def kst_oscillator(close_arr, r1, r2, r3, r4, n1, n2, n3, n4):
"""Calculate KST Oscillator for given data.
:param close_arr: close price of the bar, expect series from cudf
:param r1: r1 time steps
:param r2: r2 time steps
:param r3: r3 time steps
:param r4: r4 time steps
:param n1: n1 time steps
:param n2: n2 time steps
:param n3: n3 time steps
:param n4: n4 time steps
:return: KST Oscillator in cudf.Series
"""
M1 = diff(close_arr, r1 - 1)
N1 = shift(close_arr, r1 - 1)
M2 = diff(close_arr, r2 - 1)
N2 = shift(close_arr, r2 - 1)
M3 = diff(close_arr, r3 - 1)
N3 = shift(close_arr, r3 - 1)
M4 = diff(close_arr, r4 - 1)
N4 = shift(close_arr, r4 - 1)
term1 = Rolling(n1, division(M1, N1)).sum()
term2 = scale(Rolling(n2, division(M2, N2)).sum(), 2.0)
term3 = scale(Rolling(n3, division(M3, N3)).sum(), 3.0)
term4 = scale(Rolling(n4, division(M4, N4)).sum(), 4.0)
KST = summation(summation(summation(term1, term2), term3), term4)
return cudf.Series(KST)
def port_vortex_indicator(asset_indicator, high_arr, low_arr, close_arr, n):
"""Calculate the port Vortex Indicator for given data.
Vortex Indicator described here:
http://www.vortexindicator.com/VFX_VORTEX.PDF
: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
:return: Vortex Indicator in cudf.Series
"""
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())
VM = port_lowhigh_diff(asset_indicator.to_gpu_array(),
high_arr.data.to_gpu_array(),
low_arr.data.to_gpu_array())
VI = division(Rolling(n, VM).sum(), Rolling(n, TR).sum())
port_mask_nan(asset_indicator.data.to_gpu_array(), VI, 0, n - 1)
return cudf.Series(VI)
def diff(in_arr, n):
if n < 0:
return Rolling(1, in_arr, forward_window=-n).forward_diff()
elif n > 0:
return Rolling(n + 1, in_arr).backward_diff()
else:
return in_arr
def donchian_channel(high_arr, low_arr, n):
"""Calculate donchian channel of given pandas data frame.
: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
:return: donchian channel in cudf.Series
"""
max_high = Rolling(n, high_arr).max()
min_low = Rolling(n, low_arr).min()
dc_l = substract(max_high, min_low)
dc_l[:n - 1] = 0.0
donchian_chan = shift(dc_l, n - 1)
return cudf.Series(donchian_chan)
def commodity_channel_index(high_arr, low_arr, close_arr, n):
"""Calculate Commodity Channel 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
:return: Commodity Channel Index in cudf.Series
"""
PP = average_price(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
M = Rolling(n, PP).mean()
N = Rolling(n, PP).std()
CCI = division(substract(PP, M), N)
return cudf.Series(CCI, nan_as_null=False)
def keltner_channel(high_arr, low_arr, close_arr, n):
"""Calculate Keltner Channel 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: Keltner Channel in cudf.Series
"""
M = ((high_arr + low_arr + close_arr) / 3.0)
KelChM = cudf.Series(Rolling(n, M).mean())
U = ((4.0 * high_arr - 2.0 * low_arr + close_arr) / 3.0)
KelChU = cudf.Series(Rolling(n, U).mean())
D = ((-2.0 * high_arr + 4.0 * low_arr + close_arr) / 3.0)
KelChD = cudf.Series(Rolling(n, D).mean())
out = collections.namedtuple('Keltner', 'KelChM KelChU KelChD')
return out(KelChM=KelChM, KelChU=KelChU, KelChD=KelChD)
def moving_average(close_arr, n):
"""Calculate the moving average for the given data.
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: moving average in cu.Series
"""
MA = Rolling(n, close_arr).mean()
return cudf.Series(MA)
def port_donchian_channel(asset_indicator, high_arr, low_arr, n):
"""Calculate port donchian channel of given pandas data frame.
