factor values for many stocks, standardize them, combine them into a composite score, and select the top-ranked stocks to build a portfolio.

Signal Logic

1. Factor calculation: calculate N factors for each stock (such as momentum, value, and quality)
2. Cross-sectional standardization: standardize each factor on the cross-section with Z-score normalization (subtract mean, divide by standard deviation)
3. Composite scoring: sum the factors with equal weights (or custom weights) to obtain a composite score
4. Rank and select: go long the TopN names, with weight = 1/N for each

Built-In Factors

Factor Name	Calculation Method	Direction
momentum	Return over the past N days	Positive (higher is better)
reversal	Return over the past 5 days	Negative (lower is better)
volatility	Standard deviation of returns over the past N days	Negative (lower is better)
volume_ratio	Today's volume / N-day average volume	Positive

If extra_fields are available (China A-shares), you can also add:

• pe_factor: 1/PE (the larger, the cheaper)
• pb_factor: 1/PB
• roe_factor: ROE (the larger, the better)

Parameters

Parameter	Default	Description
momentum_window	20	Momentum lookback window
vol_window	20	Volatility lookback window
top_n	3	Number of selected stocks
rebalance_freq	20	Rebalancing frequency (trading days)

Common Pitfalls

• Cross-sectional standardization requires at least 3 stocks, otherwise Z-scores are meaningless
• Keep the previous signal unchanged between rebalance dates (do not rerank every day)
• Factors have different directions: momentum is positively sorted, volatility is negatively sorted, so directions must be aligned before standardization
• Portfolio weights must be normalized: each TopN stock gets 1/N, all others get 0

Dependencies

pip install pandas numpy

Signal Convention

• 1/N = selected into TopN (equal-weight long), 0 = not selected