longbridge-seasonality
Seasonality and calendar-effect strategy via Longbridge Securities — uses historical OHLCV data to compute month-of-year returns (January Effect), day-of-week returns (Monday / Friday effect), pre/post-holiday drift, and earnings-season effect; identifies statistically significant patterns and generates trading signals. Triggers: "季节性", "日历效应", "月份效应", "周一效应", "年初效应", "节假日效应", "财报季效应", "时间模式", "季節性", "日曆效應", "月份效應", "周一效應", "年初效應", "節假日效應", "財報季效應", "seasonality", "calendar effect", "January effect", "day of week effect", "holiday effect", "earnings season effect", "seasonal pattern", "time se
npx skills add majiayu000/claude-skill-registry --skill longbridge-seasonality --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-seasonality Identifies calendar-driven return anomalies for a stock by analysing multi-year historical OHLCV data. Computes average returns grouped by month, day-of-week, and proximity to known events (holidays, earnings seasons) to surface statistically significant seasonal patterns. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use - User asks "does AAPL tend to rise in January?", "周一买还是周五买", "节假日前后涨跌规律", "NVDA 财报季行情", "月份效应", "seasonality analysis". ## Workflow 1. Fetch 5 years of daily candles (≈ 1260 trading days): `longbridge kline <SYMBOL> --period day --count 1260 --format json` 2. Compute daily log-returns from `close` column. 3. Group by: - **Month effect**: average return per calendar month (Jan–Dec); flag months with |avg| > 1 std of all monthly averages. - **Day-of-week effect**: parse `time` field for weekday; average return Mon–Fri; flag extremes. - **Holiday drift**: identify the 3 trading days before/after major holidays (Christmas, Chinese New Year, Golden Week for HK/CN); compute average drift window. - **Earnings season**: roughly Q1 (Jan–Feb), Q2 (Apr–May), Q3 (Jul–Aug), Q4
- When to use
- Workflow
- CLI
- Output
- Error handling
- MCP fallback
- Related skills
- File layout
longbridge kline --help longbridge kline <SYMBOL> --period day --count 1260 --format json
What does the longbridge-seasonality skill do?
Seasonality and calendar-effect strategy via Longbridge Securities — uses historical OHLCV data to compute month-of-year returns (January Effect), day-of-week returns (Monday / Friday effect), pre/post-holiday drift, and earnings-season effect; identifies statistically significant patterns and generates trading signals. Triggers: "季节性", "日历效应", "月份效应", "周一效应", "年初效应", "节假日效应", "财报季效应", "时间模式", "季節性", "日曆效應", "月份效應", "周一效應", "年初效應", "節假日效應", "財報季效應", "seasonality", "calendar effect", "January effect", "day of week effect", "holiday effect", "earnings season effect", "seasonal pattern", "time se
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-seasonality --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.