: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 n: time steps
:return: donchian channel in cudf.Series
"""
max_high = Rolling(n, high_arr).max()
port_mask_nan(asset_indicator.data.to_gpu_array(), max_high, 0, n - 1)
min_low = Rolling(n, low_arr).min()
port_mask_nan(asset_indicator.data.to_gpu_array(), min_low, 0, n - 1)
dc_l = substract(max_high, min_low)
# dc_l[:n-1] = 0.0
port_mask_zero(asset_indicator.data.to_gpu_array(), dc_l, 0, n - 1)
donchian_chan = shift(dc_l, n - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), donchian_chan, 0, n - 1)
return cudf.Series(donchian_chan)
def port_commodity_channel_index(asset_indicator, high_arr, low_arr, close_arr,
n):
"""Calculate port Commodity Channel 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
:return: Commodity Channel Index in cudf.Series
"""
PP = average_price(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
M = Rolling(n, PP).mean()
port_mask_nan(asset_indicator.to_gpu_array(), M, 0, n - 1)
N = Rolling(n, PP).std()
port_mask_nan(asset_indicator.to_gpu_array(), N, 0, n - 1)
CCI = division(substract(PP, M), N)
return cudf.Series(CCI, nan_as_null=False)
def vortex_indicator(high_arr, low_arr, close_arr, n):
"""Calculate the Vortex Indicator for given data.
Vortex Indicator described here:
http://www.vortexindicator.com/VFX_VORTEX.PDF
: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
:return: Vortex Indicator in cudf.Series
"""
TR = true_range(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
VM = lowhigh_diff(high_arr.to_gpu_array(), low_arr.to_gpu_array())
VI = division(Rolling(n, VM).sum(), Rolling(n, TR).sum())
return cudf.Series(VI, nan_as_null=False)
def bollinger_bands(close_arr, n):
"""Calculate the Bollinger Bands.
See https://www.investopedia.com/terms/b/bollingerbands.asp for details
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: b1 b2
"""
MA = Rolling(n, close_arr).mean()
MSD = Rolling(n, close_arr).std()
close_arr_gpu = numba.cuda.device_array_like(close_arr.data.to_gpu_array())
close_arr_gpu[:] = close_arr.data.to_gpu_array()[:]
close_arr_gpu[0:n - 1] = math.nan
MSD_4 = scale(MSD, 4.0)
b1 = division(MSD_4, MA)
b2 = division(summation(substract(close_arr_gpu, MA), scale(MSD, 2.0)),
MSD_4)
out = collections.namedtuple('Bollinger', 'b1 b2')
return out(b1=cudf.Series(b1), b2=cudf.Series(b2))
def port_kst_oscillator(asset_indicator, close_arr, r1, r2, r3, r4, n1, n2, n3,
n4):
"""Calculate port KST Oscillator 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 r1: r1 time steps
:param r2: r2 time steps
:param r3: r3 time steps
:param r4: r4 time steps
:param n1: n1 time steps
:param n2: n2 time steps
:param n3: n3 time steps
:param n4: n4 time steps
:return: KST Oscillator in cudf.Series
"""
M1 = diff(close_arr, r1 - 1)
N1 = shift(close_arr, r1 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), M1, 0, r1 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), N1, 0, r1 - 1)
M2 = diff(close_arr, r2 - 1)
N2 = shift(close_arr, r2 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), M2, 0, r2 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), N2, 0, r2 - 1)
M3 = diff(close_arr, r3 - 1)
N3 = shift(close_arr, r3 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), M3, 0, r3 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), N3, 0, r3 - 1)
M4 = diff(close_arr, r4 - 1)
N4 = shift(close_arr, r4 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), M4, 0, r4 - 1)
port_mask_nan(asset_indicator.data.to_gpu_array(), N4, 0, r4 - 1)
term1 = Rolling(n1, division(M1, N1)).sum()
port_mask_nan(asset_indicator.data.to_gpu_array(), term1, 0, n1 - 1)
term2 = scale(Rolling(n2, division(M2, N2)).sum(), 2.0)
port_mask_nan(asset_indicator.data.to_gpu_array(), term2, 0, n2 - 1)
term3 = scale(Rolling(n3, division(M3, N3)).sum(), 3.0)
port_mask_nan(asset_indicator.data.to_gpu_array(), term3, 0, n3 - 1)
term4 = scale(Rolling(n4, division(M4, N4)).sum(), 4.0)
port_mask_nan(asset_indicator.data.to_gpu_array(), term4, 0, n4 - 1)
KST = summation(summation(summation(term1, term2), term3), term4)
return cudf.Series(KST)
def on_balance_volume(close_arr, volume_arr, n):
"""Calculate On-Balance Volume for given data.
