def zscore(candles: np.ndarray,
period: int = 14,
matype: int = 0,
nbdev: float = 1,
devtype: int = 0,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
zScore
:param candles: np.ndarray
:param period: int - default: 14
:param matype: int - default: 0
:param nbdev: float - default: 1
:param devtype: int - default: 0
: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)
means = ma(source, period=period, matype=matype, sequential=True)
if devtype == 0:
sigmas = talib.STDDEV(source, period) * nbdev
elif devtype == 1:
sigmas = mean_ad(source, period, sequential=True) * nbdev
elif devtype == 2:
sigmas = median_ad(source, period, sequential=True) * nbdev
zScores = (source - means) / sigmas
return zScores if sequential else zScores[-1]
def vlma(candles: np.ndarray,
min_period: int = 5,
max_period: int = 50,
matype: int = 0,
devtype: int = 0,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Variable Length Moving Average
:param candles: np.ndarray
:param min_period: int - default: 5
:param max_period: int - default: 50
:param matype: int - default: 0
:param devtype: int - default: 0
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: float | np.ndarray
"""
# Accept normal array too.
if len(candles.shape) == 1:
source = candles
else:
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
mean = ma(source, period=max_period, matype=matype, sequential=True)
if devtype == 0:
stdDev = talib.STDDEV(source, max_period)
elif devtype == 1:
stdDev = mean_ad(source, max_period, sequential=True)
elif devtype == 2:
stdDev = median_ad(source, max_period, sequential=True)
a = mean - (1.75 * stdDev)
b = mean - (0.25 * stdDev)
c = mean + (0.25 * stdDev)
d = mean + (1.75 * stdDev)
res = vlma_fast(source, a, b, c, d, min_period, max_period)
return res if sequential else res[-1]
def devstop(candles: np.ndarray,
period: int = 20,
mult: float = 0,
devtype: int = 0,
direction: str = "long",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Kase Dev Stops
:param candles: np.ndarray
:param period: int - default: 20
:param mult: float - default: 0
:param devtype: int - default: 0
:param direction: str - default: long
:param sequential: bool - default: False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
high = candles[:, 3]
low = candles[:, 4]
AVTR = talib.SMA(talib.MAX(high, 2) - talib.MIN(low, 2), period)
if devtype == 0:
SD = talib.STDDEV(talib.MAX(high, 2) - talib.MIN(low, 2), period)
elif devtype == 1:
SD = mean_ad(talib.MAX(high, 2) - talib.MIN(low, 2),
period,
sequential=True)
elif devtype == 2:
SD = median_ad(talib.MAX(high, 2) - talib.MIN(low, 2),
period,
sequential=True)
if direction == "long":
res = talib.MAX(high - AVTR - mult * SD, period)
else:
res = talib.MIN(low + AVTR + mult * SD, period)
return res if sequential else res[-1]
def bollinger_bands_width(
candles: np.ndarray,
period: int = 20,
devup: float = 2,
devdn: float = 2,
matype: int = 0,
devtype: int = 0,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
BBW - Bollinger Bands Width - Bollinger Bands Bandwidth
:param candles: np.ndarray
:param period: int - default: 20
:param devup: float - default: 2
:param devdn: float - default: 2
:param matype: int - default: 0
:param devtype: int - default: 0
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: BollingerBands(upperband, middleband, lowerband)
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
if devtype == 0:
dev = talib.STDDEV(source, period)
elif devtype == 1:
dev = mean_ad(source, period, sequential=True)
elif devtype == 2:
dev = median_ad(source, period, sequential=True)
middlebands = ma(source, period=period, matype=matype, sequential=True)
upperbands = middlebands + devup * dev
lowerbands = middlebands - devdn * dev
if sequential:
return (upperbands - lowerbands) / middlebands
else:
return (upperbands[-1] - lowerbands[-1]) / middlebands[-1]
def rvi(candles: np.ndarray,
period: int = 10,
ma_len: int = 14,
matype: int = 1,
devtype: int = 0,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
RVI - Relative Volatility Index
:param candles: np.ndarray
:param period: int - default: 10
:param ma_len: int - default: 14
:param matype: int - default: 1
: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)
if devtype == 0:
dev = talib.STDDEV(source, period)
elif devtype == 1:
dev = mean_ad(source, period, sequential=True)
elif devtype == 2:
dev = median_ad(source, period, sequential=True)
diff = np.diff(source)
diff = same_length(source, diff)
up = np.nan_to_num(np.where(diff <= 0, 0, dev))
down = np.nan_to_num(np.where(diff > 0, 0, dev))
up_avg = ma(up, period=ma_len, matype=matype, sequential=True)
down_avg = ma(down, period=ma_len, matype=matype, sequential=True)
result = 100 * (up_avg / (up_avg + down_avg))
return result if sequential else result[-1]