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

1. Calculate factor values: compute factor exposures for each instrument on the cross-section, and output a factor CSV (index=date, columns=codes)
2. Calculate returns: compute each instrument's forward N-day return, and output a return CSV (same structure)
3. Call the factor_analysis tool: pass in the factor CSV, return CSV, and output directory
4. Interpret the results: judge factor validity based on IC/IR criteria and quantile backtest results
5. 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