quantlib.constant package
Submodules
quantlib.constant.enum module
- class quantlib.constant.enum.LineMode(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases:
Enum- RESISTANCE = 0
- SUPPORT = 1
- class quantlib.constant.enum.OptimalSlopeMode(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases:
EnumSelection method for picking the optimal Resistance/Support curve
- Alt = 'Alt'
- Maj = 'Maj'
- class quantlib.constant.enum.RSSPSelectionMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases:
EnumSelection method for picking the optimal Resistance/Support curve
- MAX_DISTANCE = 'Max Distance'
- SMOOTH = 'Smooth'
- class quantlib.constant.enum.TimeInterval(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases:
Enum- FIFTEEN_MINUTE = 900
- FIVE_MINUTE = 300
- FOUR_HOUR = 18000
- ONE_DAY = 86400
- ONE_HOUR = 3600
- THIRTY_MINUTE = 1800
- static dict() dict[Type[TimeInterval], int]
Turn this Enum into a dict
- static infer_from_time_diff(diff_in_seconds: int, atol: float = 0) Type[TimeInterval]
refer to https://numpy.org/doc/stable/reference/generated/numpy.isclose.html about atol :param diff_in_seconds: :param atol: absolute tolerance :return: TimeInterval
- static infer_from_timestamps(timestamp1: datetime, timestamp2: datetime)