def trix(close_arr, n):
"""Calculate TRIX for given data.
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: trix indicator in cudf.Series
"""
EX1 = Ewm(n, close_arr).mean()
EX2 = Ewm(n, EX1).mean()
EX3 = Ewm(n, EX2).mean()
return rate_of_change(cudf.Series(EX3), 2)
def chaikin_oscillator(high_arr, low_arr, close_arr, volume_arr, n1, n2):
"""Calculate Chaikin Oscillator for given data.
:param high_arr: high price of the bar, expect series from cudf
:param low_arr: low price of the bar, expect series from cudf
:param close_arr: close price of the bar, expect series from cudf
:param volume_arr: volume the bar, expect series from cudf
:param n1: n1 time steps
:param n2: n2 time steps
:return: Chaikin Oscillator indicator in cudf.Series
"""
ad = (2.0 * close_arr - high_arr - low_arr) / (high_arr -
low_arr) * volume_arr
Chaikin = cudf.Series(Ewm(n1, ad).mean()) - cudf.Series(Ewm(n2, ad).mean())
return Chaikin
def mass_index(high_arr, low_arr, n1, n2):
"""Calculate the Mass Index for given data.
:param high_arr: high price of the bar, expect series from cudf
:param low_arr: low price of the bar, expect series from cudf
:param n1: n1 time steps
:param n1: n2 time steps
:return: Mass Index in cudf.Series
"""
Range = high_arr - low_arr
EX1 = Ewm(n1, Range).mean()
EX2 = Ewm(n1, EX1).mean()
Mass = division(EX1, EX2)
MassI = Rolling(n2, Mass).sum()
return cudf.Series(MassI)
def true_strength_index(close_arr, r, s):
"""Calculate True Strength Index (TSI) for given data.
:param close_arr: close price of the bar, expect series from cudf
:param r: r time steps
:param s: s time steps
:return: True Strength Index in cudf.Series
"""
M = diff(close_arr, 1)
aM = abs_arr(M)
EMA1 = Ewm(r, M).mean()
aEMA1 = Ewm(r, aM).mean()
EMA2 = Ewm(s, EMA1).mean()
aEMA2 = Ewm(s, aEMA1).mean()
TSI = division(EMA2, aEMA2)
return cudf.Series(TSI)
def relative_strength_index(high_arr, low_arr, n):
"""Calculate Relative Strength Index(RSI) for given data.
:param high_arr: high price of the bar, expect series from cudf
:param low_arr: low price of the bar, expect series from cudf
:param n: time steps to do EWM average
:return: Relative Strength Index in cudf.Series
"""
UpI, DoI = upDownMove(high_arr.to_gpu_array(), low_arr.to_gpu_array())
UpI_s = shift(UpI, 1)
UpI_s[0] = 0
DoI_s = shift(DoI, 1)
DoI_s[0] = 0
PosDI = Ewm(n, UpI_s).mean()
NegDI = Ewm(n, DoI_s).mean()
RSI = division(PosDI, summation(PosDI, NegDI))
return cudf.Series(RSI, nan_as_null=False)
def exponential_moving_average(close_arr, n):
"""Calculate the exponential weighted moving average for the given data.
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: expoential weighted moving average in cu.Series
"""
EMA = Ewm(n, close_arr).mean()
return cudf.Series(EMA)
def macd(close_arr, n_fast, n_slow):
"""Calculate MACD, MACD Signal and MACD difference
:param close_arr: close price of the bar, expect series from cudf
:param n_fast: fast time steps
:param n_slow: slow time steps
:return: MACD MACDsign MACDdiff
"""
EMAfast = Ewm(n_fast, close_arr).mean()
EMAslow = Ewm(n_slow, close_arr).mean()
MACD = substract(EMAfast, EMAslow)
average_window = 9
MACDsign = Ewm(average_window, MACD).mean()
MACDdiff = substract(MACD, MACDsign)
out = collections.namedtuple('MACD', 'MACD MACDsign MACDdiff')
return out(MACD=cudf.Series(MACD),
MACDsign=cudf.Series(MACDsign),
MACDdiff=cudf.Series(MACDdiff))
def stochastic_oscillator_d(high_arr, low_arr, close_arr, n):
"""Calculate stochastic oscillator D for given data.
:param high_arr: high price of the bar, expect series from cudf
:param low_arr: low price of the bar, expect series from cudf
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: stochastic oscillator D in cudf.Series
"""
SOk = stochastic_oscillator_k(high_arr, low_arr, close_arr)
SOd = Ewm(n, SOk).mean()
return cudf.Series(SOd)
def average_directional_movement_index(high_arr, low_arr, close_arr, n, n_ADX):
"""Calculate the Average Directional Movement Index for given data.
:param high_arr: high price of the bar, expect series from cudf
:param low_arr: low price of the bar, expect series from cudf
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps to do EWM average
:param n_ADX: time steps to do EWM average of ADX
:return: Average Directional Movement Index in cudf.Series
"""
UpI, DoI = upDownMove(high_arr.to_gpu_array(), low_arr.to_gpu_array())
last_ele = len(high_arr) - 1
tr = true_range(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
ATR = Ewm(n, tr).mean()
PosDI = division(Ewm(n, UpI).mean(), ATR)
NegDI = division(Ewm(n, DoI).mean(), ATR)
NORM = division(abs_arr(substract(PosDI, NegDI)), summation(PosDI, NegDI))
NORM[last_ele] = math.nan
ADX = cudf.Series(Ewm(n_ADX, NORM).mean(), nan_as_null=False)
return ADX
def average_true_range(high_arr, low_arr, close_arr, n):
"""Calculate the Average True Range
See https://www.investopedia.com/terms/a/atr.asp for details
:param high_arr: high price of the bar, expect series from cudf
:param low_arr: low price of the bar, expect series from cudf
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: average true range indicator
"""
tr = true_range(high_arr.data.to_gpu_array(), low_arr.data.to_gpu_array(),
close_arr.data.to_gpu_array())
ATR = Ewm(n, tr).mean()
return cudf.Series(ATR)
def coppock_curve(close_arr, n):
"""Calculate Coppock Curve for given data.
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: Coppock Curve in cudf.Series
"""
M = diff(close_arr, int(n * 11 / 10) - 1)
N = shift(close_arr, int(n * 11 / 10) - 1)
ROC1 = division(M, N)
M = diff(close_arr, int(n * 14 / 10) - 1)
N = shift(close_arr, int(n * 14 / 10) - 1)
ROC2 = division(M, N)
Copp = Ewm(n, summation(ROC1, ROC2)).mean()
return cudf.Series(Copp)
