Factor research framework with IC/IR analysis, quantile backtesting, and factor combination. Suitable for cross-sectional factor evaluation across multiple instruments.
Factor research framework with IC/IR analysis, quantile backtesting, and factor combination. Suitable for cross-sectional factor evaluation across multiple instruments.
category
analysis
Factor Research Framework
Purpose
Systematically evaluates the predictive power of single or multiple factors. Uses IC/IR statistical tests and quantile backtests to determine whether a factor has stock-selection power, and to guide factor screening and combination.
Applicable scenarios:
Single-factor validity testing (momentum, value, quality, volatility, and more)
Determining weights for multi-factor combination
Factor decay analysis (IC changes across different holding periods)
Comparing factor differences across industries and markets
Workflow
Calculate factor values: compute factor exposures for each instrument on the cross-section, and output a factor CSV (index=date, columns=codes)
Calculate returns: compute each instrument's forward N-day return, and output a return CSV (same structure)
Call the factor_analysis tool: pass in the factor CSV, return CSV, and output directory
Interpret the results: judge factor validity based on IC/IR criteria and quantile backtest results
Factor screening / combination: keep effective factors and combine them with equal weights or IC-based weights
Key point: the rows (dates) and columns (instrument codes) of the factor CSV and return CSV must align exactly. Returns must be forward returns after the factor-observation date (to avoid look-ahead bias).
factor_analysis Tool Parameters
Parameter
Type
Required
Default
Description
factor_csv
string
Yes
-
Path to the factor-value CSV
return_csv
string
Yes
-
Path to the return CSV
output_dir
string
Yes
-
Output directory for results
n_groups
integer
No
5
Number of quantile groups
Output Files
File
Contents
ic_series.csv
Daily IC series
ic_summary.json
IC mean, IC standard deviation, IR, proportion of IC > 0
group_equity.csv
Cumulative equity curves for each quantile group
IC/IR Interpretation Standards
Metric
Threshold
Interpretation
IC mean
> 0.03
Factor has basic predictive power
IC mean
> 0.05
Factor has strong predictive power
IC mean
> 0.10
Unusually high; check for look-ahead bias
IR (IC mean / IC std)
> 0.5
Factor is stably effective
IR
> 1.0
Extremely strong, very rare
Proportion of IC > 0
> 55%
Factor direction is stable
Proportion of IC > 0
< 50%
Factor direction is unstable and unusable
Note: negative IC can also be useful (reverse factors). Judge by absolute value, and reverse the signal direction in actual use.
Quantile Backtest Interpretation
Quantile backtesting sorts instruments into N groups by factor value from low to high (default 5 groups), with equal-weight holding inside each group.
Criteria:
Monotonicity: the final net values from Group_1 to Group_N should show a monotonic rising (or falling) pattern. Better monotonicity means stronger factor discrimination
Long-short spread: the net-value difference between the highest and lowest group (long_short_spread). A larger spread means stronger selection power
Nonlinearity: if only the top and bottom groups differ materially while the middle groups are similar, the factor may only be effective in the tails
Stability: group equity curves should be smooth; sharp swings indicate an unstable factor
Warning signs:
No meaningful difference across group equity curves → the factor is ineffective
Non-monotonic pattern (such as V-shape or inverted V-shape) → the factor may have a nonlinear relationship and requires further analysis
One group's net value falls persistently → the factor may be usable in reverse
Factor Combination Methods
When multiple single factors pass validity tests, they should be combined into a composite factor:
Equal-Weight Combination
The simplest method: standardize each factor and sum them with equal weights. Suitable when the factor count is small and IC differences are minor.
Composite factor = Z(factor1) + Z(factor2) + ... + Z(factorN)
where Z() is cross-sectional Z-score standardization
IC-Weighted Combination
Assign weights according to historical IC mean. Factors with higher IC receive larger weights.
First orthogonalize the factors with the Schmidt process to remove collinearity, then combine them with equal weights. Suitable when factors are highly correlated with one another.
1. Sort factors by IC from high to low
2. Keep the first factor unchanged
3. Regress each later factor on all previous factors and use the residual as the orthogonalized factor
4. Combine the orthogonalized factors with equal weights
Common Pitfalls
Look-Ahead Bias
Factor values must be computed using data from day T and earlier, while returns must use data from T+1 to T+N
Wrong example: calculate the factor with day T closing price and correlate it with day T return → artificially inflated IC
Correct approach: factor value at day T, return defined as the move from the T close to the T+1 close and beyond
Skewed Factor Distributions
Some factors (such as market cap and turnover) have heavily right-skewed distributions
Computing IC directly from raw values makes the result dominated by outliers
Solution: apply cross-sectional rank or Z-score standardization before computing IC
Industry Neutralization
Factor values can be highly similar within the same industry, causing stock selection to cluster in a few sectors
Solution: perform Z-score standardization within each industry (industry neutralization) to remove industry effects
For China A-shares, Shenwan Level-1 industries can be used
Insufficient Sample Size
Each cross-section should contain at least 5 valid instruments to compute meaningful IC
Quantile backtests require at least n_groups instruments
When the universe is too small, IC is noisy and IR becomes unreliable
Factor Crowding
Classic factors (momentum, value) may see diminished excess returns after becoming widely used
Regularly inspect the time-series evolution of factor IC to see whether decay is occurring
Consider factor innovation or factor timing
Survivorship Bias
Backtesting only on stocks that still survive today will overestimate factor performance
Use full-sample data including delisted stocks
Dependencies
pip install pandas numpy scipy
Calling Zoo Factors
Rather than recompute factors from raw OHLCV every research iteration, prefer reusing the 450+ pre-built alphas in the Alpha Zoo registry. Each alpha is metadata-validated (AlphaMeta schema with theme, universe, columns_required, decay_horizon, min_warmup_bars), shape-checked against panel["close"], and rejected if it emits +/- inf or >95% NaN — so the factor CSV you feed to factor_analysis is already sanity-checked.
from src.factors.registry import Registry
registry = Registry()
ids = registry.list(theme="momentum", universe="equity_cn") # filter the catalogue
factor_panel = registry.compute("alpha101_001", panel) # wide DataFrame, same shape as panel["close"]
factor_panel.to_csv("factor_alpha101_001.csv") # ready for factor_analysis tool
For combining several validated alphas into one composite signal, see the multi-factor skill's ZooSignalEngine (it z-scores, weights, and ranks alphas for you, with per-alpha skip isolation). For browsing the catalogue and inspecting individual __alpha_meta__ records, see the alpha-zoo skill.
