Agent skill

longbridge-factor-research

Factor research framework for evaluating single-factor effectiveness across A-shares, HK, and US stocks — information coefficient (IC), information ratio (IR), decile portfolio backtests, and IC decay (serial autocorrelation). Triggers: "因子研究", "IC分析", "信息比率", "分层回测", "因子有效性", "单因子测试", "因子衰减", "因子评估", "IC分析", "信息比率", "分層回測", "因子有效性", "單因子測試", "factor research", "information coefficient", "IC", "IR information ratio", "factor backtest", "decile portfolio", "factor decay", "factor effectiveness".

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill longbridge-factor-research --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-factor-research/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# longbridge-factor-research A systematic framework for testing whether a quantitative factor adds predictive value for future returns — covering IC analysis, information ratio, decile portfolio construction, and factor decay. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use - *"帮我分析 PE 因子的 IC"*, *"test IC for the PE factor on A-shares"* - *"动量因子有效吗"*, *"is momentum factor effective on HK stocks"* - *"做个分层回测"*, *"run a decile portfolio backtest"* - *"这个因子多少期后失效"*, *"how many periods until this factor decays"* - *"IC 序列自相关怎么算"*, *"calculate IC serial autocorrelation"* For multi-factor screening (not research), use `longbridge-factor-screen`. For ML-based strategies, use `longbridge-ml-strategy`. ## Workflow ### Step 1 — Define the factor Clarify with the user: - Factor name and calculation (e.g. trailing-12M PE, 1M price momentum, ROE YoY change). - Universe: index constituent (e.g. CSI 300, HSI, S&P 500) or custom list. - Test period (e.g. 2020-01-01 to 2024-12-31). - Holding period (e.g. monthly rebalance). ### Step 2 — Fetch universe constituents ```bash longbridge constituent --help longbridge constitue

What's inside
Steps it walks through
  1. When to use
  2. Workflow
  3. Step 1 — Define the factor
  4. Step 2 — Fetch universe constituents
  5. Step 3 — Fetch factor values and returns
  6. Step 4 — Compute IC at each rebalance date
  7. Step 5 — Summary statistics
  8. Step 6 — Decile portfolio backtest
  9. Step 7 — IC decay analysis
  10. CLI
  11. Output
  12. Error handling
  13. Related skills
  14. File layout
Ships with 1 file
  • metadata.json
Commands it runs
longbridge constituent --help
longbridge constituent <INDEX> --format json
longbridge calc-index <SYMBOL> --format json   # valuation, growth metrics
longbridge kline <SYMBOL> --period day --count 252 --format json   # price history for returns
longbridge calc-index --help
longbridge kline --help
longbridge calc-index <SYMBOL> --format json
longbridge kline <SYMBOL> --period day --count 252 --format json
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About this skill
What does the longbridge-factor-research skill do?

Factor research framework for evaluating single-factor effectiveness across A-shares, HK, and US stocks — information coefficient (IC), information ratio (IR), decile portfolio backtests, and IC decay (serial autocorrelation). Triggers: "因子研究", "IC分析", "信息比率", "分层回测", "因子有效性", "单因子测试", "因子衰减", "因子评估", "IC分析", "信息比率", "分層回測", "因子有效性", "單因子測試", "factor research", "information coefficient", "IC", "IR information ratio", "factor backtest", "decile portfolio", "factor decay", "factor effectiveness".

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-factor-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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