def pvi(candles: np.ndarray, source_type: str = "close", sequential: bool = False) -> Union[float, np.ndarray]:
"""
PVI - Positive Volume Index
:param candles: np.ndarray
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
res = ti.pvi(np.ascontiguousarray(source), np.ascontiguousarray(candles[:, 5]))
return same_length(candles, res) if sequential else res[-1]
def pvi(candles: np.ndarray,
source_type="close",
sequential=False) -> Union[float, np.ndarray]:
"""
PVI - Positive Volume Index
:param candles: np.ndarray
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
source = get_candle_source(candles, source_type=source_type)
res = ti.pvi(np.ascontiguousarray(source),
np.ascontiguousarray(candles[:, 5]))
return np.concatenate(
(np.full((candles.shape[0] - res.shape[0]), np.nan),
res), axis=0) if sequential else res[-1]
def compute(self, data_dict):
close = data_dict.get('close')
volume = data_dict.get('volume')
return ti.pvi(close, volume)