labeling/eventfilters_map.py

from .event_filters.nofilter import NoEventFilter
from .event_filters.cusum import CUSUMVolatilityEventFilter, CUSUMFixedEventFilter

eventfilters_map = dict(
    none = NoEventFilter(),
    cusum_vol = CUSUMVolatilityEventFilter(vol_period = 20),
    cusum_fixed = CUSUMFixedEventFilter(threshold = 500)
)
feat(Events): added EventFilter, EventLabeller (#186 ) 2022-01-26 23:22:43 +01:00			`from .event_filters.nofilter import NoEventFilter`
			`from .event_filters.cusum import CUSUMVolatilityEventFilter, CUSUMFixedEventFilter`

			`eventfilters_map = dict(`
			`none = NoEventFilter(),`
			`cusum_vol = CUSUMVolatilityEventFilter(vol_period = 20),`
feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192 ) 2022-02-17 16:36:35 +01:00			`cusum_fixed = CUSUMFixedEventFilter(threshold = 500)`
feat(Events): added EventFilter, EventLabeller (#186 ) 2022-01-26 23:22:43 +01:00			`)`