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
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],
AxesStyle="Normal",
gl.savefig(folder_images +'OscillatorsMOM.png',
dpi = 100, sizeInches = [2*8, 2*2])
price = timeData.get_timeSeries(["Close"]);
dates = timeData.get_dates()
df = timeData.get_timeData()
# Momentum and Rate of convergence obtained from the real price.
nMOMs = [10, 20, 30]
MOM1 = timeData.MOM(n = 1)
EMAMOMs = [indl.get_EMA(MOM1, nMOMi) for nMOMi in nMOMs]
# Normalize ROC to MOM
gl.set_subplots(2,1)
gl.plot(dates, price , nf = 1,
labels = ["Smoothed MOM(1)","","Price"],
legend = ["Price", " Momentum", "ROC"])
legend = ["EMA(MOM(1),%i)"%x for x in nMOMs]
gl.plot(dates, EMAMOMs , nf = 1, na = 0,
legend = legend)
# The nect plot is just so that the vision starts in the first date
gl.plot(dates, np.zeros((dates.size,1)) , nf = 0, na = 0)
def get_EMA(self, L, alpha=-1):
timeSeries = self.get_timeSeries()
EMA = indl.get_EMA(timeSeries, L, alpha)
return EMA
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)
## Plotting just the 3 windows
gl.set_subplots(1,3)
marker = [".",20,"r"]
lw = 4
# Plotting the 3 of them at the same time.
ax1 = gl.stem([], diffw1, nf = 1, lw = lw,
labels = ["MOMw(%i)"%ndiff1,"lag","Value"],
xlimPad = [0.1,0.3], ylimPad = [0.1,0.1],
wspace=.20,
hspace=0)
gl.savefig(folder_images + 'OscillatorsMOM.png',
dpi=100,
sizeInches=[2 * 8, 2 * 2])
price = timeData.get_timeSeries(["Close"])
dates = timeData.get_dates()
df = timeData.get_timeData()
# Momentum and Rate of convergence obtained from the real price.
nMOMs = [10, 20, 30]
MOM1 = timeData.MOM(n=1)
EMAMOMs = [indl.get_EMA(MOM1, nMOMi) for nMOMi in nMOMs]
# Normalize ROC to MOM
gl.set_subplots(2, 1)
gl.plot(dates,
price,
nf=1,
labels=["Smoothed MOM(1)", "", "Price"],
legend=["Price", " Momentum", "ROC"])
legend = ["EMA(MOM(1),%i)" % x for x in nMOMs]
gl.plot(dates, EMAMOMs, nf=1, na=0, legend=legend)
# The nect plot is just so that the vision starts in the first date
gl.plot(dates, np.zeros((dates.size, 1)), nf=0, na=0)
def get_EMA(self, L, alpha=-1):
if (self.timeSeries == []): # Check existence of timeSeries
self.get_timeSeries()
EMA = indl.get_EMA(self.timeSeries, L, alpha)
return EMA
def get_EMA(self, L, alpha = -1):
timeSeries = self.get_timeSeries()
EMA = indl.get_EMA(timeSeries, L, alpha)
return EMA
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",
labels = [title,"",r"Price ($\$$)"],