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
- Factor calculation: calculate N factors for each stock (such as momentum, value, and quality)
- Cross-sectional standardization: standardize each factor on the cross-section with Z-score normalization (subtract mean, divide by standard deviation)
- Composite scoring: sum the factors with equal weights (or custom weights) to obtain a composite score
- 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/PBroe_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
bashpip 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.
pythonfrom 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-zooskill for browsing the alpha catalogue, filtering bytheme/universe, and inspecting__alpha_meta__records. example_signal_engine.pyis kept for legacy per-symbol workflows that compute factors directly from raw OHLCV; new code should preferzoo_signal_engine.pyso it benefits from the 450+ zoo alphas, registry-level NaN/inf guardrails, and per-alpha skip isolation.
