def run_day_of_month_analysis(self, strat):
from pythalesians.economics.seasonality.seasonality import Seasonality
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
tsc = TimeSeriesCalcs()
seas = Seasonality()
strat.construct_strategy()
pnl = strat.get_strategy_pnl()
# get seasonality by day of the month
pnl = pnl.resample('B').mean()
rets = tsc.calculate_returns(pnl)
bus_day = seas.bus_day_of_month_seasonality(rets, add_average = True)
# get seasonality by month
pnl = pnl.resample('BM').mean()
rets = tsc.calculate_returns(pnl)
month = seas.monthly_seasonality(rets)
self.logger.info("About to plot seasonality...")
gp = GraphProperties()
pf = PlotFactory()
# Plotting spot over day of month/month of year
gp.color = 'Blues'
gp.scale_factor = self.scale_factor
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.png'
gp.title = strat.FINAL_STRATEGY + ' day of month seasonality'
gp.display_legend = False
gp.color_2_series = [bus_day.columns[-1]]
gp.color_2 = ['red'] # red, pink
gp.linewidth_2 = 4
gp.linewidth_2_series = [bus_day.columns[-1]]
gp.y_axis_2_series = [bus_day.columns[-1]]
pf.plot_line_graph(bus_day, adapter = 'pythalesians', gp = gp)
gp = GraphProperties()
gp.scale_factor = self.scale_factor
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.png'
gp.title = strat.FINAL_STRATEGY + ' month of year seasonality'
pf.plot_line_graph(month, adapter = 'pythalesians', gp = gp)
return month
def run_day_of_month_analysis(self, strat):
from pythalesians.economics.seasonality.seasonality import Seasonality
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
tsc = TimeSeriesCalcs()
seas = Seasonality()
strat.construct_strategy()
pnl = strat.get_strategy_pnl()
# get seasonality by day of the month
pnl = pnl.resample('B')
rets = tsc.calculate_returns(pnl)
bus_day = seas.bus_day_of_month_seasonality(rets, add_average=True)
# get seasonality by month
pnl = pnl.resample('BM')
rets = tsc.calculate_returns(pnl)
month = seas.monthly_seasonality(rets)
self.logger.info("About to plot seasonality...")
gp = GraphProperties()
pf = PlotFactory()
# Plotting spot over day of month/month of year
gp.color = 'Blues'
gp.scale_factor = self.scale_factor
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.png'
gp.title = strat.FINAL_STRATEGY + ' day of month seasonality'
gp.display_legend = False
gp.color_2_series = [bus_day.columns[-1]]
gp.color_2 = ['red'] # red, pink
gp.linewidth_2 = 4
gp.linewidth_2_series = [bus_day.columns[-1]]
gp.y_axis_2_series = [bus_day.columns[-1]]
pf.plot_line_graph(bus_day, adapter='pythalesians', gp=gp)
gp = GraphProperties()
gp.scale_factor = self.scale_factor
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.png'
gp.title = strat.FINAL_STRATEGY + ' month of year seasonality'
pf.plot_line_graph(month, adapter='pythalesians', gp=gp)
return month
df_fred = ltsf.harvest_time_series(time_series_request)
df_fred.columns = [
x.replace('.close', '') for x in df_fred.columns.values
]
# convert to USD bn
# df_fred = (df_fred * 10000000)
df = df.join(df_fred, how="outer")
df['USDJPY'] = df['USDJPY'].ffill()
# data is in 100 million JPY, divide by 10 to get into 1000 million (ie. 1 billion)
# divide by USD/JPY spot to get into USD
df['USDJPY purchases (bn USD)'] = (df['USDJPY purchases (bn USD)'] /
df['USDJPY']) / 10
gp = GraphProperties()
gp.scale_factor = 3
gp.title = "BoJ USDJPY buying"
gp.file_output = "output_data/" + datetime.date.today().strftime("%Y%m%d") + " USDJPY BoJ intervention " \
+ str(gp.scale_factor) + ".png"
