factor-research

name factor-research description 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. weight_i = |IC_mean_i| / sum(|IC_mean_j|) Composite factor = sum(weight_i * Z(factor_i)) Orthogonalized Combination 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. Artifact Placement When factor_analysis runs inside a backtest or swarm run, set output_dir to <run_dir>/artifacts/factor/<factor_name>/ so the Run Detail Factor tab can render the results. Use one subdirectory per factor (for example artifacts/factor/momentum_20d/ ), and keep the three standard output files ( ic_series.csv , ic_summary.json , group_equity.csv ) together inside it. Artifacts written elsewhere under artifacts/ are still discovered by the recursive scan, but the canonical layout keeps runs comparable.