seasonal

To generate trading signals from calendar effects, you apply month-of-year and day-of-week patterns to OHLCV data, flagging bullish or bearish periods. The seasonal skill computes these indices using pandas and numpy, returning numerical signals like 1, -1, or 0.

Yes, you can use pandas to detect month-of-year patterns for crypto assets. The skill processes any OHLCV dataset to identify calendar effects, allowing you to apply month-based bullish or bearish signals to equities, crypto, or forex data.

You combine day-of-week and month-of-year signals for double confirmation by applying a weekday overlay to your monthly signals. The skill allows you to customize combine modes on OHLCV data to output confirmed 1, -1, or 0 trading signals.

Yes, you need numpy and pandas installed to calculate seasonal trading indicators. The skill explicitly relies on these libraries to compute month and weekday indices from OHLCV datasets and generate the resulting 1, -1, or 0 trading signals.

The best way to automate 'sell in May' signals for portfolio screening is to define bearish months within your OHLCV dataset. The skill flags these calendar-driven periods automatically, returning a -1 signal to indicate the bearish seasonal trend.

A limitation of using calendar effects for trading signal generation is that it strictly relies on historical time-of-year patterns rather than live market dynamics. Signals are generated purely from month and weekday indices, returning static 1, -1, or 0 values without accounting for current volatility.