seasonal

You generate trading signals from calendar effects by applying month and day-of-week patterns to OHLCV time-series data. This produces discrete outputs of 1, -1, or 0, targeting recurring time-based market regularities directly from the input index.

Month and day-of-week effects are recurring calendar patterns in market data used to trigger long, short, or flat positions. They exploit seasonal blind spots where specific months or weekdays consistently show bullish or bearish behavior.

Yes, you can combine bullish months and weekdays for trading signal confirmation. The combined mode requires both the month and weekday directions to align before triggering a signal, providing extra validation for your trades.

Yes, this calendar effect signal generator requires pandas to process OHLCV time-series data. It applies across markets and timeframes, outputting signals aligned to your existing input index.

The discrete outputs 1, -1, and 0 in seasonal trading signals represent long, short, and flat positions respectively. These values are generated when OHLCV data matches configured bullish or bearish months and weekday patterns.

The best way to align seasonal signals with your time-series index for backtesting is to use a tool that outputs signals matched directly to the input index. This ensures the discrete 1, -1, and 0 values correspond exactly to your OHLCV timestamps.