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
gp.y_axis_2_series = ['USDJPY purchases (bn USD)']
gp.color_2_series = gp.y_axis_2_series
gp.color_2 = ['blue']
pf = PlotFactory()
pf.plot_line_graph(df, adapter='pythalesians', gp=gp)
start_date = "01 Jun 2015", # start date
finish_date = "02 Jun 2015", # finish date
freq = 'tick', # tick data
data_source = 'dukascopy', # use dukascopy as data source
tickers = ['EURUSD', 'GBPUSD'], # ticker (Thalesians)
fields = ['bid', 'ask'], # which fields to download
vendor_tickers = ['EURUSD', 'GBPUSD'], # ticker (Dukascopy)
vendor_fields = ['bid', 'ask'], # which Dukascopy fields to download
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
gp = GraphProperties()
gp.y_axis_2_series = ['GBPUSD.bid', 'GBPUSD.ask']
pf = PlotFactory()
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
###### download daily data from Google for Apple and S&P500 ETF (and then rebase, before plotting)
if False:
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 1970", # start date
finish_date = datetime.date.today(), # finish date
freq = 'daily', # daily data
data_source = 'google', # use Bloomberg as data source
tickers = ['Apple', 'S&P500 ETF'], # ticker (Thalesians)
fields = ['close'], # which fields to download
vendor_tickers = ['aapl', 'spy'], # ticker (Google)
start_date="01 Jun 2015", # start date
finish_date="02 Jun 2015", # finish date
freq='tick', # tick data
data_source='dukascopy', # use dukascopy as data source
tickers=['EURUSD', 'GBPUSD'], # ticker (Thalesians)
fields=['bid', 'ask'], # which fields to download
vendor_tickers=['EURUSD', 'GBPUSD'], # ticker (Dukascopy)
vendor_fields=['bid', 'ask'], # which Dukascopy fields to download
cache_algo='internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
gp = GraphProperties()
gp.y_axis_2_series = ['GBPUSD.bid', 'GBPUSD.ask']
pf = PlotFactory()
pf.plot_line_graph(df, adapter='pythalesians', gp=gp)
###### download daily data from Google for Apple and S&P500 ETF (and then rebase, before plotting)
if False:
time_series_request = TimeSeriesRequest(
start_date="01 Jan 1970", # start date
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
data_source='google', # use Bloomberg as data source
tickers=['Apple', 'S&P500 ETF'], # ticker (Thalesians)
fields=['close'], # which fields to download
vendor_tickers=['aapl', 'spy'], # ticker (Google)
time_series_request.vendor_tickers = ['JPINTDUSDJPY']
time_series_request.data_source = 'fred'
df_fred = ltsf.harvest_time_series(time_series_request)
df_fred.columns = [x.replace('.close', '') for x in df_fred.columns.values]
# convert to USD bn
# df_fred = (df_fred * 10000000)
df = df.join(df_fred, how="outer")
df['USDJPY'] = df['USDJPY'].ffill()
# data is in 100 million JPY, divide by 10 to get into 1000 million (ie. 1 billion)
# divide by USD/JPY spot to get into USD
df['USDJPY purchases (bn USD)'] = (df['USDJPY purchases (bn USD)'] / df['USDJPY']) / 10
gp = GraphProperties()
gp.scale_factor = 3
gp.title = "BoJ USDJPY buying"
gp.file_output = "output_data/" + datetime.date.today().strftime("%Y%m%d") + " USDJPY BoJ intervention " \
+ str(gp.scale_factor) + ".png"
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
gp.y_axis_2_series = ['USDJPY purchases (bn USD)']
gp.color_2_series = gp.y_axis_2_series
gp.color_2 = ['blue']
pf = PlotFactory()
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
df_event_times.index = df_event_times.index.tz_localize(utc_time) # work in UTC time
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)
