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
def create_graph_properties(self, title, file_add):
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + " " + title
gp.display_legend = True
gp.scale_factor = self.SCALE_FACTOR
if self.DEFAULT_PLOT_ENGINE != 'cufflinks':
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (' + file_add + ') ' + str(gp.scale_factor) + '.png'
gp.html_file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (' + file_add + ') ' + str(gp.scale_factor) + '.html'
try:
gp.silent_display = self.SILENT_DISPLAY
except: pass
return gp
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)
vendor_fields = ['Close'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
tsc = TimeSeriesCalcs()
df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request))
gp = GraphProperties()
gp.html_file_output = "output_data/apple.htm"
gp.title = "S&P500 vs Apple"
# plot first with PyThalesians and then Bokeh
# just needs 1 word to change
gp.display_legend = False
pf = PlotFactory()
pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp)
pf.plot_generic_graph(df, type = 'line', adapter = 'bokeh', gp = gp)
# test simple Bokeh bar charts - monthly returns over past 6 months
if True:
from datetime import timedelta
ltsf = LightTimeSeriesFactory()
def run_arbitrary_sensitivity(self, strat, parameter_list = None, parameter_names = None,
pretty_portfolio_names = None, parameter_type = None):
asset_df, spot_df, spot_df2, basket_dict = strat.fill_assets()
port_list = None
tsd_list = []
for i in range(0, len(parameter_list)):
br = strat.fill_backtest_request()
current_parameter = parameter_list[i]
# for calculating P&L
for k in current_parameter.keys():
setattr(br, k, current_parameter[k])
strat.br = br # for calculating signals
signal_df = strat.construct_signal(spot_df, spot_df2, br.tech_params, br)
cash_backtest = CashBacktest()
self.logger.info("Calculating... " + str(pretty_portfolio_names[i]))
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
tsd_list.append(cash_backtest.get_portfolio_pnl_tsd())
stats = str(cash_backtest.get_portfolio_pnl_desc()[0])
port = cash_backtest.get_cumportfolio().resample('B').mean()
port.columns = [str(pretty_portfolio_names[i]) + ' ' + stats]
if port_list is None:
port_list = port
else:
port_list = port_list.join(port)
# reset the parameters of the strategy
strat.br = strat.fill_backtest_request()
pf = PlotFactory()
gp = GraphProperties()
ir = [t.inforatio()[0] for t in tsd_list]
# if we have too many combinations remove legend and use scaled shaded colour
# if len(port_list) > 10:
# gp.color = 'Blues'
# gp.display_legend = False
# plot all the variations
gp.resample = 'B'
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + '.png'
gp.html_file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + '.html'
gp.scale_factor = self.SCALE_FACTOR
gp.title = strat.FINAL_STRATEGY + ' ' + parameter_type
pf.plot_line_graph(port_list, adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
# plot all the IR in a bar chart form (can be easier to read!)
gp = GraphProperties()
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + ' IR.png'
gp.html_file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + ' IR.html'
gp.scale_factor = self.SCALE_FACTOR
gp.title = strat.FINAL_STRATEGY + ' ' + parameter_type
summary = pandas.DataFrame(index = pretty_portfolio_names, data = ir, columns = ['IR'])
pf.plot_bar_graph(summary, adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
return port_list
from chartesians.graphs.graphproperties import GraphProperties
from chartesians.graphs.plotfactory import PlotFactory
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
from chartesians.graphs.graphproperties import GraphProperties
from chartesians.graphs.plotfactory import PlotFactory
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
data_source='bloomberg', # use Bloomberg as data source
tickers=tickers, # ticker (Thalesians)
fields=['close'], # which fields to download
vendor_tickers=vendor_tickers, # ticker (Bloomberg)
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()