def three_line_break_analysis_sample():
"""
three line break technical analysis sample
"""
# Three Line Break Parameters
# Currency code to analyse
currency = 'USD'
# number of days to control
line_break_control_count = 3
# start and end dates of analyse period
start_day = datetime.datetime(2015, 6, 1)
end_day = datetime.datetime(2016, 1, 23)
# Reading rates with given parameters to be analysed
mc = MongoConnector()
rates = mc.mongo_get_rates(currency, start_day,
end_day, line_break_control_count)
# creating TLB instance and analysing the data
tlb = ThreeLineBreakAnalysis(rates, line_break_control_count, start_day)
result = tlb.analyse()
# creating the chart of the result
fig = seaborn.plt.figure(figsize=(14, 5))
locs, labels = seaborn.plt.xticks()
fig.axes[0].set_title('Three Line Break (' +
str(line_break_control_count) +
') EUR/' + currency + ' ' +
start_day.strftime('%d/%m/%Y') + ' - ' +
end_day.strftime('%d/%m/%Y'))
fig.axes[0].set(xticklabels=[])
a = 0
for rs in result:
fig.axes[0].plot([a, a], [rs.min_price, rs.max_price], linewidth=0)
if rs.color == 'G':
# increases are green
fig.axes[0].add_patch(Rectangle((a, rs.min_price), 1,
rs.max_price - rs.min_price,
fill=False, edgecolor='green',
lw=1))
else:
# decreases are red
fig.axes[0].add_patch(Rectangle((a, rs.min_price), 1,
rs.max_price - rs.min_price,
fill=False, edgecolor='red',
lw=1))
a += 1
# saving chart as PNG image file
fig.savefig('TLB_Result.png', dpi=400, bbox_inches='tight')
print("Created chart PNG file.")
def rsi_analysis_sample():
"""
rsi technical analysis sample
"""
# RSI Parameters
# Currency code to analyse
currency = 'USD'
# RSI number of days for calculation
number_of_days = 14
# start and end dates of analyse period
start_day = datetime.datetime(2015, 1, 1)
end_day = datetime.datetime(2016, 1, 23)
# overbought and oversold values
over_bought = 70
over_sold = 30
# Reading rates with given parameters to be analysed
mc = MongoConnector()
rates = mc.mongo_get_rates(currency, start_day, end_day, number_of_days)
# creating RSI instance and analysing the data
rsi = RSIAnalysis(number_of_days, rates, start_day)
result = rsi.analyse()
# creating a chart with two subplots
# one for RSI values
# other one for real end-day prices
fig, axes = seaborn.plt.subplots(2, 1)
fig.set_size_inches(14, 5)
locs, labels = seaborn.plt.xticks()
seaborn.plt.setp(labels, rotation=45)
axes[0].plot([x.day for x in result], [x.value for x in result])
axes[0].set_title('RSI(' + str(number_of_days) +
') Overbought: ' + str(over_bought) +
' Oversold: ' + str(over_sold))
axes[0].set(xticklabels=[])
axes[0].set(ylim=(0, 100))
axes[0].plot([x.day for x in result], [over_bought] * len(result))
axes[0].plot([x.day for x in result], [over_sold] * len(result))
axes[1].plot([x.day for x in result], [x.price for x in result])
axes[1].set_title('Real Rates (EUR/' + currency + ')')
# saving chart as PNG image file
fig.savefig('RSI_Result.png', dpi=400, bbox_inches='tight')
print("Created chart PNG file.")
def bollinger_bands_analysis_sample():
"""
bollinger bands technical analysis sample
"""
# Bollinger Bands Parameters
# Currency code to analyse
currency = 'USD'
# number of days for calculation
number_of_days = 20
# start and end dates of analyse period
start_day = datetime.datetime(2015, 1, 1)
end_day = datetime.datetime(2016, 1, 23)
# count of standard deviations
count_of_std = 2
# Reading rates with given parameters to be analysed
mc = MongoConnector()
rates = mc.mongo_get_rates(currency, start_day, end_day, number_of_days)
# creating Bollinger Bands instance and analysing the data
bb = BollingerBandsAnalysis(number_of_days, count_of_std, rates, start_day)
result = bb.analyse()
# creating the chart
fig = seaborn.plt.figure(figsize=(18, 5))
locs, labels = seaborn.plt.xticks()
fig.axes[0].plot([x.day for x in result], [x.real_price for x in result],
label='Real Prices', color='black', alpha=1, lw=1)
fig.axes[0].plot([x.day for x in result], [x.upper_band for x in result],
label='Upper Band', color='orange', alpha=0.5, lw=2)
fig.axes[0].plot([x.day for x in result], [x.middle_band for x in result],
label='Middle Band', color='y', alpha=0.5, lw=2)
fig.axes[0].plot([x.day for x in result], [x.lower_band for x in result],
label='Lower Band', color='red', alpha=0.5, lw=2)
fig.axes[0].set_title('Bollinger Bands(' + str(number_of_days) +
', ' + str(count_of_std) +
') EUR/' + currency + ' ' +
start_day.strftime('%d/%m/%Y') + ' - ' +
end_day.strftime('%d/%m/%Y'))
seaborn.plt.legend(loc='upper right')
seaborn.plt.setp(labels, rotation=45)
# saving chart as PNG image file
fig.savefig('BollingerBands_Result.png', dpi=400, bbox_inches='tight')
print("Created chart PNG file.")
