def plot_strategy_group_benchmark_pnl(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
#gp.color = 'RdYlGn'
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - cumulative).png'
# plot cumulative line of returns
pf.plot_line_graph(self.reduce_plot(self._strategy_group_benchmark_pnl), adapter = 'pythalesians', gp = gp)
# needs write stats flag turned on
try:
keys = self._strategy_group_benchmark_tsd.keys()
ir = []
for key in keys: ir.append(self._strategy_group_benchmark_tsd[key].inforatio()[0])
ret_stats = pandas.DataFrame(index = keys, data = ir, columns = ['IR'])
ret_stats = ret_stats.sort_index()
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR).png'
gp.display_brand_label = False
# plot ret stats
pf.plot_bar_graph(ret_stats, adapter = 'pythalesians', gp = gp)
except: pass
def plot_strategy_group_benchmark_pnl(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
#gp.color = 'RdYlGn'
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - cumulative).png'
# plot cumulative line of returns
pf.plot_line_graph(self.reduce_plot(
self._strategy_group_benchmark_pnl),
adapter='pythalesians',
gp=gp)
keys = self._strategy_group_benchmark_tsd.keys()
ir = []
for key in keys:
ir.append(self._strategy_group_benchmark_tsd[key].inforatio()[0])
ret_stats = pandas.DataFrame(index=keys, data=ir, columns=['IR'])
ret_stats = ret_stats.sort()
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR).png'
gp.display_brand_label = False
# plot ret stats
pf.plot_bar_graph(ret_stats, adapter='pythalesians', gp=gp)
def plot_strategy_group_leverage(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + ' Leverage'
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Leverage).png'
pf.plot_line_graph(self.reduce_plot(self._strategy_group_leverage), adapter = 'pythalesians', gp = gp)
def plot_strategy_group_leverage(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + ' Leverage'
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Leverage).png'
pf.plot_line_graph(self.reduce_plot(self._strategy_group_leverage), adapter = 'pythalesians', gp = gp)
def plot_strategy_pnl(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy PnL).png'
try:
pf.plot_line_graph(self.reduce_plot(self._strategy_pnl), adapter = 'pythalesians', gp = gp)
except: pass
def plot_strategy_pnl(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy PnL).png'
try:
pf.plot_line_graph(self.reduce_plot(self._strategy_pnl), adapter = 'pythalesians', gp = gp)
except: pass
def plot_individual_leverage(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + ' Leverage'
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Individual Leverage).png'
try:
pf.plot_line_graph(self.reduce_plot(self._individual_leverage), adapter = 'pythalesians', gp = gp)
except: pass
def plot_individual_leverage(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + ' Leverage'
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Individual Leverage).png'
try:
pf.plot_line_graph(self.reduce_plot(self._individual_leverage), adapter = 'pythalesians', gp = gp)
except: pass
def plot_strategy_group_benchmark_annualised_pnl(self, cols = None):
# TODO - unfinished, needs checking!
if cols is None: cols = self._strategy_group_benchmark_annualised_pnl.columns
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.color = ['red', 'blue', 'purple', 'gray', 'yellow', 'green', 'pink']
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark Annualised PnL).png'
pf.plot_line_graph(self.reduce_plot(self._strategy_group_benchmark_annualised_pnl[cols]), adapter = 'pythalesians', gp = gp)
def plot_strategy_group_pnl_trades(self):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + " (bp)"
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Individual Trade PnL).png'
# zero when there isn't a trade exit
strategy_pnl_trades = self._strategy_pnl_trades.fillna(0) * 100 * 100
try:
pf.plot_line_graph(self.reduce_plot(strategy_pnl_trades), adapter = 'pythalesians', gp = gp)
except: pass
def plot_strategy_group_benchmark_annualised_pnl(self, cols = None):
