longbridge-pairs-trading
Pairs trading / statistical-arbitrage strategy via Longbridge Securities — tests cointegration between two correlated assets using the Engle-Granger (ADF) method, computes the optimal hedge ratio via OLS, calculates spread Z-score, half-life of mean reversion, and generates entry/exit signals (long spread when Z > 2, short spread when Z < -2, exit when |Z| < 0.5). Triggers: "配对交易", "统计套利", "协整", "价差交易", "对价交易", "双股套利", "配對交易", "統計套利", "協整", "價差交易", "pairs trading", "statistical arbitrage", "cointegration", "spread trading", "mean reversion pairs", "hedge ratio", "half-life", "ADF test", "Kalma
npx skills add majiayu000/claude-skill-registry --skill longbridge-pairs-trading --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# longbridge-pairs-trading Statistical-arbitrage strategy for a pair of correlated securities. Tests for cointegration, estimates hedge ratio, computes spread Z-score, and outputs actionable long/short signals with half-life and position sizing guidance. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use - User provides two ticker symbols and asks for pairs trading analysis, spread mean-reversion, cointegration test, or statistical arbitrage. - Triggers: "MSFT 和 GOOGL 配对交易", "HSBC vs StanChart 协整", "pairs trade AAPL MSFT", "价差均值回归". ## Workflow 1. Fetch 252 daily candles for each symbol: ``` longbridge kline <SYMBOL_A> --period day --count 252 --format json longbridge kline <SYMBOL_B> --period day --count 252 --format json ``` 2. Align on `time`, drop unmatched rows (different trading calendars). 3. **Cointegration test (Engle-Granger)**: - OLS regress `ln(close_A)` on `ln(close_B)` → hedge ratio β - Compute residuals (spread) = `ln(close_A) − β × ln(close_B)` - Run ADF test on residuals; if p-value < 0.05, declare cointegrated 4. **Spread statistics**: - Spread mean μ, std σ - Z-score = (spread_current − μ)
- When to use
- Workflow
- CLI
- Output
- Error handling
- MCP fallback
- Related skills
- File layout
longbridge kline --help longbridge kline <SYMBOL_A> --period day --count 252 --format json longbridge kline <SYMBOL_B> --period day --count 252 --format json
What does the longbridge-pairs-trading skill do?
Pairs trading / statistical-arbitrage strategy via Longbridge Securities — tests cointegration between two correlated assets using the Engle-Granger (ADF) method, computes the optimal hedge ratio via OLS, calculates spread Z-score, half-life of mean reversion, and generates entry/exit signals (long spread when Z > 2, short spread when Z < -2, exit when |Z| < 0.5). Triggers: "配对交易", "统计套利", "协整", "价差交易", "对价交易", "双股套利", "配對交易", "統計套利", "協整", "價差交易", "pairs trading", "statistical arbitrage", "cointegration", "spread trading", "mean reversion pairs", "hedge ratio", "half-life", "ADF test", "Kalma
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-pairs-trading --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.