| name | longbridge-seasonality |
| description | 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 series anomaly", "月度效应", "月度效應", "monthly seasonality".
|
| license | MIT |
| metadata | {"author":"longbridge","version":"1.0.0","risk_level":"read_only","requires_login":false,"default_install":true,"requires_mcp":false,"tier":"analysis"} |
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
- Fetch 5 years of daily candles (≈ 1260 trading days):
longbridge kline <SYMBOL> --period day --count 1260 --format json
- Compute daily log-returns from
close column.
- 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 (Oct–Nov) for US stocks; compute average return in those windows vs non-earnings months.
- Summarise each effect as: (a) average return, (b) win rate (% positive days), (c) signal direction (Bullish/Bearish/Neutral).
- Output a summary table + top-3 actionable patterns.
Run longbridge kline --help to confirm flag names before calling.
CLI
longbridge kline --help
longbridge kline <SYMBOL> --period day --count 1260 --format json
JSON rows: {time, open, high, low, close, volume}. Parse time for year/month/weekday grouping.
Output
| Effect | 简体 | 繁體 | English |
|---|
| Month effect | 月份效应 | 月份效應 | Month-of-year effect |
| Day-of-week | 星期效应 | 星期效應 | Day-of-week effect |
| Holiday drift | 节假日效应 | 節假日效應 | Holiday drift |
| Earnings season | 财报季效应 | 財報季效應 | Earnings season effect |
| Signal | 信号 | 訊號 | Signal |
Output: one table per effect (Month / DOW / Holiday / Earnings), then a "Top Patterns" section with concrete entry/exit rules. Cite Longbridge Securities / 数据来源：长桥证券 / 數據來源：長橋證券.
Error handling
| Situation | 简体回复 | 繁體回復 | English reply |
|---|
command not found: longbridge | 回退到 MCP 或提示安装 longbridge-terminal | 回退到 MCP 或提示安裝 longbridge-terminal | Fall back to MCP or install longbridge-terminal |
not logged in / unauthorized | 请运行 longbridge auth login | 請執行 longbridge auth login | Run longbridge auth login |
| Fewer than 250 candles returned | 数据不足以计算季节性，建议选择历史更长的标的 | 數據不足，建議選擇歷史更長的標的 | Insufficient data; choose a more liquid / longer-history symbol |
| Other stderr | 直接显示原始错误 | 直接顯示原始錯誤 | Surface verbatim |
MCP fallback
When the CLI is unavailable, fall back to the MCP server. Discover available tools from the MCP server's tool list at runtime.
Related skills
longbridge-kline — raw candle datalongbridge-calendar — forward earnings dates and holidayslongbridge-volatility-strategy — vol regime complement to seasonality
File layout
longbridge-seasonality/
└── SKILL.md
