def test_trange():
candles = np.array(mama_candles)
single = ta.trange(candles)
seq = ta.trange(candles, sequential=True)
assert round(single, 2) == 94.35
assert len(seq) == len(candles)
assert seq[-1] == single
def zlkc(candles: np.ndarray,
period: int = 20,
mult: float = 1,
source_type="hlc3",
sequential=False) -> ZeroLagKeltnerChannel:
"""
Zero Lag Keltner Channels
Based on TradingView: https://www.tradingview.com/script/CTzNAuUH-Zero-Lag-Keltner-Channels/
:param candles: np.ndarray
:param period: int - default: 20
:param mult: float - default: 1
:param source_type: str - default: "hlc3"
:param sequential: bool - default=False
:return: ZeroLagKeltnerChannel(upperband, middleband, lowerband)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
source = get_candle_source(candles, source_type=source_type)
ma = cta.zlema(source, period=period, sequential=True)
tr = ta.trange(candles=candles, sequential=True)
rangema = cta.zlema(tr, period=period, sequential=True)
upper = ma + rangema * mult
lower = ma - rangema * mult
if sequential:
return ZeroLagKeltnerChannel(upper, ma, lower)
else:
return ZeroLagKeltnerChannel(upper[-1], ma[-1], lower[-1])
def greed(self):
tr = ta.trange(candles=self.candles, sequential=True)
core = tr[np.logical_not(np.isnan(tr))]
vwma_tr = ti.vwma(np.ascontiguousarray(core),
np.ascontiguousarray(self.candles[:, 5]),
period=200)
mult = 2
return (same_length(self.candles[:, 5], vwma_tr) / self.candles[:, 2] *
100 * mult)[-1]