:param close_arr: close price of the bar, expect series from cudf
:param volume_arr: volume the bar, expect series from cudf
:param n: time steps
:return: On-Balance Volume in cudf.Series
"""
OBV = onbalance_volume(close_arr.to_gpu_array(), volume_arr.to_gpu_array())
OBV_ma = Rolling(n, OBV).mean()
return cudf.Series(OBV_ma, nan_as_null=False)
def port_moving_average(asset_indicator, close_arr, n):
"""Calculate the port moving average for the 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 n: time steps
:return: expoential weighted moving average in cu.Series
"""
MA = Rolling(n, close_arr).mean()
port_mask_nan(asset_indicator.data.to_gpu_array(), MA, 0, n - 1)
return cudf.Series(MA)
def port_keltner_channel(asset_indicator, high_arr, low_arr, close_arr, n):
"""Calculate port Keltner Channel 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
:return: Keltner Channel in cudf.Series
"""
M = ((high_arr + low_arr + close_arr) / 3.0)
KelChM = Rolling(n, M).mean()
port_mask_nan(asset_indicator.data.to_gpu_array(), KelChM, 0, n - 1)
U = ((4.0 * high_arr - 2.0 * low_arr + close_arr) / 3.0)
KelChU = Rolling(n, U).mean()
port_mask_nan(asset_indicator.data.to_gpu_array(), KelChU, 0, n - 1)
D = ((-2.0 * high_arr + 4.0 * low_arr + close_arr) / 3.0)
KelChD = Rolling(n, D).mean()
port_mask_nan(asset_indicator.data.to_gpu_array(), KelChD, 0, n - 1)
out = collections.namedtuple('Keltner', 'KelChM KelChU KelChD')
return out(KelChM=cudf.Series(KelChM),
KelChU=cudf.Series(KelChU),
KelChD=cudf.Series(KelChD))
def port_bollinger_bands(asset_indicator, close_arr, n):
"""Calculate the port Bollinger Bands.
See https://www.investopedia.com/terms/b/bollingerbands.asp for details
:param asset_indicator: the indicator of beginning of the stock
:param close_arr: close price of the bar, expect series from cudf
:param n: time steps
:return: b1 b2
"""
MA = Rolling(n, close_arr).mean()
port_mask_nan(asset_indicator.to_gpu_array(), MA, 0, n - 1)
MSD = Rolling(n, close_arr).std()
port_mask_nan(asset_indicator.to_gpu_array(), MSD, 0, n - 1)
close_arr_gpu = numba.cuda.device_array_like(close_arr.to_gpu_array())
close_arr_gpu[:] = close_arr.to_gpu_array()[:]
close_arr_gpu[0:n - 1] = math.nan
MSD_4 = scale(MSD, 4.0)
b1 = division(MSD_4, MA)
b2 = division(summation(substract(close_arr_gpu, MA), scale(MSD, 2.0)),
MSD_4)
out = collections.namedtuple('Bollinger', 'b1 b2')
return out(b1=cudf.Series(b1, nan_as_null=False),
b2=cudf.Series(b2, nan_as_null=False))
def port_on_balance_volume(asset_indicator, close_arr, volume_arr, n):
"""Calculate port On-Balance Volume 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 volume_arr: volume the bar, expect series from cudf
:param n: time steps
:return: On-Balance Volume in cudf.Series
"""
OBV = port_onbalance_volume(asset_indicator.data.to_gpu_array(),
close_arr.data.to_gpu_array(),
volume_arr.data.to_gpu_array())
OBV_ma = Rolling(n, OBV).mean()
port_mask_nan(asset_indicator.data.to_gpu_array(), OBV_ma, 0, n - 1)
return cudf.Series(OBV_ma)
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 money_flow_index(high_arr, low_arr, close_arr, volume_arr, n):
"""Calculate Money Flow Index and Ratio 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 n: time steps
:return: Money Flow Index in cudf.Series
"""
PP = average_price(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
PosMF = money_flow(PP, volume_arr.to_gpu_array())
MFR = division(PosMF, (multiply(PP, volume_arr.to_gpu_array()))) # TotMF
MFI = Rolling(n, MFR).mean()
return cudf.Series(MFI, nan_as_null=False)
def port_mass_index(asset_indicator, high_arr, low_arr, n1, n2):
"""Calculate the port Mass 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 n1: n1 time steps
:param n1: n2 time steps
