seasonal

Generate deterministic long, short, or neutral trade signals from OHLCV time series by applying configurable bullish and bearish month-of-year rules to your dataset. This approach systematically captures calendar effects without manual date filtering.

Yes, you can enable an optional weekday overlay to require dual confirmation from both month and weekday effects. This combined mode produces stronger, filtered trading signals across your daily or higher timeframe OHLCV series.

Backtest seasonal hypotheses like "sell in May" by defining bearish_months and bullish_months parameters. The tool converts these date rules into per-symbol pandas Series of 1, -1, or 0 for direct use in algorithmic strategy backtesting.

Yes, it requires inputs as pandas DataFrames indexed by a DatetimeIndex to accurately parse month and weekday calendar effects. The OHLCV dataset should be daily or higher timeframe to properly generate time-based trading signals.

A calendar effect is a seasonal market regularity, such as specific months or days of week exhibiting consistent price behavior. This tool captures these regularities to produce repeatable trade signals from historical OHLCV series for algorithmic strategies.

Yes, the signal generator supports multi-symbol datasets by producing per-symbol pandas Series outputs. This allows you to apply uniform bullish and bearish month rules across multiple assets for portfolio-level backtesting or live signal overlays.