def analyzeGoldenAndDeathCross(companyName='GOOG',
nDays_short=50,
nDays_long=200,
start='2019-10-31'):
# Load dataframe for company
dataFr = general.loadCompanyData(companyName, start=start)
# Create moving average columns
dataFr = getMovingAverage(dataFr, colName='Close', nDays=nDays_short)
dataFr = getMovingAverage(dataFr, colName='Close', nDays=nDays_long)
fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(111)
ax.plot(dataFr['Date'].to_list(),
dataFr['Close'].to_list(),
label='Closing')
ax.plot(dataFr['Date'].to_list(),
dataFr['movingAverage_{}'.format(nDays_short)].to_list(),
label='{}-day SMA'.format(nDays_short),
linewidth=3)
ax.plot(dataFr['Date'].to_list(),
dataFr['movingAverage_{}'.format(nDays_long)].to_list(),
label='{}-day SMA'.format(nDays_long),
linewidth=3)
plt.xticks(rotation=45, ha="right")
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.legend(prop={'size': 20})
def computeAndPlotFullStochasticOscillator(companyName='GOOG',
n1=14,
n2=3,
n3=3,
start='2019-10-31',
every_nth=20):
# Load dataframe for company
dataFr = general.loadCompanyData(companyName, start=start)
dataFr['fast_%K'] = STOK(dataFr, n1)
# SMA
dataFr['full_%K'] = dataFr['fast_%K'].rolling(n2).mean()
dataFr['full_%D'] = dataFr['full_%K'].rolling(n3).mean()
fig = plt.figure(figsize=(20, 10))
# set height ratios for sublots
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
# the fisrt subplot
ax0 = plt.subplot(gs[0])
# ax0.plot(dataFr['Date'].to_list(),
# dataFr['Close'].to_list(),
# label='Closing');
highList = dataFr['High'].to_list()
lowList = dataFr['Low'].to_list()
dateList = dataFr['Date'].to_list()
closeList = dataFr['Close'].to_list()
lowerError = [a - b for a, b in zip(closeList, lowList)]
upperError = [a - b for a, b in zip(highList, closeList)]
ax0.errorbar(x=dateList,
y=closeList,
yerr=[lowerError, upperError],
fmt='o')
for n, label in enumerate(ax0.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.xticks(rotation=45, ha="right")
# the second subplot
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.plot(dataFr['Date'].to_list(),
dataFr['full_%K'].to_list(),
label='Full %K',
c='r')
ax1.plot(dataFr['Date'].to_list(),
dataFr['full_%D'].to_list(),
label='Full %D',
c='k')
ax1.axhline(y=50, linestyle='-.')
ax1.axhline(y=20, linestyle='-')
ax1.axhline(y=80, linestyle='-')
for n, label in enumerate(ax1.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.xticks(rotation=45, ha="right")
plt.yticks([0, 20, 50, 80, 100])
plt.legend(prop={'size': 14})
def getRSI(companyName='GOOG', start='2020-01-01', n=14, every_nth=20):
# Load dataframe for company
dataFr = general.loadCompanyData(companyName, start=start)
dataFr.index = range(len(dataFr))
dataFr['change'] = dataFr['Close'].diff()
dataFr['gain'] = dataFr.change.mask(dataFr.change < 0, 0.0)
dataFr['loss'] = -dataFr.change.mask(dataFr.change > 0, -0.0)
dataFr.loc[n:, 'avg_gain'] = rma(dataFr.gain[n + 1:].values, n,
dataFr.loc[:n, 'gain'].mean())
dataFr.loc[n:, 'avg_loss'] = rma(dataFr.loss[n + 1:].values, n,
dataFr.loc[:n, 'loss'].mean())
dataFr['rs'] = dataFr.avg_gain / dataFr.avg_loss
dataFr['rsi_14'] = 100 - (100 / (1 + dataFr.rs))
# Plot data
fig = plt.figure(figsize=(20, 10))
# set height ratios for sublots
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
# the first subplot
ax0 = plt.subplot(gs[0])
# ax0.plot(dataFr['Date'].to_list(),
# dataFr['Close'].to_list(),
# label='Closing');
highList = dataFr['High'].to_list()
lowList = dataFr['Low'].to_list()
dateList = dataFr['Date'].to_list()
closeList = dataFr['Close'].to_list()
lowerError = [a - b for a, b in zip(closeList, lowList)]
upperError = [a - b for a, b in zip(highList, closeList)]
ax0.errorbar(x=dateList,
y=closeList,
yerr=[lowerError, upperError],
fmt='o')
for n, label in enumerate(ax0.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.xticks(rotation=45, ha="right")
# the second subplot
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.plot(dataFr['Date'].to_list(),
dataFr['rsi_14'].to_list(),
label='RSI',
c='c')
ax1.axhline(y=50, linestyle='-.', color='k')
ax1.axhline(y=30, linestyle='-', color='g')
ax1.axhline(y=70, linestyle='-', color='r')
for n, label in enumerate(ax1.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.xticks(rotation=45, ha="right")
plt.yticks([0, 30, 50, 70, 100])
plt.legend(prop={'size': 12}, loc='upper left')
def getMACD(companyName='GOOG',
start='2020-01-01',
n1=12,
n2=26,
n3=9,
every_nth=20):
# Load dataframe for company
dataFr = general.loadCompanyData(companyName, start=start)
dataFr = getExponentialMovingAverage(dataFr, nDays=n1)
dataFr = getExponentialMovingAverage(dataFr, nDays=n2)
dataFr['MACD'] = dataFr.apply(
lambda row: (row['EMA_{}'.format(n1)] - row['EMA_{}'.format(n2)]),
axis=1)
dataFr = getSignalLine(dataFr, colName='MACD', nDays=n3, n2=n2)
dataFr['MACD_histogram'] = dataFr.apply(
lambda row: row['MACD'] - row['SignalLine'], axis=1)
# Plot data
fig = plt.figure(figsize=(20, 10))
# set height ratios for sublots
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
# the first subplot
ax0 = plt.subplot(gs[0])
# ax0.plot(dataFr['Date'].to_list(),
# dataFr['Close'].to_list(),
# label='Closing');
highList = dataFr['High'].to_list()
lowList = dataFr['Low'].to_list()
dateList = dataFr['Date'].to_list()
closeList = dataFr['Close'].to_list()
lowerError = [a - b for a, b in zip(closeList, lowList)]
upperError = [a - b for a, b in zip(highList, closeList)]
ax0.errorbar(x=dateList,
y=closeList,
yerr=[lowerError, upperError],
fmt='o')
for n, label in enumerate(ax0.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.xticks(rotation=45, ha="right")
ax0.plot(dateList,
dataFr['EMA_{}'.format(n1)].to_list(),
color='y',
linewidth=3,
label='{}-day EMA'.format(n1))
ax0.plot(dateList,
dataFr['EMA_{}'.format(n2)].to_list(),
color='m',
linewidth=3,
label='{}-day EMA'.format(n2))
plt.legend(prop={'size': 12}, loc='upper left')
# the second subplot
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.plot(dataFr['Date'].to_list(),
dataFr['MACD'].to_list(),
label='MACD',
c='r')
ax1.plot(dataFr['Date'].to_list(),
dataFr['SignalLine'].to_list(),
label='Signal Line',
c='k')
ax1.bar(dataFr['Date'].to_list(),
dataFr['MACD_histogram'].to_list(),
label='MACD histogram',
width=0.2,
color='g')
for n, label in enumerate(ax1.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
plt.xticks(rotation=45, ha="right")
plt.legend(prop={'size': 12}, loc='upper left')