Generate long, short, or neutral trading signals from OHLCV data using month and weekday patterns.
npx skills add https://github.com/HKUDS/Vibe-Trading --skill seasonal
Please help me install this Agent Skill. Skill: seasonal Source: https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/seasonal Command: npx skills add https://github.com/HKUDS/Vibe-Trading --skill seasonal
Detecting time-based return patterns can be tedious when scanning through calendared data, so this Skill turns the monthly and weekday seasonality of financial instruments into clear long, short, or neutral signals across any OHLCV series.
Generate seasonal buy and sell signals from the provided OHLCV data using month and weekday confirmations.
High-intent search queries and answers about installing and using this skill.
To generate seasonal trading signals, feed your OHLCV data with a datetime index into the processor, which maps months and weekdays to bullish and bearish flags, combining them for dual confirmation long or short positions.
The sell-in-May calendar effect is a seasonal pattern identifying historical market weakness during specific months. This strategy flags these bearish months to signal when to stand aside or short positions.
Yes, you can use pandas and numpy with datetime indexed OHLCV data to map months and weekdays to bullish and bearish flags, identifying seasonal patterns and generating combined trading signals.
Apply an optional weekday overlay alongside monthly seasonal rules so positions open only when both time dimensions agree on direction, reducing noise and providing dual confirmation for trading signals.
Yes, the strategy works with China A-share index OHLCV data by identifying calendar effects such as January to March strength and May to October weakness, providing timing cues for backtesting trading signals.
Calendar effect strategies rely purely on time-based seasonal patterns and do not account for fundamental market shifts, meaning generated trading signals may lag during unprecedented volatility outside historical month or weekday norms.
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