wspace=.050,
hspace=0.01)
gl.savefig(folder_images + 'MAMAw.png', dpi=100, sizeInches=[2 * 8, 2 * 3])
if (HullsMA):
# Some basic indicators.
price = timeData.get_timeSeries(["Close"])
dates = timeData.get_dates()
# For comparing SMA, EMA, WMA
nHMA = 20
# Lag of different amplitudes.
#
HMA = indl.get_HMA(price, nHMA)
WMA = indl.get_WMA(price, nHMA)
HMAg = indl.get_HMAg(price, nHMA)
# For lag and noise
# Plotting the 3 of them at the same time.
title = "Hull's MA. " + str(
symbols[0]) + "(" + ul5.period_dic[timeData.period] + ")"
gl.plot(dates, [price, HMA, HMAg, WMA],
nf=1,
AxesStyle="Normal",
labels=[title, "", r"Price ($\$$)"],
legend=[
"Price",
Target = Target # Increase in Close price
Range_HL = H - L # measure of volatility
Daily_gap = O - bMl.shift(C, lag=1).flatten() # measure of price movement
## Add the lagged value to the database
Nlag_OCHL_information = 3
tut.add_lagged_values(data_df, Target, "Target", Nlag_OCHL_information)
tut.add_lagged_values(data_df, Range_HL, "Range_HL", Nlag_OCHL_information)
tut.add_lagged_values(data_df, Daily_gap, "Daily_gap",
Nlag_OCHL_information)
################## Daily Trading Indicators ####################
# Hulls_average !! ACDC, Volatility, ATR, Short
nHMA = 20
## Hulls Average, reactive but smoothing MA
HMA = indl.get_HMA(timeData_daily.get_timeSeries(["Close"]), nHMA)
## Volatility
nAHLR = 20
nBB = 20
nATR = 20
nCha = 20
AHLR = timeData_daily.AHLR(n=nAHLR)
ATR = timeData_daily.ATR(n=nATR)
EMA_Range, Cha = timeData_daily.Chaikin_vol(n=nCha)
BB = timeData_daily.BBANDS(seriesNames=["Close"], n=nBB)
BB = BB[:, 0] - BB[:, 1]
# Oscillators
n, SK, SD = 14, 6, 6
L = 14
def get_HMA(self, L):
timeSeries = self.get_timeSeries()
HMA = indl.get_HMA(timeSeries, L)
return HMA
def get_HMA(self, L):
if (self.timeSeries == []): # Check existence of timeSeries
self.get_timeSeries()
HMA = indl.get_HMA(self.timeSeries, L)
return HMA
Target = Target # Increase in Close price Range_HL = H-L # measure of volatility Daily_gap = O - bMl.shift(C,lag = 1).flatten() # measure of price movement ## Add the lagged value to the database Nlag_OCHL_information = 3 tut.add_lagged_values(data_df,Target,"Target",Nlag_OCHL_information) tut.add_lagged_values(data_df,Range_HL,"Range_HL",Nlag_OCHL_information) tut.add_lagged_values(data_df,Daily_gap,"Daily_gap",Nlag_OCHL_information) ################## Daily Trading Indicators #################### # Hulls_average !! ACDC, Volatility, ATR, Short nHMA = 20 ## Hulls Average, reactive but smoothing MA HMA = indl.get_HMA(timeData_daily.get_timeSeries(["Close"]), nHMA) ## Volatility nAHLR = 20; nBB = 20; nATR = 20; nCha = 20; AHLR = timeData_daily.AHLR(n = nAHLR) ATR = timeData_daily.ATR(n = nATR) EMA_Range, Cha = timeData_daily.Chaikin_vol(n = nCha) BB = timeData_daily.BBANDS(seriesNames = ["Close"], n = nBB) BB = BB[:,0] - BB[:,1] # Oscillators n , SK, SD = 14, 6,6 L = 14 L1 , L2, L3 = 14, 9,12 STO = timeData_daily.STO(n = n, SK = SK, SD = SD)
def get_HMA(self, L):
timeSeries = self.get_timeSeries()
HMA = indl.get_HMA(timeSeries, L)
return HMA
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.050, hspace=0.01)
gl.savefig(folder_images +'MAMAw.png',
dpi = 100, sizeInches = [2*8, 2*3])
if (HullsMA):
# Some basic indicators.
price = timeData.get_timeSeries(["Close"]);
dates = timeData.get_dates()
# For comparing SMA, EMA, WMA
nHMA = 20
# Lag of different amplitudes.
#
HMA = indl.get_HMA(price, nHMA)
WMA = indl.get_WMA(price, nHMA)
HMAg = indl.get_HMAg(price, nHMA)
# For lag and noise
# Plotting the 3 of them at the same time.
title = "Hull's MA. " + str(symbols[0]) + "(" + ul.period_dic[timeData.period]+ ")"
gl.plot(dates, [price, HMA, HMAg, WMA] , nf = 1 ,AxesStyle = "Normal",
labels = [title,"",r"Price ($\$$)"],
legend = ["Price", "HMA(%i)"%nHMA, "HMAg(%i)"%nHMA, "WMA(%i)"%nHMA])
gl.savefig(folder_images +'HMA.png',
dpi = 100, sizeInches = [2*8, 2*3])