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[ ]:
short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates,
join='left',
axis=0)
slope = pandas.DataFrame(data=short_dates.values - long_dates.values,
index=short_dates.index,
columns=["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M', how='mean')
slope_monthly.index = [
str(x.year) + '/' + str(x.month) for x in slope_monthly.index
]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter='bokeh', gp=gp)
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = [str(x.year) + '/' + str(x.month) for x in daily_vals.index]
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
gp.html_file_output = "output_data/equities.htm"
gp.title = 'Recent monthly changes in equity markets'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = ['bar', 'scatter', 'line']
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Bokeh then PyThalesians
pf.plot_bar_graph(daily_vals * 100, adapter = 'bokeh', gp = gp)
pf.plot_bar_graph(daily_vals * 100, adapter = 'pythalesians', gp = gp)
# plot daily changes in FX
if True:
from datetime import timedelta
ltsf = LightTimeSeriesFactory()
end = datetime.datetime.utcnow()
start = end - timedelta(days=5)
tickers = ['EUR', 'GBP', 'AUD', 'NZD', 'CAD', 'CHF', 'NOK', 'SEK', 'JPY']
vendor_tickers = [x + 'USD BGN Curncy' for x in tickers]
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)
# resample for year end
daily_vals = daily_vals.resample('A')
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = daily_vals.index.year
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Yearly changes in spot'
gp.scale_factor = 3
gp.y_title = "Percent Change"
daily_vals = daily_vals * 100
# plot using PyThalesians (stacked & then bar graph)
pf.plot_stacked_graph(daily_vals, adapter='pythalesians', gp=gp)
pf.plot_bar_graph(daily_vals, adapter='pythalesians', gp=gp)
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)
# resample for end of month
daily_vals = daily_vals.resample("BM")
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = [str(x.year) + "/" + str(x.month) for x in daily_vals.index]
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = "Thalesians/BBG"
gp.html_file_output = "output_data/equities.htm"
gp.title = "Recent monthly changes in equity markets"
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = ["bar", "scatter", "line"]
gp.x_title = "Dates"
gp.y_title = "Pc"
# plot using Bokeh then PyThalesians
pf.plot_bar_graph(daily_vals * 100, adapter="bokeh", gp=gp)
pf.plot_bar_graph(daily_vals * 100, adapter="pythalesians", gp=gp)
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)
# resample for year end
daily_vals = daily_vals.resample("A")
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = daily_vals.index.year
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = "Thalesians/BBG"
gp.title = "Yearly changes in spot"
gp.scale_factor = 3
gp.y_title = "Percent Change"
daily_vals = daily_vals * 100
# plot using PyThalesians (stacked & then bar graph)
pf.plot_stacked_graph(daily_vals, adapter="pythalesians", gp=gp)
pf.plot_bar_graph(daily_vals, adapter="pythalesians", gp=gp)