def fisher(candles: np.ndarray, period=9, sequential=False) -> FisherTransform:
"""
The Fisher Transform helps identify price reversals.
:param candles: np.ndarray
:param period: int - default: 9
:param sequential: bool - default=False
:return: FisherTransform(fisher, signal)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
fisher, fisher_signal = ti.fisher(np.ascontiguousarray(candles[:, 3]),
np.ascontiguousarray(candles[:, 4]),
period=period)
if sequential:
return FisherTransform(
np.concatenate((np.full(
(candles.shape[0] - fisher.shape[0]), np.nan), fisher),
axis=0),
np.concatenate(
(np.full((candles.shape[0] - fisher_signal.shape[0]),
np.nan), fisher_signal)))
else:
return FisherTransform(fisher[-1], fisher_signal[-1])
def fisher(candles: np.ndarray, period: int = 9, sequential: bool = False) -> FisherTransform:
"""
The Fisher Transform helps identify price reversals.
:param candles: np.ndarray
:param period: int - default: 9
:param sequential: bool - default: False
:return: FisherTransform(fisher, signal)
"""
candles = slice_candles(candles, sequential)
fisher_val, fisher_signal = ti.fisher(np.ascontiguousarray(candles[:, 3]), np.ascontiguousarray(candles[:, 4]),
period=period)
if sequential:
return FisherTransform(same_length(candles, fisher_val), same_length(candles, fisher_signal))
else:
return FisherTransform(fisher_val[-1], fisher_signal[-1])