def get_BollingerBand (timeSeries, L = 20):
## Get the Bollinger Bands !!
MA = indl.get_SMA(timeSeries, L = L)
diff = np.power(timeSeries - MA, 2);# Get the difference to the square
# print MA.shape
# print price.shape
# print diff.shape
diff_SMA = indl.get_SMA(diff, L) # Get the avera STD over L periods
diff_SMA = 2 * np.sqrt(diff_SMA)
# Now we apply a MA over this shit
return diff_SMA
def get_MACD (time_series, Ls = 12, Ll = 26, Lsmoth = 9, alpha = -1):
"""
Moving Average Convergence/Divergence (MACD) indicates the correlation between
two price moving averages.
Usually 26-period and 12-period Exponential Moving Average (EMA).
In order to clearly show buy/sell opportunities,
a so-called signal line (9-period indicators` moving average) is plotted on the MACD chart.
The MACD proves most effective in wide-swinging trading markets.
There are three popular ways to use the Moving Average Convergence/Divergence:
crossovers, overbought/oversold conditions, and divergences.
The MACD is calculated by subtracting the value of a 26-period exponential
moving average from a 12-period exponential moving average.
A 9-period dotted simple moving average of the MACD (the signal line)
is then plotted on top of the MACD.
"""
eMlong = indl.get_EMA(time_series, Ll, alpha)
eMshort = indl.get_EMA(time_series, Ls, alpha)
MACD = indl.get_SMA(eMshort - eMlong, Lsmoth)
return eMlong, eMshort, MACD
right=.90,
top=.95,
wspace=.20,
hspace=0)
gl.savefig(folder_images + 'lagsMAs.png', dpi=100, sizeInches=[16, 9])
if (viewing_SEW_windows):
# Some basic indicators.
price = timeData.get_timeSeries(["Close"])
dates = timeData.get_dates()
nMA1 = 10
nMA2 = 20
SMAw = indl.get_SMA(bMA.delta(nMA1), nMA1, cval=1)
EMAw = indl.get_EMA(bMA.delta(nMA1), nMA1, cval=1)
WMAw = indl.get_WMA(bMA.delta(nMA1), nMA1, cval=1)
SMAw2 = indl.get_SMA(bMA.delta(nMA2), nMA2, cval=1)
EMAw2 = indl.get_EMA(bMA.delta(nMA2), nMA2, cval=1)
WMAw2 = indl.get_WMA(bMA.delta(nMA2), nMA2, cval=1)
SMA = timeData.SMA(n=nMA1)
""" 1st GRAPH """
############## Average and Window ################
ax1 = gl.subplot2grid((1, 5), (0, 0), rowspan=1, colspan=3)
title = "Price and SMA. " + str(
symbols[0]) + "(" + ul5.period_dic[timeData.period] + ")"
gl.plot(dates, [price, SMA],
indx = 1 lag = 35 ## MACD Parameters !! n_fast = 12 n_slow = 26 n_smooth = 9 EMAfast = Cartera.EMA(n=n_fast) EMAslow = Cartera.EMA(n=n_slow) MACD = EMAfast - EMAslow MACD = pd.ewma(MACD, span=n_smooth, min_periods=n_smooth - 1) # Smoothin of velocity, we want to enter when it is falling MACD_vel = bMA.diff(MACD, n=1) nsmooth_vel = 4 MACD_vel = indl.get_SMA(MACD_vel, L=nsmooth_vel) ## RSI Parameter ! RSI = Cartera.RSI(n=14) RSI_vel = bMA.diff(RSI, n=1) #RSI_vel = indl.get_SMA(RSI_vel, L = nsmooth_vel) ## ATR Parameter ! ATR = Cartera.ATR(n=14) ATR_vel = bMA.diff(ATR, n=1) RSI_vel = indl.get_SMA(ATR_vel, L=nsmooth_vel) ########################################################### ################# PREPARE THE DATA ######################## ########################################################### X_data = np.concatenate((MACD[:, [indx]], MACD_vel[:, [indx]]), axis=1)
def get_SMA(self, L):
timeSeries = self.get_timeSeries()
SMA = indl.get_SMA(timeSeries, L)
