longbridge-seasonality

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

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 (Oct–Nov) for US stocks; compute average return in those windows vs non-earnings months.
4. Summarise each effect as: (a) average return, (b) win rate (% positive days), (c) signal direction (Bullish/Bearish/Neutral).
5. Output a summary table + top-3 actionable patterns.

Run longbridge kline --help to confirm flag names before calling.

CLI

longbridge kline --help

# 5-year daily history
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 data
• longbridge-calendar — forward earnings dates and holidays
• longbridge-volatility-strategy — vol regime complement to seasonality

File layout

longbridge-seasonality/
└── SKILL.md