def vidya(candles: np.ndarray,
short_period: int = 2,
long_period: int = 5,
alpha: float = 0.2,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
VIDYA - Variable Index Dynamic Average
:param candles: np.ndarray
:param short_period: int - default: 2
:param long_period: int - default: 5
:param alpha: float - default: 0.2
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
if len(candles.shape) == 1:
source = candles
else:
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
res = ti.vidya(np.ascontiguousarray(source),
short_period=short_period,
long_period=long_period,
alpha=alpha)
return same_length(candles, res) if sequential else res[-1]
def vidya(candles: np.ndarray,
short_period: int = 2,
long_period: int = 5,
alpha: float = 0.2,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
VIDYA - Variable Index Dynamic Average
:param candles: np.ndarray
:param short_period: int - default: 2
:param long_period: int - default: 5
:param alpha: float - default: 0.2
: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.vidya(np.ascontiguousarray(source),
short_period=short_period,
long_period=long_period,
alpha=alpha)
return np.concatenate(
(np.full((candles.shape[0] - res.shape[0]), np.nan),
res), axis=0) if sequential else res[-1]