Generate trading signals from month-of-year and day-of-week market patterns in OHLCV data.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill seasonal-hxhyyy
Please help me install this Agent Skill. Skill: seasonal Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/seasonal Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill seasonal-hxhyyy
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 the BTC-USDT pair using the default bullish and bearish month configurations.
High-intent search queries and answers about installing and using this skill.
You can generate trading signals from calendar patterns by applying vector-based calculations to datetime-indexed OHLCV market data to identify month-of-year and day-of-week statistical anomalies. This approach detects recurring bullish or bearish windows based on historical time regularities.
Yes, you can automate a Sell in May strategy or capture the Spring Rally by applying seasonal filters to your OHLCV data. The process identifies specific bullish and bearish month configurations and generates trading signals when those calendar windows occur.
You can backtest combined trading signals by using dual-confirmation logic that triggers entries only when both monthly and weekly calendar patterns align. This requires running vector-based signal calculations on datetime-indexed market data using pandas and numpy.
Yes, generating seasonal trading signals requires both pandas and numpy to perform vector-based signal calculations on datetime-indexed financial time-series data. These dependencies handle the data manipulation and numerical analysis needed to detect calendar anomalies.
The best way to detect day-of-week effects is to run statistical analysis on datetime-indexed OHLCV data to identify recurring behavioral anomalies. This process isolates specific weekdays that historically show bullish or bearish tendencies and converts them into actionable trading signals.
You should avoid relying solely on seasonal signals when market conditions deviate from historical norms or when structural market changes invalidate past calendar patterns. These statistical anomalies identify historical regularities but do not guarantee future performance in volatile markets.
0xZKnwGenerate trading signals from historical month-of-year and day-of-week effects.
hxhyyyAnalyze crypto-derivatives markets for funding rates, term structures, and volatility surfaces.
HKUDSGenerate long, short, or neutral trading signals from OHLCV data using month and weekday patterns.
hung-phanGenerate classical time-series features like lags and rolling windows.
