factor-research
Factor Research systematically validates whether factors predict future returns using information coefficient and information ratio metrics, plus quantile-based backtests across instrument groups. Apply it to momentum, value, quality, volatility, or custom factors—then combine validated signals with equal or IC-weighted averaging.
Factor Research evaluates single or multiple factors' predictive power using IC/IR statistical tests and quantile backtesting.
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Install
HKUDS/Vibe-Trading/factor-research · repository language: Python
git clone https://github.com/HKUDS/Vibe-Trading
cp -r Vibe-Trading/agent/src/skills/factor-research ~/.claude/skills/factor-researchnpx skillfed install HKUDS/Vibe-Trading/factor-researchFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does factor-research test if a stock factor works?
Factor Research evaluates predictive power using information coefficient (IC) and information ratio (IR) tests. These metrics quantify whether a factor's cross-sectional rankings correlate with forward returns. The skill also runs quantile backtests to assess monotonicity—checking whether higher factor quintiles consistently outperform lower ones across your chosen holding period and instrument universe.
What's the process for factor analysis IC IR backtest in Factor Research?
Factor Research calculates IC (correlation between factor values and forward returns) and IR (IC divided by its standard deviation) to measure statistical significance. It then stratifies your universe into quantile groups and generates equity curves for each quintile. This reveals whether factor signals decay over time and whether performance remains stable across sectors or asset classes.
How does Factor Research combine multiple factors into a composite signal?
Factor Research lets you combine validated factors using equal-weight or IC-weighted averaging. After individually testing each factor's predictive power, you specify weights—either uniform across all factors or proportional to each factor's information coefficient. The skill then merges them into a single composite ranking for portfolio construction.
How can Factor Research detect look-ahead bias and survivorship bias?
Factor Research flags common pitfalls during backtest setup. Look-ahead bias checks ensure factor values are known before the signal date. Survivorship bias detection reminds you to include delisted securities in your historical universe. The skill guides you to use point-in-time data and handles corporate actions so results reflect realistic trading conditions.
What does Factor Research reveal about factor decay and holding periods?
Factor Research analyzes how factor predictive power changes across different holding periods—from 1-day to multi-month horizons. It also segments performance by sector or industry to show whether factors remain stable or decay faster in certain segments. This helps you choose optimal rebalance frequencies and identify crowded or deteriorating signals.
Is Factor Research open-source and what license does it use?
Yes, Factor Research is released under the MIT license, making it free to use, modify, and distribute for commercial and personal projects. The MIT license imposes minimal restrictions, requiring only that you retain the original copyright notice in any copies or derivative works you create.
SKILL.md
rendered from the published skill — quoted content, verbatim
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_analysistool: pass in the factor CSV, return CSV, and output directory - **Interpret the
(truncated - see the full file via the links below)
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agent/src/skills/factor-research/SKILL.md