:return: Mass Index in cudf.Series
"""
Range = high_arr - low_arr
EX1 = PEwm(n1, Range, asset_indicator).mean()
EX2 = PEwm(n1, EX1, asset_indicator).mean()
Mass = division(EX1, EX2)
MassI = Rolling(n2, Mass).sum()
port_mask_nan(asset_indicator.data.to_gpu_array(), MassI, 0, n2 - 1)
return cudf.Series(MassI)
def ease_of_movement(high_arr, low_arr, volume_arr, n):
"""Calculate Ease of Movement 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 volume_arr: volume the bar, expect series from cudf
:param n: time steps
:return: Ease of Movement in cudf.Series
"""
high_arr_gpu = high_arr.data.to_gpu_array()
low_arr_gpu = low_arr.data.to_gpu_array()
EoM = division(
multiply(summation(diff(high_arr_gpu, 1), diff(low_arr_gpu, 1)),
substract(high_arr_gpu, low_arr_gpu)),
scale(volume_arr.data.to_gpu_array(), 2.0))
Eom_ma = Rolling(n, EoM).mean()
return cudf.Series(Eom_ma)
def port_money_flow_index(asset_indicator, high_arr, low_arr, close_arr,
volume_arr, n):
"""Calculate port Money Flow Index and Ratio 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 volume_arr: volume the bar, expect series from cudf
:param n: time steps
:return: Money Flow Index in cudf.Series
"""
PP = average_price(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
PosMF = port_money_flow(asset_indicator.to_gpu_array(), PP,
volume_arr.to_gpu_array())
MFR = division(PosMF, (multiply(PP, volume_arr.to_gpu_array()))) # TotMF
MFI = Rolling(n, MFR).mean()
port_mask_nan(asset_indicator.to_gpu_array(), MFI, 0, n - 1)
return cudf.Series(MFI, nan_as_null=False)
def port_ease_of_movement(asset_indicator, high_arr, low_arr, volume_arr, n):
"""Calculate port Ease of Movement 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 volume_arr: volume the bar, expect series from cudf
:param n: time steps
:return: Ease of Movement in cudf.Series
"""
high_arr_gpu = high_arr.data.to_gpu_array()
low_arr_gpu = low_arr.data.to_gpu_array()
EoM = division(
multiply(summation(diff(high_arr_gpu, 1), diff(low_arr_gpu, 1)),
substract(high_arr_gpu, low_arr_gpu)),
scale(volume_arr.data.to_gpu_array(), 2.0))
port_mask_nan(asset_indicator.data.to_gpu_array(), EoM, 0, 1)
Eom_ma = Rolling(n, EoM).mean()
port_mask_nan(asset_indicator.data.to_gpu_array(), Eom_ma, 0, n - 1)
return cudf.Series(Eom_ma)
def ultimate_oscillator(high_arr, low_arr, close_arr):
"""Calculate Ultimate 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
:return: Ultimate Oscillator in cudf.Series
"""
TR_l, BP_l = ultimate_osc(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
close_arr.to_gpu_array())
term1 = division(scale(Rolling(7, BP_l).sum(), 4.0),
Rolling(7, TR_l).sum())
term2 = division(scale(Rolling(14, BP_l).sum(), 2.0),
Rolling(14, TR_l).sum())
term3 = division(Rolling(28, BP_l).sum(), Rolling(28, TR_l).sum())
UltO = summation(summation(term1, term2), term3)
return cudf.Series(UltO, nan_as_null=False)
def port_ultimate_oscillator(asset_indicator, high_arr, low_arr, close_arr):
"""Calculate port Ultimate Oscillator 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
:return: Ultimate Oscillator in cudf.Series
"""
TR_l, BP_l = port_ultimate_osc(asset_indicator.data.to_gpu_array(),
high_arr.data.to_gpu_array(),
low_arr.data.to_gpu_array(),
close_arr.data.to_gpu_array())
term1 = division(scale(Rolling(7, BP_l).sum(), 4.0),
Rolling(7, TR_l).sum())
term2 = division(scale(Rolling(14, BP_l).sum(), 2.0),
Rolling(14, TR_l).sum())
term3 = division(Rolling(28, BP_l).sum(), Rolling(28, TR_l).sum())
port_mask_nan(asset_indicator.data.to_gpu_array(), term1, 0, 6)
port_mask_nan(asset_indicator.data.to_gpu_array(), term2, 0, 13)
port_mask_nan(asset_indicator.data.to_gpu_array(), term3, 0, 27)
UltO = summation(summation(term1, term2), term3)
return cudf.Series(UltO)
