def plot_strategy_signals(self, date = None, strip = None):
######## plot signals
strategy_signal = self._strategy_signal
strategy_signal = 100 * (strategy_signal)
if date is None:
last_day = strategy_signal.ix[-1].transpose().to_frame()
else:
last_day = strategy_signal.ix[date].transpose().to_frame()
if strip is not None:
last_day.index = [x.replace(strip, '') for x in last_day.index]
pf = PlotFactory()
gp = self.create_graph_properties("positions (% portfolio notional)", "Positions")
pf.plot_generic_graph(last_day, adapter = self.DEFAULT_PLOT_ENGINE, type = 'bar', gp = gp)
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()
end = datetime.datetime.utcnow()
start = end - timedelta(days=180)
tickers = ['S&P500', 'FTSE', 'Nikkei']
vendor_tickers = ['SPX Index', 'UKX Index', 'NKY Index']
time_series_request = TimeSeriesRequest(
start_date = start, # start date
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)
vendor_fields=['Close'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
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)
vendor_fields = ['Close'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
cache_algo='internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
# plot data
gp = GraphProperties()
pf = PlotFactory()
gp.title = 'Spot values'
gp.file_output = 'output_data/demo.png'
gp.scale_factor = 2
gp.style_sheet = 'pythalesians'
# first use PyThalesians matplotlib wrapper
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
pf.plot_generic_graph(daily_vals, gp=gp, type='line')
# use modified 538 Matplotlib stylesheet
gp.style_sheet = '538-pythalesians'
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
# use miletus matplotlib stylesheet
gp.style_sheet = 'miletus-pythalesians'
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
# use ggplot matplotlib styleheet
gp.scale_factor = 1
gp.display_brand_label = False
gp.display_source = False
gp.style_sheet = 'ggplot-pythalesians'
gp.display_mpld3 = True
