def chaikin_adl(high: nda.NdType, low: nda.NdType, close: nda.NdType,
volume: nda.NdType) -> nda.NdType:
"""
Chaikin Accumulation Distribution Line
"""
return nda.nd_universal_adapter(chaikin_adl_np_1d,
(high, low, close, volume), ())
def ema(series: nda.NdType,
periods: int = 20,
warm_periods: tp.Union[int, None] = None) -> nda.NdType:
"""
Exponential moving average
"""
if warm_periods is None:
warm_periods = periods
return nda.nd_universal_adapter(ema_np_1d, (series, ), (
periods,
warm_periods,
))
def wma(series: nda.NdType,
weights: tp.Union[tp.List[float], np.ndarray] = None) -> nda.NdType:
"""
:param weights: weights in decreasing order. lwma(series, 3) == wma(series, [3,2,1])
"""
global last_alert
if (weights is None or type(weights) is int):
if time.time() - last_alert > 60:
last_alert = time.time()
log_err(
"Warning! wma(series:ndarray, periods:int) deprecated. Use lwma instead of wma."
)
return lwma(series, weights)
if type(weights) is list:
weights = np.array(weights, np.float64)
return nda.nd_universal_adapter(wma_np_1d, (series, ), (weights, ))
def shift(series: nda.NdType, periods: int = 1) -> nda.NdType:
return nda.nd_universal_adapter(shift_np_1d, (series,), (periods,))
def obv(close: nda.NdType, volume: nda.NdType) -> nda.NdType:
return nda.nd_universal_adapter(obv_np_1d, (close, volume), ())
def vwma(price: nda.NdType, volume: nda.NdType, periods: int = 20):
return nda.nd_universal_adapter(vwma_np_1d, (price, volume), (periods, ))
def lwma(series: nda.NdType, periods: int = 20):
return nda.nd_universal_adapter(lwma_np_1d, (series, ), (periods, ))
def stochastic_k(high: nda.NdType, low: nda.NdType, close: nda.NdType, periods: int = 14):
return nda.nd_universal_adapter(k_np_1d, (high, low, close), (periods,))
def covariance(x: nda.NdType, y: nda.NdType, periods: int = 1) -> nda.NdType:
return nda.nd_universal_adapter(covariance_np_1d, (x, y), (periods, ))
def variance(series: nda.NdType, periods: int = 1) -> nda.NdType:
return nda.nd_universal_adapter(variance_np_1d, (series, ), (periods, ))
def top_pivot_points(series: nda.NdType, periods: int = 5) -> nda.NdType:
return nda.nd_universal_adapter(top_pivot_points_np_1d, (series, ),
(periods, ))
