Generate trading signals from historical month-of-year and day-of-week effects.
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill seasonal-0xzknw
Please help me install this Agent Skill. Skill: seasonal Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/seasonal Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill seasonal-0xzknw
This Skill addresses the difficulty of identifying and executing trades based on recurring calendar-based market regularities, such as seasonal rallies or day-of-week effects, which are often overlooked in manual analysis.
Use the seasonal skill to generate trading signals for BTC-USDT using the default bullish and bearish month configurations.
High-intent search queries and answers about installing and using this skill.
You generate trading signals by applying this Skill to OHLCV financial data to identify statistical seasonal trends using historical month-of-year and day-of-week regularities.
A calendar effect in market analysis refers to recurring time-based regularities, such as seasonal rallies or day-of-week effects, used to identify historical bullish or bearish trading windows.
Yes, the Skill supports combined signal logic for dual-confirmation strategies where both month-of-year and day-of-week signals must align to trigger a trade.
Yes, you need pandas and numpy to process OHLCV time-series data and compute signal arrays for backtesting seasonal trends and calendar effects.
You automate a strategy avoiding the Sell in May period by configuring bearish month parameters to identify and bypass historically underperforming seasonal windows in OHLCV time-series data.
hxhyyyGenerate trading signals from month-of-year and day-of-week market patterns in OHLCV data.
mahmoud20138Backtest trading strategies on OHLCV data and compute performance metrics.
HKUDSGenerate long, short, or neutral trading signals from OHLCV data using month and weekday patterns.
santoosaraujoGenerate trading signals from month-of-year and day-of-week effects in OHLCV data.