return SMA
ndiff1 = 1; ndiff2 = 3; ndiff3 = 7
# Lag of different amplitudes.
#
delta = bMA.delta(15)
diffw1 = bMA.diffw(10,ndiff1)
diffw2 = bMA.diffw(10,ndiff2)
diffw3 = bMA.diffw(10,ndiff3)
diffws = [diffw1,diffw2,diffw3]
sel_diff = 0
diff_final = diffws[sel_diff]
SMAw = indl.get_SMA(delta, nHMA, cval = 1)
dSMAw = bMA.convolve(SMAw, diff_final)
# SMASMA = bMA.diff(SMASMA, cval = np.nan)
# SMASMA = indl.get_SMA(diffw1, nHMA, cval = 1)
WMAw = indl.get_WMA(delta, nHMA, cval = 1)
dWMAw = bMA.convolve(WMAw, diff_final)
# WMAWMA = bMA.diff(WMAWMA, cval = np.nan)
# WMAWMA = indl.get_WMA(diffw1, nHMA, cval = 1)
EMAw = indl.get_EMA(delta, nHMA, cval = 1)
dEMAw = bMA.convolve(EMAw, diff_final)
# EMAEMA = bMA.diff(EMAEMA, cval = np.nan)
# EMAEMA = indl.get_EMA(diffw1, nHMA, cval = 1)
region_to_optimize = df_list[ir]
different_contries = np.unique(
region_to_optimize["LocationCountryName"])
days = np.unique(region_to_optimize["Date"])
Country_day.append([])
days_list.append([])
for soID in different_contries:
Country_day[-1].append([])
days_list[-1].append([])
rows_trailer = region_to_optimize[
region_to_optimize["LocationCountryName"] == soID]
# Now we find the collection
Ns, Nd = rows_trailer.shape
# print rows_trailer.shape
for day in days:
Country_day[-1][-1].append(0)
days_list[-1][-1].append(day)
# print rows_trailer.shape
rows_trailer2 = rows_trailer[rows_trailer["Date"] == day]
# print rows_trailer.shape
rows_trailer2 = rows_trailer2[rows_trailer2["TrackingAction"]
== "Delivery"]
Country_day[-1][-1][-1] += rows_trailer2.shape[0]
for k in range(len(days_list[0])):
week_mean = intl.get_SMA(ul.fnp(Country_day[0][k]), 7)
gl.plot(days_list[0][k],
week_mean,
nf=0,
legend=[different_contries[k]])
def get_SMA(self, L):
if (self.timeSeries == []): # Check existence of timeSeries
self.get_timeSeries()
SMA = indl.get_SMA(self.timeSeries, L)
return SMA
# We get now the moving averages indx = 1 lag = 35 ## MACD Parameters !! n_fast = 12; n_slow = 26; n_smooth = 9 EMAfast = Cartera.EMA(n = n_fast) EMAslow = Cartera.EMA(n = n_slow) MACD = EMAfast - EMAslow MACD = pd.ewma(MACD, span = n_smooth, min_periods = n_smooth - 1) # Smoothin of velocity, we want to enter when it is falling MACD_vel = bMA.diff(MACD,n = 1) nsmooth_vel = 4 MACD_vel = indl.get_SMA(MACD_vel, L = nsmooth_vel) ## RSI Parameter ! RSI = Cartera.RSI(n = 14) RSI_vel = bMA.diff(RSI,n = 1) #RSI_vel = indl.get_SMA(RSI_vel, L = nsmooth_vel) ## ATR Parameter ! ATR = Cartera.ATR(n = 14) ATR_vel = bMA.diff(ATR,n = 1) RSI_vel = indl.get_SMA(ATR_vel, L = nsmooth_vel) ########################################################### ################# PREPARE THE DATA ######################## ########################################################### X_data = np.concatenate((MACD[:,[indx]],MACD_vel[:,[indx]]), axis = 1)
def get_SMA(self, L ):
timeSeries = self.get_timeSeries()
SMA = indl.get_SMA(timeSeries, L)
return SMA
legend = legend, AxesStyle = "Normal")
gl.subplots_adjust(left=.09, bottom=.10, right=.90, top=.95, wspace=.20, hspace=0)
gl.savefig(folder_images +'lagsMAs.png',
dpi = 100, sizeInches = [16, 9])
if (viewing_SEW_windows):
# Some basic indicators.
price = timeData.get_timeSeries(["Close"]);
dates = timeData.get_dates()
nMA1 = 10
nMA2 = 20
SMAw = indl.get_SMA(bMA.delta(nMA1), nMA1, cval = 1)
EMAw = indl.get_EMA(bMA.delta(nMA1), nMA1, cval = 1)
WMAw = indl.get_WMA(bMA.delta(nMA1), nMA1, cval = 1)
SMAw2 = indl.get_SMA(bMA.delta(nMA2), nMA2, cval = 1)
EMAw2 = indl.get_EMA(bMA.delta(nMA2), nMA2, cval = 1)
WMAw2 = indl.get_WMA(bMA.delta(nMA2), nMA2, cval = 1)
SMA = timeData.SMA(n = nMA1)
""" 1st GRAPH """
############## Average and Window ################
ax1 = gl.subplot2grid((1,5), (0,0), rowspan=1, colspan=3)
title = "Price and SMA. " + str(symbols[0]) + "(" + ul.period_dic[timeData.period]+ ")"
gl.plot(dates, [price, SMA] ,AxesStyle = "Normal",