# TODO - unfinished, needs checking!
if cols is None: cols = self._strategy_group_benchmark_annualised_pnl.columns
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
gp.color = ['red', 'blue', 'purple', 'gray', 'yellow', 'green', 'pink']
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark Annualised PnL).png'
pf.plot_line_graph(self.reduce_plot(self._strategy_group_benchmark_annualised_pnl[cols]), adapter = 'pythalesians', gp = gp)
def plot_strategy_group_benchmark_pnl(self, strip=None):
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
#gp.color = 'RdYlGn'
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - cumulative).png'
strat_list = self._strategy_group_benchmark_pnl.columns.sort_values()
for line in strat_list:
self.logger.info(line)
# plot cumulative line of returns
pf.plot_line_graph(self.reduce_plot(
self._strategy_group_benchmark_pnl),
adapter='pythalesians',
gp=gp)
# needs write stats flag turned on
try:
keys = self._strategy_group_benchmark_tsd.keys()
ir = []
for key in keys:
ir.append(
self._strategy_group_benchmark_tsd[key].inforatio()[0])
if strip is not None: keys = [k.replace(strip, '') for k in keys]
ret_stats = pandas.DataFrame(index=keys, data=ir, columns=['IR'])
ret_stats = ret_stats.sort_index()
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR).png'
gp.display_brand_label = False
# plot ret stats
pf.plot_bar_graph(ret_stats, adapter='pythalesians', gp=gp)
except:
pass
time_series_request = TimeSeriesRequest(
start_date="01 Jan 1970", # start date
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
data_source='quandl', # use Quandl as data source
tickers=['EURUSD', # ticker (Thalesians)
'GBPUSD'],
fields=['close'], # which fields to download
vendor_tickers=['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl)
vendor_fields=['close'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
daily_vals = ltsf.harvest_time_series(time_series_request)
techind = TechIndicator()
tech_params = TechParams()
tech_params.sma_period = 20
techind.create_tech_ind(daily_vals, 'SMA', tech_params=tech_params)
sma = techind.get_techind()
signal = techind.get_signal()
combine = daily_vals.join(sma, how='outer')
pf = PlotFactory()
pf.plot_line_graph(combine, adapter='pythalesians')
cache_algo="internet_load_return",
) # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Spot values"
gp.file_output = "output_data/demo.png"
gp.html_file_output = "output_data/demo.htm"
gp.source = "Thalesians/BBG"
# plot using PyThalesians
pf.plot_line_graph(daily_vals, adapter="pythalesians", gp=gp)
# plot using Bokeh (still needs a lot of work!)
pf.plot_line_graph(daily_vals, adapter="bokeh", gp=gp)
# do more complicated charts using several different Matplotib stylesheets (which have been customised)
if False:
ltsf = LightTimeSeriesFactory()
# load market data
start = "01 Jan 1970"
end = datetime.datetime.utcnow()
tickers = ["AUDJPY", "USDJPY"]
vendor_tickers = ["AUDJPY BGN Curncy", "USDJPY BGN Curncy"]
time_series_request = TimeSeriesRequest(
vendor_fields=['PX_LAST'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
pf = PlotFactory()
gp = GraphProperties()
gp.title = 'Spot values'
gp.file_output = 'output_data/demo.png'
gp.html_file_output = 'output_data/demo.htm'
gp.source = 'Thalesians/BBG'
# plot using PyThalesians
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
# plot using Bokeh (still needs a lot of work!)
pf.plot_line_graph(daily_vals, adapter='bokeh', gp=gp)
# do more complicated charts using several different Matplotib stylesheets (which have been customised)
if False:
ltsf = LightTimeSeriesFactory()
# load market data
start = '01 Jan 1970'
end = datetime.datetime.utcnow()
tickers = ['AUDJPY', 'USDJPY']
vendor_tickers = ['AUDJPY BGN Curncy', 'USDJPY BGN Curncy']
time_series_request = TimeSeriesRequest(
start_date="01 Jan 1970", # start date
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
data_source='quandl', # use Quandl as data source
tickers=[
'EURUSD', # ticker (Thalesians)
'GBPUSD'
],
fields=['close'], # which fields to download
vendor_tickers=['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl)
vendor_fields=['close'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
daily_vals = ltsf.harvest_time_series(time_series_request)
techind = TechIndicator()
tech_params = TechParams()
tech_params.sma_period = 20
techind.create_tech_ind(daily_vals, 'SMA', tech_params=tech_params)
sma = techind.get_techind()
signal = techind.get_signal()
combine = daily_vals.join(sma, how='outer')
pf = PlotFactory()
pf.plot_line_graph(combine, adapter='pythalesians')
df = tsc.create_mult_index_from_prices(daily_vals) df = df.fillna(method='ffill') # Lastly we can plot the results! We shall plot it with the PlotFactory class using PyThalesians engine (a wrapper on top of matplotlib). We can specify the properties of the plot using a GraphProperties class. The first line, forces matplotlib to give us the plot in the notebook, as opposed to a separate window. # In[ ]: get_ipython().magic(u'matplotlib inline') from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() gp.source = 'Thalesians/BBG (created with PyThalesians Python library)' gp.title = 'Equity Markets' gp.scale_factor = 1 gp.display_legend = True gp.x_title = 'Dates' gp.y_title = 'Index' pf = PlotFactory() pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp) # In[ ]:
vendor_fields = ['PX_LAST'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
pf = PlotFactory()
gp = GraphProperties()
gp.title = 'Spot values'
gp.file_output = 'demo.png'
gp.html_file_output = 'demo.htm'
gp.source = 'Thalesians/BBG'
# plot using PyThalesians
pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp)
