def port_average_directional_movement_index(asset_indicator, high_arr, low_arr,
close_arr, n, n_ADX):
"""Calculate the port Average Directional Movement Index for given data.
:param asset_indicator: the indicator of beginning of the stock
: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.data.to_gpu_array(),
low_arr.data.to_gpu_array())
tr = port_true_range(asset_indicator.to_gpu_array(),
high_arr.data.to_gpu_array(),
low_arr.data.to_gpu_array(),
close_arr.data.to_gpu_array())
ATR = PEwm(n, tr, asset_indicator).mean()
PosDI = division(PEwm(n, UpI, asset_indicator).mean(), ATR)
NegDI = division(PEwm(n, DoI, asset_indicator).mean(), ATR)
NORM = division(abs_arr(substract(PosDI, NegDI)), summation(PosDI, NegDI))
port_mask_nan(asset_indicator.data.to_gpu_array(), NORM, -1, 0)
ADX = cudf.Series(PEwm(n_ADX, NORM, asset_indicator).mean())
return ADX
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 port_true_strength_index(asset_indicator, close_arr, r, s):
"""Calculate port True Strength Index (TSI) for given data.
:param asset_indicator: the indicator of beginning of the stock
: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)
port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0, 1)
aM = abs_arr(M)
EMA1 = PEwm(r, M, asset_indicator).mean()
aEMA1 = PEwm(r, aM, asset_indicator).mean()
EMA2 = PEwm(s, EMA1, asset_indicator).mean()
aEMA2 = PEwm(s, aEMA1, asset_indicator).mean()
TSI = division(EMA2, aEMA2)
return cudf.Series(TSI)
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