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

Zoo Signal Engine (new in 0.1.8)

When the user wants to compose 1-N alphas drawn from the Alpha Zoo (450+ pre-built factors) into a multi-factor strategy, use ZooSignalEngine.from_zoo(...) from zoo_signal_engine.py instead of the old per-symbol example_signal_engine.py. The new engine operates on wide-panel dict[str, pd.DataFrame] inputs (the same shape the registry's Alpha.compute(panel) contract uses), redistributes weights when any alpha fails or is skipped, and supports long-only (top_n), short-only (bottom_n), and long-short (top_n + bottom_n) signal modes. It also exposes a generate(data_map) adapter so it drops straight into the existing run_backtest pipelines.

from src.factors.registry import Registry
from zoo_signal_engine import ZooSignalEngine

registry = Registry()
# Browse candidates with registry.list(theme="momentum") -- see the alpha-zoo skill.
alpha_ids = ["alpha101_001", "alpha101_012", "guotai_191_003"]
engine = ZooSignalEngine.from_zoo(alpha_ids, top_n=10, bottom_n=10, standardize=True)
# Feed into a panel-aware backtest, or via .generate(data_map) into the bundled engines.
signal_panel = engine.compute_signal(panel)  # DataFrame, same shape as panel["close"]

Cross-references:

• See the alpha-zoo skill for browsing the alpha catalogue, filtering by theme/universe, and inspecting __alpha_meta__ records.
• example_signal_engine.py is kept for legacy per-symbol workflows that compute factors directly from raw OHLCV; new code should prefer zoo_signal_engine.py so it benefits from the 450+ zoo alphas, registry-level NaN/inf guardrails, and per-alpha skip isolation.