seasonal-calendar-effects

To generate trading signals from seasonal calendar effects, you process OHLCV datasets using pandas-based datetime indexing to map month-of-year and optional day-of-week regularities into long, short, or neutral position outputs.

Backtesting a "sell in May" seasonality strategy involves configuring specific bullish and bearish months on your OHLCV DataFrame to produce deterministic long, short, or flat signals for your historical market analysis.

Yes, you can combine month-of-year and day-of-week effects by applying a combined-confirmation rule that outputs a trade signal only when both the configured month and weekday conditions agree.

Yes, you need pandas and numpy because the signal generation engine requires pandas-based datetime indexing to process OHLCV time-series data and apply the configured seasonal calendar rules.

Using calendar regularities for backtesting treats temporal seasonality as the primary hypothesis, meaning it outputs deterministic signals based solely on configured dates without incorporating fundamental market analysis or external volatility drivers.

Yes, seasonal-calendar-effects works with any OHLCV dataset including crypto markets, applying configurable bullish and bearish month or weekday rules to generate deterministic time-series trading signals.