Individual skill
factor-research
Factor research framework with IC/IR analysis, quantile backtesting, and factor combination. Suitable for cross-sectional factor evaluation across multiple instruments.
agent/src/skills/factor-research/SKILL.md
Source commit a79afbdb048f873433fdb651a667e43dd0bb735d
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 37,948
- Repository last updated
- Nov 8, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- backtesting Repository topic
- multi-agent Repository topic
- quantitative-finance Repository topic
- trading Repository topic
- ai-agent Repository topic
- algorithmic-trading Repository topic
- fintech Repository topic
- llm Repository topic
- mcp Repository topic
- python Repository topic
- research Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 12,618 bytesSKILL.mdcore · 12,618 bytesSKILL.mdcore · 12,618 bytesSKILL.mdcore · 12,618 bytesreferences/combination.mddoc · 9,827 bytesreferences/neutralization.mddoc · 10,727 bytesreferences/research_contract.mddoc · 2,186 bytes
Read excerpt
# 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
