Generate trading signals from month-of-year and day-of-week effects in OHLCV datasets.
npx skills add https://github.com/20YN04/vibe-trading-macos --skill seasonal-20yn04
Please help me install this Agent Skill. Skill: seasonal Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/seasonal Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill seasonal-20yn04
This Skill addresses the difficulty of identifying and acting upon recurring time-based market regularities, such as month-of-year or day-of-week effects, which are often overlooked in manual trading.
Use the seasonal skill to generate trading signals for BTC-USDT using the default calendar effect parameters.
High-intent search queries and answers about installing and using this skill.
You can generate trading signals from calendar effects by applying vectorized date-based calculations to OHLCV data to identify statistical bullish or bearish windows based on month-of-year and day-of-week regularities.
Seasonal market patterns are time-based regularities like month-of-year and day-of-week effects. They identify historical bullish or bearish windows to help flag potential long positions during strong months and avoid historically weak periods.
Yes, you can combine weekday and monthly trends for dual-confirmation trading signals by overlaying weekday effects onto monthly trends to create combined signal logic for automated strategy execution.
Yes, generating seasonal signals requires pandas and numpy to perform the vectorized date-based calculations and filtering needed to identify time-based market regularities in financial datasets.
You can backtest a sell-in-May strategy by processing OHLCV financial datasets to detect statistical bearish windows and automatically flag potential long or short positions for automated strategy execution based on those calendar effects.
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