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.html_file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.html'
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 = self.DEFAULT_PLOT_ENGINE, 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.html_file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.html'
gp.title = strat.FINAL_STRATEGY + ' month of year seasonality'
pf.plot_line_graph(month, adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
return month
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)
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)
from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
if True:
import pandas
df = pandas.read_csv("volsurface.csv") # load a snapshot for a vol surface from disk
gp = GraphProperties()
gp.plotly_plot_mode = "offline_html" # render Plotly plot locally (rather than via website)
gp.file_output = "volsurface.png" # save as static PNG file
gp.html_file_output = "volsurface.html" # save as interactive HTML file
gp.title = "GBP/USD vol surface"
gp.color = 'Blues'
# plot surface with Plotly
pf = PlotFactory()
pf.plot_generic_graph(df, type = 'surface', adapter = 'cufflinks', gp = gp)
if True:
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 2013", # 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)