# plot using Bokeh (still needs a lot of work!)
pf.plot_line_graph(daily_vals, adapter = 'bokeh', gp = gp)
# do more complicated charts using several different Matplotib stylesheets (which have been customised)
if True:
ltsf = LightTimeSeriesFactory()
# load market data
start = '01 Jan 1970'
end = datetime.datetime.utcnow()
tickers = ['AUDJPY', 'USDJPY']
vendor_tickers = ['AUDJPY BGN Curncy', 'USDJPY BGN Curncy']
time_series_request = TimeSeriesRequest(
freq="daily", # daily data
data_source="bloomberg", # use Bloomberg as data source
tickers=["EURUSD", "GBPUSD"], # ticker (Thalesians)
fields=["close", "high", "low"], # which fields to download
vendor_tickers=["EURUSD BGN Curncy", "GBPUSD BGN Curncy"], # ticker (Bloomberg)
vendor_fields=["PX_LAST", "PX_HIGH", "PX_LOW"], # which Bloomberg fields to download
cache_algo="internet_load_return",
) # how to return data
ltsf = LightTimeSeriesFactory()
df = None
df = ltsf.harvest_time_series(time_series_request)
pf = PlotFactory()
pf.plot_line_graph(df, adapter="pythalesians")
###### download event dates for non farm payrolls and then print
if False:
time_series_request = TimeSeriesRequest(
start_date="01 Jan 2014", # start date
finish_date=datetime.date.today(), # finish date
category="events",
freq="daily", # daily data
data_source="bloomberg", # use Bloomberg as data source
tickers=["FOMC", "NFP"],
fields=["release-date-time-full", "release-dt", "actual-release"], # which fields to download
vendor_tickers=["FDTR Index", "NFP TCH Index"], # ticker (Bloomberg)
vendor_fields=[
"ECO_FUTURE_RELEASE_DATE_LIST",
end = datetime.datetime.utcnow()
start_date = end.replace(hour=0, minute=0, second=0, microsecond=0) # Returns a copy
time_series_request = TimeSeriesRequest(
start_date = start_date, # start date
finish_date = datetime.datetime.utcnow(), # finish date
freq = 'intraday', # intraday data
data_source = 'bloomberg', # use Bloomberg as data source
tickers = ['EURUSD'] , # ticker (Thalesians)
fields = ['close'], # which fields to download
vendor_tickers = ['EURUSD BGN Curncy'], # ticker (Bloomberg)
vendor_fields = ['close'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
df.columns = [x.replace('.close', '') for x in df.columns.values]
gp = GraphProperties()
gp.title = 'EURUSD stuff!'
gp.file_output = 'EURUSD.png'
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
pf = PlotFactory()
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
pytwitter.update_status("check out my plot of EUR/USD!", picture = gp.file_output)
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
data_source='quandl', # use Quandl as data source
tickers=[
'EURUSD', # ticker (Thalesians)
'GBPUSD'
],
fields=['close'], # which fields to download
vendor_tickers=['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl)
vendor_fields=['close'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
df_ret = tsc.calculate_returns(df)
day_of_month_seasonality = seasonality.bus_day_of_month_seasonality(
df_ret, partition_by_month=False)
day_of_month_seasonality = tsc.convert_month_day_to_date_time(
day_of_month_seasonality)
gp = GraphProperties()
gp.date_formatter = '%b'
gp.title = 'FX spot moves by day of month'
gp.scale_factor = 3
gp.file_output = "output_data/FX spot DOM seasonality.png"
pf.plot_line_graph(day_of_month_seasonality, adapter='pythalesians', gp=gp)
###### calculate seasonal moves in EUR/USD and GBP/USD (using Quandl data)
if True:
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 1970", # start date
finish_date = datetime.date.today(), # finish date
freq = 'daily', # daily data
data_source = 'quandl', # use Quandl as data source
tickers = ['EURUSD', # ticker (Thalesians)
'GBPUSD'],
fields = ['close'], # which fields to download
vendor_tickers = ['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl)
vendor_fields = ['close'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
df_ret = tsc.calculate_returns(df)
day_of_month_seasonality = seasonality.bus_day_of_month_seasonality(df_ret, partition_by_month = False)
day_of_month_seasonality = tsc.convert_month_day_to_date_time(day_of_month_seasonality)
gp = GraphProperties()
gp.date_formatter = '%b'
gp.title = 'FX spot moves by time of year'
gp.scale_factor = 3
gp.file_output = "output_data/20150724 FX spot seas.png"
pf.plot_line_graph(day_of_month_seasonality, adapter='pythalesians', gp = gp)
from pythalesians.economics.events.eventstudy import EventStudy
es = EventStudy()
# work out cumulative asset price moves moves over the event
df_event = es.get_intraday_moves_over_custom_event(df, df_event_times)
# create an average move
df_event['Avg'] = df_event.mean(axis = 1)
# plotting spot over economic data event
gp = GraphProperties()
gp.scale_factor = 3
gp.title = 'USDJPY spot moves over recent NFP'
# plot in shades of blue (so earlier releases are lighter, later releases are darker)
gp.color = 'Blues'; gp.color_2 = []
gp.y_axis_2_series = []
gp.display_legend = False
# last release will be in red, average move in orange
gp.color_2_series = [df_event.columns[-2], df_event.columns[-1]]
gp.color_2 = ['red', 'orange'] # red, pink
gp.linewidth_2 = 2
gp.linewidth_2_series = gp.color_2_series
pf = PlotFactory()
pf.plot_line_graph(df_event * 100, adapter = 'pythalesians', gp = gp)
